interview in quantitative research

9.3 Quantitative Interview Techniques and Considerations

Learning objectives.

• Define and describe standardized interviews.
• Describe how quantitative interviews differ from qualitative interviews.
• Describe the process and some of the drawbacks of telephone interviewing techniques.
• Describe how the analysis of quantitative interview works.
• Identify the strengths and weaknesses of quantitative interviews.

Quantitative interviews are similar to qualitative interviews in that they involve some researcher/respondent interaction. But the process of conducting and analyzing findings from quantitative interviews also differs in several ways from that of qualitative interviews. Each approach also comes with its own unique set of strengths and weaknesses. We’ll explore those differences here.

Conducting Quantitative Interviews

Much of what we learned in the previous chapter on survey research applies to quantitative interviews as well. In fact, quantitative interviews are sometimes referred to as survey interviews because they resemble survey-style question-and-answer formats. They might also be called standardized interviews Interviews during which the same questions are asked of every participant in the same way, and survey-style question-and-answer formats are utilized. . The difference between surveys and standardized interviews is that questions and answer options are read to respondents rather than having respondents complete a questionnaire on their own. As with questionnaires, the questions posed in a standardized interview tend to be closed ended. See Chapter 8 "Survey Research: A Quantitative Technique" for the definition of closed ended. There are instances in which a quantitative interviewer might pose a few open-ended questions as well. In these cases, the coding process works somewhat differently than coding in-depth interview data. We’ll describe this process in the following subsection.

In quantitative interviews, an interview schedule A document containing the list of questions and answer options that quantitative interviewers read to respondents. is used to guide the researcher as he or she poses questions and answer options to respondents. An interview schedule is usually more rigid than an interview guide. It contains the list of questions and answer options that the researcher will read to respondents. Whereas qualitative researchers emphasize respondents’ roles in helping to determine how an interview progresses, in a quantitative interview, consistency in the way that questions and answer options are presented is very important. The aim is to pose every question-and-answer option in the very same way to every respondent. This is done to minimize interviewer effect Occurs when an interviewee is influenced by how or when questions and answer options are presented by an interviewer. , or possible changes in the way an interviewee responds based on how or when questions and answer options are presented by the interviewer.

Quantitative interviews may be recorded, but because questions tend to be closed ended, taking notes during the interview is less disruptive than it can be during a qualitative interview. If a quantitative interview contains open-ended questions, however, recording the interview is advised. It may also be helpful to record quantitative interviews if a researcher wishes to assess possible interview effect. Noticeable differences in responses might be more attributable to interviewer effect than to any real respondent differences. Having a recording of the interview can help a researcher make such determinations.

Quantitative interviewers are usually more concerned with gathering data from a large, representative sample. As you might imagine, collecting data from many people via interviews can be quite laborious. Technological advances in telephone interviewing procedures can assist quantitative interviewers in this process. One concern about telephone interviewing is that fewer and fewer people list their telephone numbers these days, but random digit dialing (RDD) takes care of this problem. RDD programs dial randomly generated phone numbers for researchers conducting phone interviews. This means that unlisted numbers are as likely to be included in a sample as listed numbers (though, having used this software for quantitative interviewing myself, I will add that folks with unlisted numbers are not always very pleased to receive calls from unknown researchers). Computer-assisted telephone interviewing (CATI) programs have also been developed to assist quantitative survey researchers. These programs allow an interviewer to enter responses directly into a computer as they are provided, thus saving hours of time that would otherwise have to be spent entering data into an analysis program by hand.

Conducting quantitative interviews over the phone does not come without some drawbacks. Aside from the obvious problem that not everyone has a phone, research shows that phone interviews generate more fence-sitters than in-person interviews (Holbrook, Green, & Krosnick, 2003). Holbrook, A. L., Green, M. C., & Krosnick, J. A. (2003). Telephone versus face-to-face interviewing of national probability samples with long questionnaires: Comparisons of respondent satisficing and social desirability response bias. Public Opinion Quarterly, 67 , 79–125. Responses to sensitive questions or those that respondents view as invasive are also generally less accurate when data are collected over the phone as compared to when they are collected in person. I can vouch for this latter point from personal experience. While conducting quantitative telephone interviews when I worked at a research firm, it was not terribly uncommon for respondents to tell me that they were green or purple when I asked them to report their racial identity.

Analysis of Quantitative Interview Data

As with the analysis of survey data, analysis of quantitative interview data usually involves coding response options numerically, entering numeric responses into a data analysis computer program, and then running various statistical commands to identify patterns across responses. Section 8.5 "Analysis of Survey Data" of Chapter 8 "Survey Research: A Quantitative Technique" describes the coding process for quantitative data. But what happens when quantitative interviews ask open-ended questions? In this case, responses are typically numerically coded, just as closed-ended questions are, but the process is a little more complex than simply giving a “no” a label of 0 and a “yes” a label of 1.

In some cases, quantitatively coding open-ended interview questions may work inductively, as described in Section 9.2.2 "Analysis of Qualitative Interview Data" . If this is the case, rather than ending with codes, descriptions of codes, and interview excerpts, the researcher will assign a numerical value to codes and may not utilize verbatim excerpts from interviews in later reports of results. Keep in mind, as described in Chapter 1 "Introduction" , that with quantitative methods the aim is to be able to represent and condense data into numbers. The quantitative coding of open-ended interview questions is often a deductive process. The researcher may begin with an idea about likely responses to his or her open-ended questions and assign a numerical value to each likely response. Then the researcher will review participants’ open-ended responses and assign the numerical value that most closely matches the value of his or her expected response.

Strengths and Weaknesses of Quantitative Interviews

Quantitative interviews offer several benefits. The strengths and weakness of quantitative interviews tend to be couched in comparison to those of administering hard copy questionnaires. For example, response rates tend to be higher with interviews than with mailed questionnaires (Babbie, 2010). Babbie, E. (2010). The practice of social research (12th ed.). Belmont, CA: Wadsworth. That makes sense—don’t you find it easier to say no to a piece of paper than to a person? Quantitative interviews can also help reduce respondent confusion. If a respondent is unsure about the meaning of a question or answer option on a questionnaire, he or she probably won’t have the opportunity to get clarification from the researcher. An interview, on the other hand, gives the researcher an opportunity to clarify or explain any items that may be confusing.

As with every method of data collection we’ve discussed, there are also drawbacks to conducting quantitative interviews. Perhaps the largest, and of most concern to quantitative researchers, is interviewer effect. While questions on hard copy questionnaires may create an impression based on the way they are presented, having a person administer questions introduces a slew of additional variables that might influence a respondent. As I’ve said, consistency is key with quantitative data collection—and human beings are not necessarily known for their consistency. Interviewing respondents is also much more time consuming and expensive than mailing questionnaires. Thus quantitative researchers may opt for written questionnaires over interviews on the grounds that they will be able to reach a large sample at a much lower cost than were they to interact personally with each and every respondent.

Key Takeaways

• Unlike qualitative interviews, quantitative interviews usually contain closed-ended questions that are delivered in the same format and same order to every respondent.
• Quantitative interview data are analyzed by assigning a numerical value to participants’ responses.
• While quantitative interviews offer several advantages over self-administered questionnaires such as higher response rates and lower respondent confusion, they have the drawbacks of possible interviewer effect and greater time and expense.
• The General Social Survey (GSS), which we’ve mentioned in previous chapters, is administered via in-person interview, just like quantitative interviewing procedures described here. Read more about the GSS at http://www.norc.uchicago.edu/GSS+Website .
• Take a few of the open-ended questions you created after reading Section 9.2 "Qualitative Interview Techniques and Considerations" on qualitative interviewing techniques. See if you can turn them into closed-ended questions.

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• Types of Interviews in Research | Guide & Examples

Types of Interviews in Research | Guide & Examples

Published on 4 May 2022 by Tegan George . Revised on 10 October 2022.

An interview is a qualitative research method that relies on asking questions in order to collect data . Interviews involve two or more people, one of whom is the interviewer asking the questions.

There are several types of interviews, often differentiated by their level of structure. Structured interviews have predetermined questions asked in a predetermined order. Unstructured interviews are more free-flowing, and semi-structured interviews fall in between.

Interviews are commonly used in market research, social science, and ethnographic research.

Table of contents

What is a structured interview, what is a semi-structured interview, what is an unstructured interview, what is a focus group, examples of interview questions, advantages and disadvantages of interviews, frequently asked questions about types of interviews.

Structured interviews have predetermined questions in a set order. They are often closed-ended, featuring dichotomous (yes/no) or multiple-choice questions. While open-ended structured interviews exist, they are much less common. The types of questions asked make structured interviews a predominantly quantitative tool.

Asking set questions in a set order can help you see patterns among responses, and it allows you to easily compare responses between participants while keeping other factors constant. This can mitigate biases and lead to higher reliability and validity. However, structured interviews can be overly formal, as well as limited in scope and flexibility.

• You feel very comfortable with your topic. This will help you formulate your questions most effectively.
• You have limited time or resources. Structured interviews are a bit more straightforward to analyse because of their closed-ended nature, and can be a doable undertaking for an individual.
• Your research question depends on holding environmental conditions between participants constant

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Semi-structured interviews are a blend of structured and unstructured interviews. While the interviewer has a general plan for what they want to ask, the questions do not have to follow a particular phrasing or order.

Semi-structured interviews are often open-ended, allowing for flexibility, but follow a predetermined thematic framework, giving a sense of order. For this reason, they are often considered ‘the best of both worlds’.

However, if the questions differ substantially between participants, it can be challenging to look for patterns, lessening the generalisability and validity of your results.

• You have prior interview experience. It’s easier than you think to accidentally ask a leading question when coming up with questions on the fly. Overall, spontaneous questions are much more difficult than they may seem.
• Your research question is exploratory in nature. The answers you receive can help guide your future research.

An unstructured interview is the most flexible type of interview. The questions and the order in which they are asked are not set. Instead, the interview can proceed more spontaneously, based on the participant’s previous answers.

Unstructured interviews are by definition open-ended. This flexibility can help you gather detailed information on your topic, while still allowing you to observe patterns between participants.

However, so much flexibility means that they can be very challenging to conduct properly. You must be very careful not to ask leading questions, as biased responses can lead to lower reliability or even invalidate your research.

• You have a solid background in your research topic and have conducted interviews before
• Your research question is exploratory in nature, and you are seeking descriptive data that will deepen and contextualise your initial hypotheses
• Your research necessitates forming a deeper connection with your participants, encouraging them to feel comfortable revealing their true opinions and emotions

A focus group brings together a group of participants to answer questions on a topic of interest in a moderated setting. Focus groups are qualitative in nature and often study the group’s dynamic and body language in addition to their answers. Responses can guide future research on consumer products and services, human behaviour, or controversial topics.

Focus groups can provide more nuanced and unfiltered feedback than individual interviews and are easier to organise than experiments or large surveys. However, their small size leads to low external validity and the temptation as a researcher to ‘cherry-pick’ responses that fit your hypotheses.

• Your research focuses on the dynamics of group discussion or real-time responses to your topic
• Your questions are complex and rooted in feelings, opinions, and perceptions that cannot be answered with a ‘yes’ or ‘no’
• Your topic is exploratory in nature, and you are seeking information that will help you uncover new questions or future research ideas

Depending on the type of interview you are conducting, your questions will differ in style, phrasing, and intention. Structured interview questions are set and precise, while the other types of interviews allow for more open-endedness and flexibility.

Here are some examples.

• Semi-structured
• Unstructured
• Focus group
• Do you like dogs? Yes/No
• Do you associate dogs with feeling: happy; somewhat happy; neutral; somewhat unhappy; unhappy
• If yes, name one attribute of dogs that you like.
• If no, name one attribute of dogs that you don’t like.
• What feelings do dogs bring out in you?
• When you think more deeply about this, what experiences would you say your feelings are rooted in?

Interviews are a great research tool. They allow you to gather rich information and draw more detailed conclusions than other research methods, taking into consideration nonverbal cues, off-the-cuff reactions, and emotional responses.

However, they can also be time-consuming and deceptively challenging to conduct properly. Smaller sample sizes can cause their validity and reliability to suffer, and there is an inherent risk of interviewer effect arising from accidentally leading questions.

Here are some advantages and disadvantages of each type of interview that can help you decide if you’d like to utilise this research method.

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order.
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when:

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyse your data quickly and efficiently
• Your research question depends on strong parity between participants, with environmental conditions held constant

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualise your initial thoughts and hypotheses
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

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Quantitative Interview Techniques & Considerations

61 Conducting Quantitative Interviews

Much of what we learned in the previous chapter on survey research applies to quantitative interviews as well. In fact, quantitative interviews are sometimes referred to as survey interviews because they resemble survey-style question-and-answer formats. They might also be called standardized interviews . The difference between surveys and standardized interviews is that questions and answer options are read to respondents in a standardized interview, rather than having respondents complete a survey on their own. As with surveys, the questions posed in a standardized interview tend to be closed ended. There are instances in which a quantitative interviewer might pose a few open-ended questions as well. In these cases, the coding process works somewhat differently than coding in-depth interview data. We will describe this process in the following section.

In quantitative interviews, an interview schedule is used to guide the researcher as he or she poses questions and answer options to respondents. An interview schedule is usually more rigid than an interview guide. It contains the list of questions and answer options that the researcher will read to respondents. Whereas qualitative researchers emphasize respondents’ roles in helping to determine how an interview progresses, in a quantitative interview, consistency in the way that questions and answer options are presented is very important. The aim is to pose every question-and-answer option in the very same way to every respondent. This is done to minimize interviewer effect, or possible changes in the way an interviewee responds based on how or when questions and answer options are presented by the interviewer.

Quantitative interviews may be recorded, but because questions tend to be closed ended, taking notes during the interview is less disruptive than it can be during a qualitative interview. If a quantitative interview contains open-ended questions, recording the interview is advised. It may also be helpful to record quantitative interviews if a researcher wishes to assess possible interview effect. Noticeable differences in responses might be more attributable to interviewer effect than to any real respondent differences. Having a recording of the interview can help a researcher make such determinations.

Quantitative interviewers are usually more concerned with gathering data from a large, representative sample. As you might imagine, collecting data from many people via interviews can be quite laborious. In the past, telephone interviewing was quite common; however, the growth in use mobile phones has raised concern regarding whether or not traditional landline telephone interviews and surveys are now representative of the general population (Busse & Fuchs, 2012). Indeed, there are other drawbacks to telephone interviews. Aside from the obvious problem that not everyone has a phone (mobile or landline), research shows that phone interview respondents were less cooperative, less engaged in the interview, and more likely to express dissatisfaction with the length of the interview than were face-to-face respondents (Holbrook, Green, & Krosnick, 2003, p. 79). Holbrook et al.’s research also demonstrated that telephone respondents were more suspicious of the interview process and more likely than face-to-face respondents to present themselves in a socially desirable manner.

Text Attributions

• This chapter is an adaptation of Chapter 9.3 in Principles of Sociological Inquiry , which was adapted by the Saylor Academy without attribution to the original authors or publisher, as requested by the licensor. © Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License .

An Introduction to Research Methods in Sociology Copyright © 2019 by Valerie A. Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Chapter 9 Interviews: Qualitative and Quantitative Approaches

Why interview research.

Today’s young men are delaying their entry into adulthood. That’s a nice way of saying they are “totally confused”; “cannot commit to their relationships, work, or lives”; and are “obsessed with never wanting to grow up.” These quotes come from a summary of reviews on the website dedicated to Kimmel’s book, Guyland : http://www.guyland.net . But don’t take my word for it. Take sociologist Michael Kimmel’s word. He interviewed 400 young men, ages 16 to 26, over the course of 4 years across the United States to learn how they made the transition from adolescence into adulthood. Since the results of Kimmel’s research were published in 2008, Kimmel, M. (2008). Guyland: The perilous world where boys become men . New York, NY: Harper Collins. his book has made quite a splash. Featured in news reports, on blogs, and in many book reviews, some claim Kimmel’s research “could save the humanity of many young men,” This quote from Gloria Steinem is provided on the website dedicated to Kimmel’s book, Guyland : http://www.guyland.net . while others suggest that its conclusions can only be applied to “fraternity guys and jocks.” This quote comes from “Thomas,” who wrote a review of Kimmel’s book on the following site: http://yesmeansyesblog.wordpress.com/2010/03/12/review-guyland . Whatever your take on Kimmel’s research, one thing remains true: We surely would not know nearly as much as we now do about the lives of many young American men were it not for interview research.

Thanks to interview research, we know something about how young men today do (or do not) make the transition into adulthood.

© Thinkstock

9.1 Interview Research: What Is It and When Should It Be Used?

Learning objectives.

• Define interviews from the social scientific perspective.
• Identify when it is appropriate to employ interviews as a data-collection strategy.

Knowing how to create and conduct a good interview is one of those skills you just can’t go wrong having. Interviews are used by market researchers to learn how to sell their products, journalists use interviews to get information from a whole host of people from VIPs to random people on the street. Regis Philbin (a sociology major in college This information comes from the following list of famous sociology majors provided by the American Sociological Association on their website: http://www.asanet.org/students/famous.cfm . ) used interviews to help television viewers get to know guests on his show, employers use them to make decisions about job offers, and even Ruth Westheimer (the famous sex doctor who has an MA in sociology Read more about Dr. Ruth, her background, and her credentials at her website: http://www.drruth.com . ) used interviews to elicit details from call-in participants on her radio show. Interested in hearing Dr. Ruth’s interview style? There are a number of audio clips from her radio show, Sexually Speaking , linked from the following site: http://www.cs.cmu.edu/~chuck/ruthpg . Warning: some of the images and audio clips on this page may be offensive to some readers. It seems everyone who’s anyone knows how to conduct an interview.

Social scientists use interviews as do many others, from journalists to talk show hosts to businesses making hiring decisions.

From the social scientific perspective, interviews A method of data collection that involves two or more people exchanging information through a series of questions and answers. are a method of data collection that involves two or more people exchanging information through a series of questions and answers. The questions are designed by a researcher to elicit information from interview participant(s) on a specific topic or set of topics. Typically interviews involve an in-person meeting between two people, an interviewer and an interviewee. But as you’ll discover in this chapter, interviews need not be limited to two people, nor must they occur in person.

In social science, interviews are a method of data collection that involves two or more people exchanging information through a series of questions and answers.

The question of when to conduct an interview might be on your mind. Interviews are an excellent way to gather detailed information. They also have an advantage over surveys; with a survey, if a participant’s response sparks some follow-up question in your mind, you generally don’t have an opportunity to ask for more information. What you get is what you get. In an interview, however, because you are actually talking with your study participants in real time, you can ask that follow-up question. Thus interviews are a useful method to use when you want to know the story behind responses you might receive in a written survey.

Interviews are also useful when the topic you are studying is rather complex, when whatever you plan to ask requires lengthy explanation, or when your topic or answers to your questions may not be immediately clear to participants who may need some time or dialogue with others in order to work through their responses to your questions. Also, if your research topic is one about which people will likely have a lot to say or will want to provide some explanation or describe some process, interviews may be the best method for you. For example, I used interviews to gather data about how people reach the decision not to have children and how others in their lives have responded to that decision. To understand these “how’s” I needed to have some back-and-forth dialogue with respondents. When they begin to tell me their story, inevitably new questions that hadn’t occurred to me from prior interviews come up because each person’s story is unique. Also, because the process of choosing not to have children is complex for many people, describing that process by responding to closed-ended questions on a survey wouldn’t work particularly well.

In sum, interview research is especially useful when the following are true:

• You wish to gather very detailed information
• You anticipate wanting to ask respondents for more information about their responses
• You plan to ask questions that require lengthy explanation
• The topic you are studying is complex or may be confusing to respondents
• Your topic involves studying processes

Key Takeaways

• Understanding how to design and conduct interview research is a useful skill to have.
• In a social scientific interview, two or more people exchange information through a series of questions and answers.
• Interview research is often used when detailed information is required and when a researcher wishes to examine processes.
• Think about a topic about which you might wish to collect data by conducting interviews. What makes this topic suitable for interview research?

9.2 Qualitative Interview Techniques and Considerations

• Identify the primary aim of in-depth interviews.
• Describe what makes qualitative interview techniques unique.
• Define the term interview guide and describe how to construct an interview guide.
• Outline the guidelines for constructing good qualitative interview questions.
• Define the term focus group and identify one benefit of focus groups.
• Identify and describe the various stages of qualitative interview data analysis.
• Identify the strengths and weaknesses of qualitative interviews.

Qualitative interviews are sometimes called intensive or in-depth interviews A semistructured meeting between a researcher and respondent in which the researcher asks a series of open-ended questions; questions may be posed to respondents in slightly different ways or orders. . These interviews are semistructured; the researcher has a particular topic about which he or she would like to hear from the respondent, but questions are open ended and may not be asked in exactly the same way or in exactly the same order to each and every respondent. In in-depth interviews, the primary aim is to hear from respondents about what they think is important about the topic at hand and to hear it in their own words. In this section, we’ll take a look at how to conduct interviews that are specifically qualitative in nature, analyze qualitative interview data, and use some of the strengths and weaknesses of this method. In Section 9.4 "Issues to Consider for All Interview Types" , we return to several considerations that are relevant to both qualitative and quantitative interviewing.

Conducting Qualitative Interviews

Qualitative interviews might feel more like a conversation than an interview to respondents, but the researcher is in fact usually guiding the conversation with the goal in mind of gathering information from a respondent. A key difference between qualitative and quantitative interviewing is that qualitative interviews contain open-ended questions Questions for which a researcher does not provide answer options; questions that require respondents to answer in their own words. . The meaning of this term is of course implied by its name, but just so that we’re sure to be on the same page, I’ll tell you that open-ended questions are questions that a researcher poses but does not provide answer options for. Open-ended questions are more demanding of participants than closed-ended questions, for they require participants to come up with their own words, phrases, or sentences to respond.

In a qualitative interview, the researcher usually develops a guide in advance that he or she then refers to during the interview (or memorizes in advance of the interview). An interview guide A list of topics or questions that an interviewer hopes to cover during the course of an interview. is a list of topics or questions that the interviewer hopes to cover during the course of an interview. It is called a guide because it is simply that—it is used to guide the interviewer, but it is not set in stone. Think of an interview guide like your agenda for the day or your to-do list—both probably contain all the items you hope to check off or accomplish, though it probably won’t be the end of the world if you don’t accomplish everything on the list or if you don’t accomplish it in the exact order that you have it written down. Perhaps new events will come up that cause you to rearrange your schedule just a bit, or perhaps you simply won’t get to everything on the list.

You might think of an interview guide as you would your to-do list. Perhaps it contains the list of things you hope to accomplish, in the order you hope to accomplish them, but there may be new events or new information that cause you to alter your list or to change its order just a bit.

Interview guides should outline issues that a researcher feels are likely to be important, but because participants are asked to provide answers in their own words, and to raise points that they believe are important, each interview is likely to flow a little differently. While the opening question in an in-depth interview may be the same across all interviews, from that point on what the participant says will shape how the interview proceeds. This, I believe, is what makes in-depth interviewing so exciting. It is also what makes in-depth interviewing rather challenging to conduct. It takes a skilled interviewer to be able to ask questions; actually listen to respondents; and pick up on cues about when to follow up, when to move on, and when to simply let the participant speak without guidance or interruption.

I’ve said that interview guides can list topics or questions. The specific format of an interview guide might depend on your style, experience, and comfort level as an interviewer or with your topic. I have conducted interviews using different kinds of guides. In my interviews of young people about their experiences with workplace sexual harassment, the guide I used was topic based. There were few specific questions contained in the guide. Instead, I had an outline of topics that I hoped to cover, listed in an order that I thought it might make sense to cover them, noted on a sheet of paper. That guide can be seen in Figure 9.4 "Interview Guide Displaying Topics Rather Than Questions" .

Figure 9.4 Interview Guide Displaying Topics Rather Than Questions

In my interviews with child-free adults, the interview guide contained questions rather than brief topics. One reason I took this approach is that this was a topic with which I had less familiarity than workplace sexual harassment. I’d been studying harassment for some time before I began those interviews, and I had already analyzed much quantitative survey data on the topic. When I began the child-free interviews, I was embarking on a research topic that was entirely new for me. I was also studying a topic about which I have strong personal feelings, and I wanted to be sure that I phrased my questions in a way that didn’t appear biased to respondents. To help ward off that possibility, I wrote down specific question wording in my interview guide. As I conducted more and more interviews, and read more and more of the literature on child-free adults, I became more confident about my ability to ask open-ended, nonbiased questions about the topic without the guide, but having some specific questions written down at the start of the data collection process certainly helped. The interview guide I used for the child-free project is displayed in Figure 9.5 "Interview Guide Displaying Questions Rather Than Topics" .

Figure 9.5 Interview Guide Displaying Questions Rather Than Topics

As you might have guessed, interview guides do not appear out of thin air. They are the result of thoughtful and careful work on the part of a researcher. As you can see in both of the preceding guides, the topics and questions have been organized thematically and in the order in which they are likely to proceed (though keep in mind that the flow of a qualitative interview is in part determined by what a respondent has to say). Sometimes qualitative interviewers may create two versions of the interview guide: one version contains a very brief outline of the interview, perhaps with just topic headings, and another version contains detailed questions underneath each topic heading. In this case, the researcher might use the very detailed guide to prepare and practice in advance of actually conducting interviews and then just bring the brief outline to the interview. Bringing an outline, as opposed to a very long list of detailed questions, to an interview encourages the researcher to actually listen to what a participant is telling her. An overly detailed interview guide will be difficult to navigate through during an interview and could give respondents the misimpression that the interviewer is more interested in her questions than in the participant’s answers.

When beginning to construct an interview guide, brainstorming is usually the first step. There are no rules at the brainstorming stage—simply list all the topics and questions that come to mind when you think about your research question. Once you’ve got a pretty good list, you can begin to pare it down by cutting questions and topics that seem redundant and group like questions and topics together. If you haven’t done so yet, you may also want to come up with question and topic headings for your grouped categories. You should also consult the scholarly literature to find out what kinds of questions other interviewers have asked in studies of similar topics. As with quantitative survey research, it is best not to place very sensitive or potentially controversial questions at the very beginning of your qualitative interview guide. You need to give participants the opportunity to warm up to the interview and to feel comfortable talking with you. Finally, get some feedback on your interview guide. Ask your friends, family members, and your professors for some guidance and suggestions once you’ve come up with what you think is a pretty strong guide. Chances are they’ll catch a few things you hadn’t noticed.

In terms of the specific questions you include on your guide, there are a few guidelines worth noting. First, try to avoid questions that can be answered with a simple yes or no, or if you do choose to include such questions, be sure to include follow-up questions. Remember, one of the benefits of qualitative interviews is that you can ask participants for more information—be sure to do so. While it is a good idea to ask follow-up questions, try to avoid asking “why” as your follow-up question, as this particular question can come off as confrontational, even if that is not how you intend it. Often people won’t know how to respond to “why,” perhaps because they don’t even know why themselves. Instead of “why,” I recommend that you say something like, “Could you tell me a little more about that?” This allows participants to explain themselves further without feeling that they’re being doubted or questioned in a hostile way.

Also, try to avoid phrasing your questions in a leading way. For example, rather than asking, “Don’t you think that most people who don’t want kids are selfish?” you could ask, “What comes to mind for you when you hear that someone doesn’t want kids?” Or rather than asking, “What do you think about juvenile delinquents who drink and drive?” you could ask, “How do you feel about underage drinking?” or “What do you think about drinking and driving?” Finally, as noted earlier in this section, remember to keep most, if not all, of your questions open ended. The key to a successful qualitative interview is giving participants the opportunity to share information in their own words and in their own way.

Even after the interview guide is constructed, the interviewer is not yet ready to begin conducting interviews. The researcher next has to decide how to collect and maintain the information that is provided by participants. It is probably most common for qualitative interviewers to take audio recordings of the interviews they conduct.

Recording interviews allows the researcher to focus on her or his interaction with the interview participant rather than being distracted by trying to take notes. Of course, not all participants will feel comfortable being recorded and sometimes even the interviewer may feel that the subject is so sensitive that recording would be inappropriate. If this is the case, it is up to the researcher to balance excellent note-taking with exceptional question asking and even better listening. I don’t think I can understate the difficulty of managing all these feats simultaneously. Whether you will be recording your interviews or not (and especially if not), practicing the interview in advance is crucial. Ideally, you’ll find a friend or two willing to participate in a couple of trial runs with you. Even better, you’ll find a friend or two who are similar in at least some ways to your sample. They can give you the best feedback on your questions and your interview demeanor.

Ideally, you will take an audio recording of your interviews so that you can pay attention to your participants during the interview and so that you have a verbatim record of the interview.

All interviewers should be aware of, give some thought to, and plan for several additional factors, such as where to conduct an interview and how to make participants as comfortable as possible during an interview. Because these factors should be considered by both qualitative and quantitative interviewers, we will return to them in Section 9.4 "Issues to Consider for All Interview Types" after we’ve had a chance to look at some of the unique features of each approach to interviewing.

Although our focus here has been on interviews for which there is one interviewer and one respondent, this is certainly not the only way to conduct a qualitative interview. Sometimes there may be multiple respondents present, and occasionally more than one interviewer may be present as well. When multiple respondents participate in an interview at the same time, this is referred to as a focus group Multiple respondents participate in an interview at the same time. . Focus groups can be an excellent way to gather information because topics or questions that hadn’t occurred to the researcher may be brought up by other participants in the group. Having respondents talk with and ask questions of one another can be an excellent way of learning about a topic; not only might respondents ask questions that hadn’t occurred to the researcher, but the researcher can also learn from respondents’ body language around and interactions with one another. Of course, there are some unique ethical concerns associated with collecting data in a group setting. We’ll take a closer look at how focus groups work and describe some potential ethical concerns associated with them in Chapter 12 "Other Methods of Data Collection and Analysis" .

Analysis of Qualitative Interview Data

Analysis of qualitative interview data typically begins with a set of transcripts of the interviews conducted. Obtaining said transcripts requires having either taken exceptionally good notes during an interview or, preferably, recorded the interview and then transcribed it. Transcribing interviews is usually the first step toward analyzing qualitative interview data. To transcribe Creating a complete, written copy of a recorded interview by playing the recording back and typing in each word that is spoken on the recording, noting who spoke which words. an interview means that you create, or someone whom you’ve hired creates, a complete, written copy of the recorded interview by playing the recording back and typing in each word that is spoken on the recording, noting who spoke which words. In general, it is best to aim for a verbatim transcription, one that reports word for word exactly what was said in the recorded interview. If possible, it is also best to include nonverbals in an interview’s written transcription. Gestures made by respondents should be noted, as should the tone of voice and notes about when, where, and how spoken words may have been emphasized by respondents.

If you have the time (or if you lack the resources to hire others), I think it is best to transcribe your interviews yourself. I never cease to be amazed by the things I recall from an interview when I transcribe it myself. If the researcher who conducted the interview transcribes it himself or herself, that person will also be able to make a note of nonverbal behaviors and interactions that may be relevant to analysis but that could not be picked up by audio recording. I’ve seen interviewees roll their eyes, wipe tears from their face, and even make obscene gestures that spoke volumes about their feelings but that could not have been recorded had I not remembered to include these details in their transcribed interviews.

Sometimes a respondents’ body language during an interview provides as much information as her spoken words. Qualitative researchers often note respondents’ gestures and other nonverbal cues during an interview.

The goal of analysis The process of arriving at some inferences, lessons, or conclusions by condensing large amounts of data into relatively smaller bits of understandable information. is to reach some inferences, lessons, or conclusions by condensing large amounts of data into relatively smaller, more manageable bits of understandable information. Analysis of qualitative interview data often works inductively (Glaser & Strauss, 1967; Charmaz, 2006). For an additional reminder about what an inductive approach to analysis means, see Chapter 2 "Linking Methods With Theory" . If you would like to learn more about inductive qualitative data analysis, I recommend two titles: Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research . Chicago, IL: Aldine; Charmaz, K. (2006). Constructing grounded theory: A practical guide through qualitative analysis . Thousand Oaks, CA: Sage. To move from the specific observations an interviewer collects to identifying patterns across those observations, qualitative interviewers will often begin by reading through transcripts of their interviews and trying to identify codes. A code A shorthand representation of some more complex set of issues or ideas. is a shorthand representation of some more complex set of issues or ideas. In this usage, the word code is a noun. But it can also be a verb. The process of identifying codes in one’s qualitative data is often referred to as coding . Coding involves identifying themes across interview data by reading and rereading (and rereading again) interview transcripts until the researcher has a clear idea about what sorts of themes come up across the interviews.

Qualitative researcher and textbook author Kristin Esterberg (2002) Esterberg, K. G. (2002). Qualitative methods in social research . Boston, MA: McGraw-Hill. describes coding as a multistage process. Esterberg suggests that there are two types of coding: open coding and focused coding. To analyze qualitative interview data, one can begin by open coding The first stage of developing codes in qualitative data; involves reading data with an open mind and jotting down themes or categories that various bits of data seem to suggest. transcripts. This means that you read through each transcript, line by line, and make a note of whatever categories or themes seem to jump out to you. At this stage, it is important that you not let your original research question or expectations about what you think you might find cloud your ability to see categories or themes. It’s called open coding for a reason—keep an open mind. Open coding will probably require multiple go-rounds. As you read through your transcripts, it is likely that you’ll begin to see some commonalities across the categories or themes that you’ve jotted down. Once you do, you might begin focused coding.

Focused coding A later stage of developing codes in qualitative data; occurs after open coding and involves collapsing or narrowing themes and categories identified in open coding, succinctly naming them, describing them, and identifying passages of data that represent them. involves collapsing or narrowing themes and categories identified in open coding by reading through the notes you made while conducting open coding. Identify themes or categories that seem to be related, perhaps merging some. Then give each collapsed/merged theme or category a name (or code), and identify passages of data that fit each named category or theme. To identify passages of data that represent your emerging codes, you’ll need to read through your transcripts yet again (and probably again). You might also write up brief definitions or descriptions of each code. Defining codes is a way of making meaning of your data and of developing a way to talk about your findings and what your data mean. Guess what? You are officially analyzing data!

As tedious and laborious as it might seem to read through hundreds of pages of transcripts multiple times, sometimes getting started with the coding process is actually the hardest part. If you find yourself struggling to identify themes at the open coding stage, ask yourself some questions about your data. The answers should give you a clue about what sorts of themes or categories you are reading. In their text on analyzing qualitative data, Lofland and Lofland (1995) Lofland, J., & Lofland, L. H. (1995). Analyzing social settings: A guide to qualitative observation and analysis (3rd ed.) Belmont, CA: Wadsworth. identify a set of questions that I find very useful when coding qualitative data. They suggest asking the following:

• Of what topic, unit, or aspect is this an instance?
• What question about a topic does this item of data suggest?
• What sort of answer to a question about a topic does this item of data suggest (i.e., what proposition is suggested)?

Asking yourself these questions about the passages of data that you’re reading can help you begin to identify and name potential themes and categories.

Still feeling uncertain about how this process works? Sometimes it helps to see how interview passages translate into codes. In Table 9.1 "Interview Coding Example" , I present two codes that emerged from the inductive analysis of transcripts from my interviews with child-free adults. I also include a brief description of each code and a few (of many) interview excerpts from which each code was developed.

Table 9.1 Interview Coding Example

As you might imagine, wading through all these data is quite a process. Just as quantitative researchers rely on the assistance of special computer programs designed to help with sorting through and analyzing their data, so, too, do qualitative researchers. Where quantitative researchers have SPSS and MicroCase (and many others), qualitative researchers have programs such as NVivo ( http://www.qsrinternational.com ) and Atlasti ( http://www.atlasti.com ). These are programs specifically designed to assist qualitative researchers with organizing, managing, sorting, and analyzing large amounts of qualitative data. The programs work by allowing researchers to import interview transcripts contained in an electronic file and then label or code passages, cut and paste passages, search for various words or phrases, and organize complex interrelationships among passages and codes.

In sum, the following excerpt, from a paper analyzing the workplace sexual harassment interview data I have mentioned previously, summarizes how the process of analyzing qualitative interview data often works:

All interviews were tape recorded and then transcribed and imported into the computer program NVivo. NVivo is designed to assist researchers with organizing, managing, interpreting, and analyzing non-numerical, qualitative data. Once the transcripts, ranging from 20 to 60 pages each, were imported into NVivo, we first coded the data according to the themes outlined in our interview guide. We then closely reviewed each transcript again, looking for common themes across interviews and coding like categories of data together. These passages, referred to as codes or “meaning units” (Weiss, 2004), Weiss, R. S. (2004). In their own words: Making the most of qualitative interviews. Contexts, 3 , 44–51. were then labeled and given a name intended to succinctly portray the themes present in the code. For this paper, we coded every quote that had something to do with the labeling of harassment. After reviewing passages within the “labeling” code, we placed quotes that seemed related together, creating several sub-codes. These sub-codes were named and are represented by the three subtitles within the findings section of this paper. Our three subcodes were the following: (a) “It’s different because you’re in high school”: Sociability and socialization at work; (b) Looking back: “It was sexual harassment; I just didn’t know it at the time”; and (c) Looking ahead: New images of self as worker and of workplace interactions. Once our sub-codes were labeled, we re-examined the interview transcripts, coding additional quotes that fit the theme of each sub-code. (Blackstone, Houle, & Uggen, 2006) Blackstone, A., Houle, J., & Uggen, C. “At the time, I thought it was great”: Age, experience, and workers’ perceptions of sexual harassment. Presented at the Annual Meeting of the American Sociological Association, Montreal, QC, August 2006. Currently under review.

Strengths and Weaknesses of Qualitative Interviews

As the preceding sections have suggested, qualitative interviews are an excellent way to gather detailed information. Whatever topic is of interest to the researcher employing this method can be explored in much more depth than with almost any other method. Not only are participants given the opportunity to elaborate in a way that is not possible with other methods such as survey research, but they also are able share information with researchers in their own words and from their own perspectives rather than being asked to fit those perspectives into the perhaps limited response options provided by the researcher. And because qualitative interviews are designed to elicit detailed information, they are especially useful when a researcher’s aim is to study social processes, or the “how” of various phenomena. Yet another, and sometimes overlooked, benefit of qualitative interviews that occurs in person is that researchers can make observations beyond those that a respondent is orally reporting. A respondent’s body language, and even her or his choice of time and location for the interview, might provide a researcher with useful data.

Of course, all these benefits do not come without some drawbacks. As with quantitative survey research, qualitative interviews rely on respondents’ ability to accurately and honestly recall whatever details about their lives, circumstances, thoughts, opinions, or behaviors are being asked about. As Esterberg (2002) puts it, “If you want to know about what people actually do, rather than what they say they do, you should probably use observation [instead of interviews].” Esterberg, K. G. (2002). Qualitative methods in social research . Boston, MA: McGraw-Hill. Further, as you may have already guessed, qualitative interviewing is time intensive and can be quite expensive. Creating an interview guide, identifying a sample, and conducting interviews are just the beginning. Transcribing interviews is labor intensive—and that’s before coding even begins. It is also not uncommon to offer respondents some monetary incentive or thank-you for participating. Keep in mind that you are asking for more of participants’ time than if you’d simply mailed them a questionnaire containing closed-ended questions. Conducting qualitative interviews is not only labor intensive but also emotionally taxing. When I interviewed young workers about their sexual harassment experiences, I heard stories that were shocking, infuriating, and sad. Seeing and hearing the impact that harassment had had on respondents was difficult. Researchers embarking on a qualitative interview project should keep in mind their own abilities to hear stories that may be difficult to hear.

• In-depth interviews are semistructured interviews where the researcher has topics and questions in mind to ask, but questions are open ended and flow according to how the participant responds to each.
• Interview guides can vary in format but should contain some outline of the topics you hope to cover during the course of an interview.
• NVivo and Atlas.ti are computer programs that qualitative researchers use to help them with organizing, sorting, and analyzing their data.
• Qualitative interviews allow respondents to share information in their own words and are useful for gathering detailed information and understanding social processes.
• Drawbacks of qualitative interviews include reliance on respondents’ accuracy and their intensity in terms of time, expense, and possible emotional strain.
• Based on a research question you have identified through earlier exercises in this text, write a few open-ended questions you could ask were you to conduct in-depth interviews on the topic. Now critique your questions. Are any of them yes/no questions? Are any of them leading?
• Read the open-ended questions you just created, and answer them as though you were an interview participant. Were your questions easy to answer or fairly difficult? How did you feel talking about the topics you asked yourself to discuss? How might respondents feel talking about them?

9.3 Quantitative Interview Techniques and Considerations

• Define and describe standardized interviews.
• Describe how quantitative interviews differ from qualitative interviews.
• Describe the process and some of the drawbacks of telephone interviewing techniques.
• Describe how the analysis of quantitative interview works.
• Identify the strengths and weaknesses of quantitative interviews.

Quantitative interviews are similar to qualitative interviews in that they involve some researcher/respondent interaction. But the process of conducting and analyzing findings from quantitative interviews also differs in several ways from that of qualitative interviews. Each approach also comes with its own unique set of strengths and weaknesses. We’ll explore those differences here.

Conducting Quantitative Interviews

Much of what we learned in the previous chapter on survey research applies to quantitative interviews as well. In fact, quantitative interviews are sometimes referred to as survey interviews because they resemble survey-style question-and-answer formats. They might also be called standardized interviews Interviews during which the same questions are asked of every participant in the same way, and survey-style question-and-answer formats are utilized. . The difference between surveys and standardized interviews is that questions and answer options are read to respondents rather than having respondents complete a questionnaire on their own. As with questionnaires, the questions posed in a standardized interview tend to be closed ended. See Chapter 8 "Survey Research: A Quantitative Technique" for the definition of closed ended. There are instances in which a quantitative interviewer might pose a few open-ended questions as well. In these cases, the coding process works somewhat differently than coding in-depth interview data. We’ll describe this process in the following subsection.

In quantitative interviews, an interview schedule A document containing the list of questions and answer options that quantitative interviewers read to respondents. is used to guide the researcher as he or she poses questions and answer options to respondents. An interview schedule is usually more rigid than an interview guide. It contains the list of questions and answer options that the researcher will read to respondents. Whereas qualitative researchers emphasize respondents’ roles in helping to determine how an interview progresses, in a quantitative interview, consistency in the way that questions and answer options are presented is very important. The aim is to pose every question-and-answer option in the very same way to every respondent. This is done to minimize interviewer effect Occurs when an interviewee is influenced by how or when questions and answer options are presented by an interviewer. , or possible changes in the way an interviewee responds based on how or when questions and answer options are presented by the interviewer.

Quantitative interviews may be recorded, but because questions tend to be closed ended, taking notes during the interview is less disruptive than it can be during a qualitative interview. If a quantitative interview contains open-ended questions, however, recording the interview is advised. It may also be helpful to record quantitative interviews if a researcher wishes to assess possible interview effect. Noticeable differences in responses might be more attributable to interviewer effect than to any real respondent differences. Having a recording of the interview can help a researcher make such determinations.

Quantitative interviewers are usually more concerned with gathering data from a large, representative sample. As you might imagine, collecting data from many people via interviews can be quite laborious. Technological advances in telephone interviewing procedures can assist quantitative interviewers in this process. One concern about telephone interviewing is that fewer and fewer people list their telephone numbers these days, but random digit dialing (RDD) takes care of this problem. RDD programs dial randomly generated phone numbers for researchers conducting phone interviews. This means that unlisted numbers are as likely to be included in a sample as listed numbers (though, having used this software for quantitative interviewing myself, I will add that folks with unlisted numbers are not always very pleased to receive calls from unknown researchers). Computer-assisted telephone interviewing (CATI) programs have also been developed to assist quantitative survey researchers. These programs allow an interviewer to enter responses directly into a computer as they are provided, thus saving hours of time that would otherwise have to be spent entering data into an analysis program by hand.

Conducting quantitative interviews over the phone does not come without some drawbacks. Aside from the obvious problem that not everyone has a phone, research shows that phone interviews generate more fence-sitters than in-person interviews (Holbrook, Green, & Krosnick, 2003). Holbrook, A. L., Green, M. C., & Krosnick, J. A. (2003). Telephone versus face-to-face interviewing of national probability samples with long questionnaires: Comparisons of respondent satisficing and social desirability response bias. Public Opinion Quarterly, 67 , 79–125. Responses to sensitive questions or those that respondents view as invasive are also generally less accurate when data are collected over the phone as compared to when they are collected in person. I can vouch for this latter point from personal experience. While conducting quantitative telephone interviews when I worked at a research firm, it was not terribly uncommon for respondents to tell me that they were green or purple when I asked them to report their racial identity.

Analysis of Quantitative Interview Data

As with the analysis of survey data, analysis of quantitative interview data usually involves coding response options numerically, entering numeric responses into a data analysis computer program, and then running various statistical commands to identify patterns across responses. Section 8.5 "Analysis of Survey Data" of Chapter 8 "Survey Research: A Quantitative Technique" describes the coding process for quantitative data. But what happens when quantitative interviews ask open-ended questions? In this case, responses are typically numerically coded, just as closed-ended questions are, but the process is a little more complex than simply giving a “no” a label of 0 and a “yes” a label of 1.

In some cases, quantitatively coding open-ended interview questions may work inductively, as described in Section 9.2.2 "Analysis of Qualitative Interview Data" . If this is the case, rather than ending with codes, descriptions of codes, and interview excerpts, the researcher will assign a numerical value to codes and may not utilize verbatim excerpts from interviews in later reports of results. Keep in mind, as described in Chapter 1 "Introduction" , that with quantitative methods the aim is to be able to represent and condense data into numbers. The quantitative coding of open-ended interview questions is often a deductive process. The researcher may begin with an idea about likely responses to his or her open-ended questions and assign a numerical value to each likely response. Then the researcher will review participants’ open-ended responses and assign the numerical value that most closely matches the value of his or her expected response.

Strengths and Weaknesses of Quantitative Interviews

Quantitative interviews offer several benefits. The strengths and weakness of quantitative interviews tend to be couched in comparison to those of administering hard copy questionnaires. For example, response rates tend to be higher with interviews than with mailed questionnaires (Babbie, 2010). Babbie, E. (2010). The practice of social research (12th ed.). Belmont, CA: Wadsworth. That makes sense—don’t you find it easier to say no to a piece of paper than to a person? Quantitative interviews can also help reduce respondent confusion. If a respondent is unsure about the meaning of a question or answer option on a questionnaire, he or she probably won’t have the opportunity to get clarification from the researcher. An interview, on the other hand, gives the researcher an opportunity to clarify or explain any items that may be confusing.

As with every method of data collection we’ve discussed, there are also drawbacks to conducting quantitative interviews. Perhaps the largest, and of most concern to quantitative researchers, is interviewer effect. While questions on hard copy questionnaires may create an impression based on the way they are presented, having a person administer questions introduces a slew of additional variables that might influence a respondent. As I’ve said, consistency is key with quantitative data collection—and human beings are not necessarily known for their consistency. Interviewing respondents is also much more time consuming and expensive than mailing questionnaires. Thus quantitative researchers may opt for written questionnaires over interviews on the grounds that they will be able to reach a large sample at a much lower cost than were they to interact personally with each and every respondent.

• Unlike qualitative interviews, quantitative interviews usually contain closed-ended questions that are delivered in the same format and same order to every respondent.
• Quantitative interview data are analyzed by assigning a numerical value to participants’ responses.
• While quantitative interviews offer several advantages over self-administered questionnaires such as higher response rates and lower respondent confusion, they have the drawbacks of possible interviewer effect and greater time and expense.
• The General Social Survey (GSS), which we’ve mentioned in previous chapters, is administered via in-person interview, just like quantitative interviewing procedures described here. Read more about the GSS at http://www.norc.uchicago.edu/GSS+Website .
• Take a few of the open-ended questions you created after reading Section 9.2 "Qualitative Interview Techniques and Considerations" on qualitative interviewing techniques. See if you can turn them into closed-ended questions.

9.4 Issues to Consider for All Interview Types

• Identify the main issues that both qualitative and quantitative interviewers should consider.
• Describe the options that interviewers have for balancing power between themselves and interview participants.
• Describe and define rapport.
• Define the term probe and describe how probing differs in qualitative and quantitative interviewing.

While quantitative interviews resemble survey research in their question/answer formats, they share with qualitative interviews the characteristic that the researcher actually interacts with her or his subjects. The fact that the researcher interacts with his or her subjects creates a few complexities that deserve attention. We’ll examine those here.

First and foremost, interviewers must be aware of and attentive to the power differential between themselves and interview participants. The interviewer sets the agenda and leads the conversation. While qualitative interviewers aim to allow participants to have some control over which or to what extent various topics are discussed, at the end of the day it is the researcher who is in charge (at least that is how most respondents will perceive it to be). As the researcher, you are asking someone to reveal things about themselves they may not typically share with others. Also, you are generally not reciprocating by revealing much or anything about yourself. All these factors shape the power dynamics of an interview.

A number of excellent pieces have been written dealing with issues of power in research and data collection. Feminist researchers in particular paved the way in helping researchers think about and address issues of power in their work (Oakley, 1981). Oakley, A. (1981). Interviewing women: A contradiction in terms. In H. Roberts (Ed.), Doing feminist research (pp. 30–61). London, UK: Routledge & Kegan Paul. Suggestions for overcoming the power imbalance between researcher and respondent include having the researcher reveal some aspects of her own identity and story so that the interview is a more reciprocal experience rather than one-sided, allowing participants to view and edit interview transcripts before the researcher uses them for analysis, and giving participants an opportunity to read and comment on analysis before the researcher shares it with others through publication or presentation (Reinharz, 1992; Hesse-Biber, Nagy, & Leavy, 2007). Reinharz, S. (1992). Feminist methods in social research . New York, NY: Oxford University Press; Hesse-Biber, S. N., & Leavy, P. L. (Eds.). (2007). Feminist research practice: A primer . Thousand Oaks, CA: Sage. On the other hand, some researchers note that sharing too much with interview participants can give the false impression that there is no power differential, when in reality researchers retain the ability to analyze and present participants’ stories in whatever way they see fit (Stacey, 1988). Stacey, J. (1988). Can there be a feminist ethnography? Women’s Studies International Forum, 11 , 21–27.

However you feel about sharing details about your background with an interview participant, another way to balance the power differential between yourself and your interview participants is to make the intent of your research very clear to the subjects. Share with them your rationale for conducting the research and the research question(s) that frame your work. Be sure that you also share with subjects how the data you gather will be used and stored. Also, be sure that participants understand how their privacy will be protected including who will have access to the data you gather from them and what procedures, such as using pseudonyms, you will take to protect their identities. Many of these details will be covered by your institutional review board’s informed consent procedures and requirements, but even if they are not, as researchers we should be attentive to how sharing information with participants can help balance the power differences between ourselves and those who participate in our research.

Feminist researchers offer several strategies for balancing the power between interviewers and their research participants.

There are no easy answers when it comes to handling the power differential between the researcher and researched, and even social scientists do not agree on the best approach for doing so. It is nevertheless an issue to be attentive to when conducting any form of research, particularly those that involve interpersonal interactions and relationships with research participants.

Location, Location, Location

One way to balance the power between researcher and respondent is to conduct the interview in a location of the participants’ choosing, where he or she will feel most comfortable answering your questions. Interviews can take place in any number of locations—in respondents’ homes or offices, researchers’ homes or offices, coffee shops, restaurants, public parks, or hotel lobbies, to name just a few possibilities. I have conducted interviews in all these locations, and each comes with its own set of benefits and its own challenges. While I would argue that allowing the respondent to choose the location that is most convenient and most comfortable for her or him is of utmost importance, identifying a location where there will be few distractions is also important. For example, some coffee shops and restaurants are so loud that recording the interview can be a challenge. Other locations may present different sorts of distractions. For example, I have conducted several interviews with parents who, out of necessity, spent more time attending to their children during an interview than responding to my questions (of course, depending on the topic of your research, the opportunity to observe such interactions could be invaluable). As an interviewer, you may want to suggest a few possible locations, and note the goal of avoiding distractions, when you ask your respondents to choose a location.

Of course, the extent to which a respondent should be given complete control over choosing a location must also be balanced by accessibility of the location to you, the interviewer, and by your safety and comfort level with the location. I once agreed to conduct an interview in a respondent’s home only to discover on arriving that the living room where we conducted the interview was decorated wall to wall with posters representing various white power organizations displaying a variety of violently racist messages. Though the topic of the interview had nothing to do with the topic of the respondent’s home décor, the discomfort, anger, and fear I felt during the entire interview consumed me and certainly distracted from my ability to carry on the interview. In retrospect, I wish I had thought to come up with some excuse for needing to reschedule the interview and then arranged for it to happen in a more neutral location. While it is important to conduct interviews in a location that is comfortable for respondents, doing so should never come at the expense of your safety.

Researcher-Respondent Relationship

Finally, a unique feature of interviews is that they require some social interaction, which means that to at least some extent, a relationship is formed between interviewer and interviewee. While there may be some differences in how the researcher-respondent relationship works depending on whether your interviews are qualitative or quantitative, one essential relationship element is the same: R-E-S-P-E-C-T. You should know by now that I can’t help myself. If you, too, now have Aretha Franklin on the brain, feel free to excuse yourself for a moment to enjoy a song and dance: http://www.youtube.com/watch?v=z0XAI-PFQcA . A good rapport between you and the person you interview is crucial to successful interviewing. Rapport The sense of connection a researcher establishes with a participant. is the sense of connection you establish with a participant. Some argue that this term is too clinical, and perhaps it implies that a researcher tricks a participant into thinking they are closer than they really are (Esterberg, 2002). Esterberg, K. G. (2002). Qualitative methods in social research . Boston, MA: McGraw-Hill. While it is unfortunately true that some researchers might adopt this misguided approach to rapport, that is not the sense in which I use the term here nor is that the sort of rapport I advocate researchers attempt to establish with their subjects. Instead, as already mentioned, it is respect that is key.

There are no big secrets or tricks for how to show respect for research participants. At its core, the interview interaction should not differ from any other social interaction in which you show gratitude for a person’s time and respect for a person’s humanity. It is crucial that you, as the interviewer, conduct the interview in a way that is culturally sensitive. In some cases, this might mean educating yourself about your study population and even receiving some training to help you learn to effectively communicate with your research participants. Do not judge your research participants; you are there to listen to them, and they have been kind enough to give you their time and attention. Even if you disagree strongly with what a participant shares in an interview, your job as the researcher is to gather the information being shared with you, not to make personal judgments about it. In case you still feel uncertain about how to establish rapport and show your participants respect, I will leave you with a few additional bits of advice.

Developing good rapport requires good listening. In fact, listening during an interview is an active, not a passive, practice. Active listening Occurs when an interviewer demonstrates that he or she understands what an interview participant has said; requires probes or follow-up questions that indicate such understanding. means that you, the researcher, participate with the respondent by showing that you understand and follow whatever it is that he or she is telling you (Devault, 1990). For more on the practice of listening, especially in qualitative interviews, see Devault, M. (1990). Talking and listening from women’s standpoint: Feminist strategies for interviewing and analysis. Social Problems, 37 , 96–116. The questions you ask respondents should indicate that you’ve actually heard what they’ve just said. Active listening probably means that you will probe the respondent for more information from time to time throughout the interview. A probe A request, on the part of an interviewer, for more information from an interview participant. is a request for more information. Both qualitative and quantitative interviewers probe respondents, though the way they probe usually differs. In quantitative interviews, probing should be uniform. Often quantitative interviewers will predetermine what sorts of probes they will use. As an employee at the research firm I’ve mentioned before, our supervisors used to randomly listen in on quantitative telephone interviews we conducted. We were explicitly instructed not to use probes that might make us appear to agree or disagree with what respondents said. So “yes” or “I agree” or a questioning “hmmmm” were discouraged. Instead, we could respond with “thank you” to indicate that we’d heard a respondent. We could use “yes” or “no” if, and only if, a respondent had specifically asked us if we’d heard or understood what they had just said.

In some ways qualitative interviews better lend themselves to following up with respondents and asking them to explain, describe, or otherwise provide more information. This is because qualitative interviewing techniques are designed to go with the flow and take whatever direction the respondent goes during the interview. Nevertheless, it is worth your time to come up with helpful probes in advance of an interview even in the case of a qualitative interview. You certainly do not want to find yourself stumped or speechless after a respondent has just said something about which you’d like to hear more. This is another reason that practicing your interview in advance with people who are similar to those in your sample is a good idea.

• While there are several key differences between qualitative and quantitative interviewing techniques, all interviewers using either technique should take into consideration the power differential between themselves and respondents, should take care in identifying a location for an interview, and should take into account the fact that an interview is, to at least some extent, a form of relationship.
• Feminist researchers paved the way for helping interviewers think about how to balance the power differential between themselves and interview participants.
• Interviewers must always be respectful of interview participants.
• Imagine that you will be conducting interviews. What are some possible locations in your area you think might be good places to conduct interviews? What makes those locations good?
• What do you think about the suggestions for balancing power between interviewers and interviewees? How much of your own story do you think you’d be likely to share with interview participants? Why? What are the possible consequences (positive and negative) of revealing information about yourself when you’re the researcher?

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Survey - data collection tool for gather information from a group of individuals. Data can be factual or opinions. Administration options includes structured interview or participant/respondent completing survey on their own.  

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Qualitative vs Quantitative Research Methods & Data Analysis

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research, on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis.

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded.

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

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Methodology

• What Is Quantitative Research? | Definition, Uses & Methods

What Is Quantitative Research? | Definition, Uses & Methods

Published on June 12, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analyzing non-numerical data (e.g., text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

• What is the demographic makeup of Singapore in 2020?
• How has the average temperature changed globally over the last century?
• Does environmental pollution affect the prevalence of honey bees?
• Does working from home increase productivity for people with long commutes?

Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, other interesting articles, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

• In descriptive research , you simply seek an overall summary of your study variables.
• In correlational research , you investigate relationships between your study variables.
• In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalized to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

Note that quantitative research is at risk for certain research biases , including information bias , omitted variable bias , sampling bias , or selection bias . Be sure that you’re aware of potential biases as you collect and analyze your data to prevent them from impacting your work too much.

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Once data is collected, you may need to process it before it can be analyzed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualize your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalizations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

First, you use descriptive statistics to get a summary of the data. You find the mean (average) and the mode (most frequent rating) of procrastination of the two groups, and plot the data to see if there are any outliers.

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardize data collection and generalize findings . Strengths of this approach include:

• Replication

Repeating the study is possible because of standardized data collection protocols and tangible definitions of abstract concepts.

• Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

• Large samples

Data from large samples can be processed and analyzed using reliable and consistent procedures through quantitative data analysis.

• Hypothesis testing

Using formalized and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

• Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

• Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

• Structural bias

Despite standardized procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

• Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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Top 20 Quantitative Interview Questions & Answers

Master your responses to Quantitative related interview questions with our example questions and answers. Boost your chances of landing the job by learning how to effectively communicate your Quantitative capabilities.

Embarking on a career in the quantitative field means immersing yourself in a world where numbers, data analysis, and algorithmic thinking are paramount. Whether you’re aiming for a role in finance, research, or another sector that relies heavily on quantitative skills, it’s imperative to demonstrate not just your technical acumen but also your ability to apply complex mathematical concepts to real-world problems.

During an interview for a quantitative position, expect to encounter questions designed to probe your expertise in statistical methods, your experience with programming languages, and your knack for critical thinking under pressure. To help you navigate these challenges and convey your quantitative prowess effectively, we’ve curated a selection of typical interview questions tailored for the quantitatively inclined professional, coupled with strategic advice on formulating compelling responses.

Common Quantitative Interview Questions

1. how would you validate a statistical model you’ve developed for predicting market trends.

Understanding the validation of a statistical model is essential, particularly when predicting market trends. It’s not just about accuracy but also about the model’s adaptability to changing conditions and the robustness of the underlying data. Candidates should be prepared to discuss their experience with statistical validation techniques and their ability to foresee potential issues.

When responding, highlight your approach to model validation, which might include split-sample testing, cross-validation, or out-of-time validation. Discuss the importance of using relevant and high-quality data, as well as the need to test the model against unseen data to gauge its generalizability. Emphasize your understanding of key performance metrics—like R-squared, RMSE, or MAE—that can help quantify the model’s accuracy. Show that you are also mindful of the model’s limitations and the importance of continuous monitoring and updating to maintain its predictive relevance over time.

Example: “ To validate a statistical model designed for predicting market trends, I would employ a combination of split-sample testing and cross-validation techniques to ensure the model’s robustness and generalizability. Initially, I would partition the dataset into training and testing subsets, using the training set to build the model and the testing set to evaluate its predictive power. This approach helps to mitigate overfitting and provides an initial assessment of the model’s performance on unseen data.

Subsequently, I would implement k-fold cross-validation, which further refines the validation process by averaging the model’s performance across different partitions of the data, offering a more comprehensive view of its predictive capabilities. Throughout this process, I would closely monitor key performance metrics such as R-squared for assessing the proportion of variance explained by the model, and RMSE or MAE for quantifying the prediction errors, adjusting the model accordingly to optimize these metrics.

It’s crucial to recognize that model validation is not a one-time task but an ongoing process. As market conditions evolve, the model should be periodically revalidated using fresh, out-of-time data to ensure its continued relevance. Moreover, I would remain vigilant about the quality of data inputs, as the predictive accuracy of the model is inherently dependent on the relevance and integrity of the data it is trained on. Continuous monitoring and updating of the model are essential to account for new patterns or structural changes in the market, ensuring the model’s long-term predictive validity.”

2. Describe an experience where you had to interpret complex data sets without clear patterns.

For roles that require a high degree of analytical rigor, candidates must be adept at interpreting ambiguous data. This question probes the candidate’s persistence, creativity, and ability to communicate complex findings to non-technical stakeholders.

When responding, candidates should outline the steps they took to analyze the data, including any specific methodologies or tools they employed. They should discuss how they identified variables of interest, dealt with missing or noisy data, and the reasoning behind the particular analytical approaches they chose. It’s also beneficial to explain how they validated their conclusions and the impact of their findings on the decision-making process. A story that conveys the complexity of the situation, the approach taken, and the eventual outcome or learning experience will demonstrate their competence in handling such challenges.

Example: “ In an experience dealing with a multifaceted data set lacking apparent patterns, I approached the analysis through a combination of exploratory data analysis (EDA) and advanced statistical techniques. Initially, I employed EDA methods, such as visualizing the data through histograms, boxplots, and scatter plots, to identify any underlying structures or anomalies. This preliminary step was crucial in formulating hypotheses about potential relationships within the data.

Subsequently, I used dimensionality reduction techniques like Principal Component Analysis (PCA) to distill the data into its most informative components. This was followed by implementing machine learning algorithms, including clustering methods like K-means and DBSCAN, to detect any subtle groupings or trends that were not immediately obvious. To address missing or noisy data, I applied imputation methods and robust statistical measures to minimize bias.

Throughout the analysis, I rigorously validated the findings using cross-validation techniques and sensitivity analysis to ensure the robustness of the results. The insights gained from this comprehensive approach led to the identification of several key drivers that were previously obscured. These findings informed strategic decisions, resulting in optimized processes and a significant improvement in the overall efficiency of the project. The experience underscored the importance of a methodical and iterative approach to data analysis, especially when faced with complex and initially inscrutable data sets.”

3. What metrics would you prioritize when assessing the financial health of a tech startup?

A tech startup’s financial health is not just about current profitability. Candidates should be ready to discuss how they evaluate a startup’s potential for growth, scalability, and other industry-specific KPIs that reflect the unique challenges of the technology sector.

When responding to this question, candidates should focus on a balanced set of metrics such as cash flow, runway, customer acquisition cost (CAC), customer lifetime value (CLV), monthly recurring revenue (MRR), and churn rate. It’s important to articulate why each metric is relevant and how it would influence strategic decisions. For instance, a high CAC relative to CLV could signal unsustainable growth, while a short runway might necessitate immediate fundraising or cost-cutting measures. Demonstrating an understanding of how these metrics interplay can show your analytical skills and your ability to guide a startup towards financial stability and growth.

Example: “ When evaluating the financial health of a tech startup, I would prioritize a mix of liquidity, profitability, and growth metrics. Cash flow is paramount as it indicates the company’s ability to sustain operations and grow without external financing. I would assess the runway by comparing the current burn rate with available cash to estimate the time before additional capital is required. This metric is critical for understanding the immediacy of fundraising needs or the necessity for cost optimization.

Simultaneously, I would analyze customer-centric metrics such as CAC and CLV to gauge the efficiency of the startup’s growth strategies. A lower CAC relative to CLV suggests a sustainable acquisition strategy and a healthy potential for long-term profitability. MRR and churn rate offer insights into the recurring revenue stability and customer retention, respectively. High MRR growth coupled with low churn rates often indicates product-market fit and a loyal customer base, which are strong indicators of a startup’s upward trajectory. These metrics collectively provide a comprehensive view of the startup’s financial performance and can inform strategic decisions to ensure both short-term survival and long-term success.”

4. Outline your process for conducting a Monte Carlo simulation in portfolio risk assessment.

Conducting a Monte Carlo simulation is a key skill for effective portfolio risk assessment. Candidates should be prepared to explain their approach to this stochastic technique and how it helps them understand the impact of risk and uncertainty in financial models.

When responding, you should clearly outline the key steps: defining a domain of possible inputs, generating inputs randomly from a probability distribution that reflects the risk or uncertainty being modeled, running a deterministic computation with those inputs, and aggregating the results to get a probability distribution of the output. It’s important to detail your experience with relevant software or programming languages, explain how you ensure the accuracy and reliability of the data, and discuss how you interpret the results to inform risk management decisions. Demonstrating an understanding of the limitations and assumptions of the model will also show depth of knowledge.

Example: “ In conducting a Monte Carlo simulation for portfolio risk assessment, I begin by defining the domain of possible inputs, which typically includes historical return distributions for each asset class, correlations, and volatilities. I ensure these inputs are based on robust statistical analysis and are reflective of current market conditions. Using these distributions, I generate a large number of random scenarios for future returns, employing a pseudo-random number generator or a quasi-random sequence for better convergence properties.

Next, I run deterministic computations for each scenario, which involves calculating the portfolio returns and risk metrics such as VaR (Value at Risk) or CVaR (Conditional Value at Risk). This is done through a simulation engine that I either code in a language like Python or R, or by using specialized software such as @RISK or Crystal Ball, depending on the complexity and the specific requirements of the task.

The aggregation of results is critical; I analyze the output distribution to assess the risk profile of the portfolio, looking at the range of outcomes and the likelihood of extreme losses. I interpret these results within the context of the portfolio’s investment strategy and risk tolerance, providing actionable insights for risk management decisions.

Throughout the process, I am mindful of the assumptions and limitations inherent in the model, such as the assumption of a static correlation structure or the potential for model risk due to input uncertainty. I conduct sensitivity analyses to understand how changes in the inputs affect the outcomes, ensuring that the final recommendations are robust and account for a range of possible market conditions.”

5. In what ways have you utilized machine learning algorithms to enhance quantitative analysis?

In modern finance, marketing, or data science, machine learning is increasingly important. Candidates should be able to discuss how they use machine learning algorithms to enhance decision-making and drive innovation.

When responding to this question, a candidate should highlight specific projects or experiences where machine learning algorithms directly impacted the analysis. Discuss the type of algorithms used—such as decision trees, neural networks, or clustering techniques—and the outcomes they achieved. Explain the problem-solving process, including how the algorithm was selected, the data preparation involved, and how the results were validated and interpreted. It’s essential to articulate the value added through these techniques, such as increased accuracy of predictions, time saved, or improved profitability.

Example: “ In a recent project, I leveraged Random Forest algorithms to enhance the predictive accuracy of a quantitative trading strategy. By integrating a multitude of decision trees, the model was trained on historical market data to identify complex patterns and interactions between various financial indicators. The ensemble approach not only improved the robustness of the predictions against overfitting but also increased the out-of-sample Sharpe ratio significantly, leading to a more profitable and risk-adjusted return profile.

Furthermore, I employed neural networks for time-series forecasting, specifically LSTM (Long Short-Term Memory) models, to capture the temporal dependencies in asset price movements. The LSTM’s ability to remember information over extended periods was crucial for understanding the momentum and mean-reversion effects in the markets. The model’s forecasts were instrumental in optimizing trade execution and managing dynamic portfolio allocations, resulting in a marked decrease in slippage costs and enhanced overall portfolio performance. Validation of the models’ effectiveness was conducted through rigorous backtesting and forward performance monitoring, ensuring that the machine learning applications provided tangible benefits to the quantitative analysis framework.”

6. Detail a scenario where you significantly improved a model’s accuracy; what changes did you make?

Model improvement is a critical skill in quantitative roles. Candidates should be ready to showcase their problem-solving skills and their ability to enhance existing systems, demonstrating a deep understanding of data science methodologies.

When responding, candidates should outline the situation clearly, describing the model in question, the specific issues with its accuracy, and the steps taken to address them. They should discuss the data analysis performed, any algorithm adjustments, feature engineering, or cross-validation techniques employed. Articulating the reasoning behind each change and how it contributed to the overall improvement in accuracy will show a thoughtful and methodical approach to model optimization.

Example: “ In one scenario, the model’s performance was hindered by overfitting due to a high dimensionality of features relative to the number of observations. To address this, I implemented a combination of feature selection and regularization techniques. I started by applying a variance threshold to remove features with minimal variance, as they were unlikely to contribute significantly to the model’s predictive power. Then, I used a recursive feature elimination process with cross-validation (RFECV) to identify and retain the most impactful features.

Subsequently, I incorporated L1 regularization (Lasso) into the model to penalize the magnitude of the coefficients and encourage sparsity, effectively reducing the complexity of the model. This regularization technique not only helped in feature selection but also improved the model’s generalization by discouraging overfitting. The changes led to a more parsimonious model with a better balance between bias and variance, resulting in a significant uplift in out-of-sample accuracy, as confirmed by a stratified K-fold cross-validation approach.”

7. Walk us through a time when you had to explain quantitative findings to a non-technical audience.

Translating complex quantitative data into digestible insights is a valuable skill. Candidates should be prepared to discuss how they make complex numerical information accessible and actionable for those without a technical background.

When responding, begin by outlining the context of the quantitative findings you were dealing with, emphasizing the audience’s lack of technical expertise. Describe the steps you took to simplify the data, such as using analogies, visual aids, or breaking down the methodology into layman’s terms. Highlight your ability to engage with the audience, asking questions to gauge their understanding, and adjusting your explanation accordingly. Conclude by reflecting on the outcome—how your communication facilitated a better understanding and led to informed decisions or actions.

Example: “ In a recent project, I was tasked with presenting the results of a complex regression analysis that identified key factors affecting customer retention rates. The audience comprised department heads with varied backgrounds, most of whom lacked statistical training. To bridge the gap, I distilled the findings into the core message: which factors were most influential and by how much. I used a simple analogy, comparing the statistical model to a recipe where each ingredient’s quantity affects the final taste, to convey the idea of coefficients impacting the outcome.

I supplemented this with a clear, intuitive visualization—a bar chart showing the relative importance of each factor, avoiding technical jargon like “p-values” or “confidence intervals.” During the presentation, I engaged with the audience by asking if the visual representation made sense and if they could relate the findings to their experiences. This interaction helped me tailor the explanation further, ensuring clarity.

The outcome was a productive discussion that led to actionable strategies. The department heads grasped the key takeaways and were able to brainstorm targeted initiatives to improve customer retention, demonstrating that the quantitative findings had been successfully communicated and understood.”

8. Which quantitative research publication has most influenced your work and why?

The influence of quantitative research on your work can demonstrate scholarly rigor and breadth of knowledge. Candidates should be ready to discuss how they integrate complex data from research publications into their thought process.

To respond effectively, you should select a publication that is not only reputable but also closely related to your field of work or the position you are applying for. Discuss the publication’s findings or methodologies and clearly articulate how it has shaped your approach to data analysis or problem-solving. Be prepared to explain the publication’s impact on your thought process or professional practices, providing concrete examples of how you have applied its insights to your work.

Example: “ The publication that has most influenced my work is “The Econometrics of Financial Markets” by John Y. Campbell, Andrew W. Lo, and A. Craig MacKinlay. This seminal work provided a comprehensive framework for analyzing financial markets using econometric methods, which has been invaluable in both my approach to modeling market behaviors and in developing predictive analytics for asset pricing.

The rigorous treatment of topics such as time-series analysis, the Capital Asset Pricing Model (CAPM), and the Efficient Market Hypothesis (EMH) within this text has been particularly impactful. It has honed my ability to critically evaluate model assumptions and to integrate econometric techniques with machine learning algorithms for enhanced forecasting accuracy. For instance, leveraging the concepts from this publication, I’ve successfully implemented vector autoregression models to predict stock prices, accounting for the dynamic interplay between multiple economic indicators. The book’s empirical focus also encouraged a data-driven mindset that ensures the robustness and validity of my analyses, a principle that I’ve upheld in all my quantitative endeavors.”

9. What approach do you take to ensure compliance with data protection regulations during analysis?

Data protection is a critical concern, especially when handling sensitive information. Candidates should be prepared to discuss their knowledge of data protection laws and how they incorporate compliance into their daily work processes.

To respond effectively, outline specific steps you take to ensure compliance, such as anonymizing data, implementing access controls, and conducting regular audits. You may also mention staying updated on the latest regulations, attending training sessions, and collaborating with legal or compliance teams. It’s essential to demonstrate a proactive approach to data protection, showcasing that you prioritize ethical considerations alongside analytical rigor.

Example: “ In ensuring compliance with data protection regulations during analysis, my approach is both proactive and systematic. Initially, I conduct a thorough data inventory to classify the sensitivity of the data sets and understand the specific compliance requirements associated with each. Following this, I employ data minimization techniques, ensuring that only the necessary data for the analysis is processed, and anonymize or pseudonymize personal data to mitigate any privacy risks.

I implement robust access controls, restricting data access to authorized personnel and applying the principle of least privilege. To maintain the integrity of these measures, I regularly perform audits and monitor data access logs to detect any unauthorized attempts or non-compliance issues. Furthermore, I stay abreast of the evolving regulatory landscape by attending advanced training sessions and actively engaging with legal or compliance teams to ensure that my analysis practices are aligned with the latest data protection standards. This ongoing dialogue allows for the anticipation of regulatory changes and the seamless integration of new compliance requirements into the analytical workflow.”

10. Share an example of how you’ve used hypothesis testing to inform a business decision.

Hypothesis testing is a fundamental statistical method in decision-making. Candidates should be ready to explain how they conduct hypothesis tests and how they use the results to inform business strategies.

When responding to this question, articulate a clear scenario where hypothesis testing was central to resolving a business question or challenge. Describe the hypothesis you tested, the data you used, and the statistical method you employed. Most importantly, discuss the results of the test and how you interpreted them to make a well-informed business decision. Highlight how your analysis had a tangible impact on the business, such as improving a product, optimizing a process, or enhancing customer satisfaction. This will showcase your ability to bridge the gap between data analysis and practical business outcomes.

Example: “ In a recent project, we were faced with the challenge of optimizing the pricing strategy for a new product line. The hypothesis was that by increasing the price point by 10%, we would not significantly affect the sales volume, aiming to increase overall revenue without deterring customers. To test this hypothesis, we conducted an A/B test, where Group A was exposed to the current pricing and Group B to the increased price.

Utilizing a t-test for the difference in means between the two groups, we analyzed sales data over a four-week period. The results indicated that there was no statistically significant difference in sales volume (p > 0.05), suggesting that the price increase did not negatively impact sales. Based on this analysis, we advised the business to implement the new pricing strategy across all markets, which ultimately led to a 9% rise in revenue without a drop in sales volume. This decision was further supported by monitoring post-implementation sales data, confirming the sustainability of the new pricing model.”

11. When building predictive models, how do you address multicollinearity in your independent variables?

Multicollinearity can complicate the interpretation of predictive models. Candidates should be prepared to discuss how they diagnose and resolve issues of multicollinearity to ensure the accuracy of their models.

When responding to this question, you should demonstrate a clear understanding of the concept of multicollinearity and its implications for predictive modeling. Outline the methods you employ to detect multicollinearity, such as examining correlation matrices or variance inflation factors (VIF). Then, explain the strategies you apply to address it, which might include removing highly correlated predictors, combining them into a single variable, or using regularization techniques like Lasso or Ridge regression that can penalize coefficients for correlated variables and reduce overfitting. Your answer should convey a methodical and informed approach to maintaining the integrity of your predictive models.

Example: “ To address multicollinearity among independent variables in predictive modeling, I first employ diagnostic tools like correlation matrices and Variance Inflation Factor (VIF) thresholds to detect the presence and severity of multicollinearity. A VIF above 5 or 10, depending on the context and domain-specific thresholds, usually signals a multicollinearity issue that needs to be addressed.

Once identified, I tackle multicollinearity using a combination of domain knowledge and statistical techniques. If two variables are highly correlated and one is deemed less important based on domain knowledge, I might remove it to reduce redundancy. Alternatively, I might combine correlated variables into a single feature through principal component analysis or factor analysis, which preserves the information while mitigating the multicollinearity effect. In situations where variable selection is crucial, I might apply regularization methods like Lasso regression, which is designed to perform both variable selection and parameter shrinkage, effectively handling multicollinearity by penalizing the coefficients of correlated predictors and driving some to zero. This approach ensures the model remains robust and generalizable without compromising the predictive power.”

12. Describe a situation where you integrated qualitative insights into your quantitative analysis.

Blending quantitative and qualitative insights can lead to more effective analysis. Candidates should be ready to discuss how they incorporate qualitative factors like consumer behavior and market trends into their data analysis.

When responding, candidates should recount a specific scenario where they identified the need for qualitative input to complement their quantitative findings. They might discuss how they gathered qualitative data, such as customer feedback or expert opinions, and the methods used to integrate this information with quantitative results. The aim is to illustrate their thought process, the challenges they faced, and how the integration of both data types led to a more informed decision or recommendation. It’s important to convey adaptability, analytical depth, and a recognition that numbers tell a part of the story, but not the whole.

Example: “ In a recent analysis of customer churn, I recognized that while the quantitative data highlighted trends in cancellations, it didn’t provide the underlying reasons for customer dissatisfaction. To address this gap, I conducted a series of in-depth interviews and focus groups to gain qualitative insights. I used thematic analysis to identify common reasons for churn that weren’t apparent in the numerical data, such as perceived value and customer service interactions.

Integrating these qualitative insights with the quantitative data involved creating a framework that allowed for a holistic view of the factors influencing churn. By overlaying sentiment analysis on the churn rate across different customer segments, I was able to pinpoint specific service touchpoints that required improvement. This mixed-methods approach not only enriched the analysis but also led to targeted strategies that reduced churn by 15% over the next quarter. The process highlighted the importance of blending qualitative nuances with quantitative rigor to derive actionable intelligence.”

13. How do you determine the right sample size for a statistically significant survey study?

Determining the right sample size is crucial for survey studies. Candidates should be prepared to discuss their approach to calculating sample size and ensuring the reliability and validity of their research findings.

When responding to this question, one should discuss the factors that influence sample size determination, such as the desired confidence level, the margin of error, the population size, and the expected effect size or variability in the data. It’s important to articulate a systematic approach to sample size calculation, perhaps referencing specific statistical formulas or software used to make these estimations. Offering an example from past experience where you determined sample size for a study can showcase your practical application of these concepts, along with any lessons learned from the outcome of that research.

Example: “ Determining the right sample size for a statistically significant survey study hinges on balancing precision and practicality. Initially, I establish the desired confidence level, typically 95% for conventional standards, which sets the Z-score used in calculations. The margin of error, reflecting the maximum expected difference between the sample statistic and the population parameter, is chosen based on the level of precision required for the study’s objectives. For a general population survey, a 5% margin is common, but this might be tightened for more sensitive analyses.

I then consider the population size, especially relevant when dealing with finite populations, to apply the finite population correction if necessary. The expected effect size or variability in the data, informed by prior research or pilot studies, guides my estimation of the standard deviation, which is crucial for calculating the required sample size. Utilizing formulas for sample size calculation or software like G*Power or nQuery, I incorporate these parameters to yield a sample size that balances statistical rigor with resource constraints. For instance, in a previous study estimating the prevalence of a health behavior, I used a conservative estimate of variability to ensure the sample size was sufficient to detect even small prevalence rates, which was vital for the subsequent health intervention planning. The study’s success underscored the importance of meticulous sample size calculation in achieving meaningful and actionable results.”

14. What is your strategy for staying current with advancements in quantitative methods and tools?

Keeping up with the latest trends and tools is essential in the quantitative field. Candidates should be ready to discuss their commitment to continuous learning and how they stay current with new technologies and methodologies.

When responding to this question, a candidate should highlight their proactive approach to professional development. This could include subscribing to industry journals, attending workshops and conferences, participating in online courses, or being part of professional forums and networks. Demonstrating a systematic approach to integrating new knowledge and tools into one’s work routine will reassure employers that the candidate is both technically proficient and strategically prepared to contribute to the organization’s success.

Example: “ To stay abreast of the latest advancements in quantitative methods and tools, I maintain a disciplined approach to continuous learning and professional development. I regularly subscribe to and read key industry journals such as the “Journal of Quantitative Analysis” and “Quantitative Finance,” which provide insights into cutting-edge research and applications. Additionally, I leverage online platforms like Coursera and edX to enroll in relevant courses that sharpen my skills and deepen my understanding of new techniques.

I also prioritize attending annual conferences and workshops, which not only offer exposure to innovative methodologies but also provide opportunities to engage with thought leaders and practitioners in the field. This engagement is complemented by active participation in professional forums and networks, such as the Quantitative Finance Professional Group on LinkedIn, where I can exchange ideas and discuss practical challenges with peers. By systematically integrating new knowledge into my existing framework, I ensure that my quantitative analysis remains robust, relevant, and aligned with the state-of-the-art in the field.”

15. Provide an instance where you had to adapt your analytical approach due to unexpected data limitations.

Agility in problem-solving is key when data is incomplete or unexpected. Candidates should be prepared to discuss how they handle such challenges while maintaining data integrity and accuracy.

When responding, recount a specific scenario where you encountered data limitations, emphasizing how you evaluated the situation, identified the constraints, and decided on an alternative approach. Outline the steps you took to ensure the new approach still provided valuable insights, and if possible, share the outcome of your analysis. This will illustrate your adaptability, problem-solving skills, and commitment to delivering results despite challenges.

Example: “ In a project where I was modeling customer churn, I encountered a situation where the historical data was far less comprehensive than initially anticipated. The data lacked granularity on customer interactions and product usage, which were critical for building a robust predictive model. I quickly realized that traditional regression techniques would not yield the predictive power needed due to the sparsity of data.

To adapt, I pivoted to a survival analysis approach, leveraging what we did have—customer tenure and churn events. This allowed me to model churn risk over time without the need for detailed interaction data. I also employed bootstrapping methods to enhance the stability of the model given the limited dataset. By focusing on the time-to-event aspect, I could still provide the business with meaningful insights into customer retention patterns and identify key time intervals for intervention. The outcome was a strategic shift in customer engagement, informed by the survival model, which ultimately led to a measurable reduction in churn.”

16. In your view, what is the biggest challenge facing quantitative analysts in the finance sector today?

The finance sector’s constant evolution requires quantitative analysts to be adaptable. Candidates should be ready to discuss how they integrate new data sources and technologies into their financial models.

When responding to this question, candidates should articulate their understanding of the current financial landscape, highlighting specific challenges such as the need for real-time data analysis, cybersecurity threats, or the implications of global economic events. They should also discuss their approach to continuous learning and adaptation, perhaps by mentioning their engagement with new analytical tools, professional development courses, or forums that discuss emerging trends in finance. Demonstrating awareness of these challenges and a proactive approach to overcoming them will show interviewers that the candidate is both informed and forward-thinking.

Example: “ The most pressing challenge for quantitative analysts in the finance sector today is the rapid evolution of machine learning and artificial intelligence technologies. These advancements are constantly reshaping the landscape of data analysis and predictive modeling. The integration of AI into quantitative finance not only requires analysts to maintain a robust understanding of new algorithms and computational methods but also to ensure that these tools are employed without introducing systemic risk. As financial markets become increasingly complex and automated, the potential for AI-driven strategies to create feedback loops or unforeseen market dynamics grows, necessitating a deep understanding of both the financial instruments involved and the underlying technology.

To navigate this challenge, I actively engage with the latest research and developments in AI and machine learning, applying a critical eye to how these innovations can be leveraged responsibly in financial contexts. This involves not only staying abreast of the technical aspects but also understanding the broader economic implications and regulatory frameworks. By maintaining a dialogue with industry peers through forums and professional development opportunities, I ensure that my approach to quantitative analysis is both cutting-edge and grounded in a risk-aware perspective.”

17. How do you balance the need for timely results with the rigor of thorough quantitative analysis?

Delivering accurate data analysis promptly is essential in quantitative roles. Candidates should be prepared to discuss how they balance the need for speed with the need for precision in their work.

When responding to this question, candidates should articulate a structured approach to quantitative tasks that showcases their time management skills. They could mention specific methodologies or tools they use to streamline analysis, such as automating repetitive tasks, employing statistical software, or breaking projects into phases to allow for preliminary insights to be shared in advance of full analysis completion. It’s also important to communicate an understanding of when depth is crucial versus when an executive summary will suffice, and to provide examples from past experiences where this balance was successfully achieved.

Example: “ Balancing timeliness with rigor in quantitative analysis is a matter of prioritizing efficiency without compromising on the integrity of the results. I employ a phased approach, where I initially focus on exploratory data analysis to quickly identify patterns, outliers, and potential areas of interest. This allows for the generation of preliminary insights that can guide subsequent, more detailed investigations. I leverage statistical software and scripting to automate routine data processing tasks, which significantly cuts down on the time required for data cleaning and manipulation.

When deeper analysis is necessary, I judiciously apply sampling techniques or model-based approaches to extrapolate findings without having to crunch every data point, which can be time-consuming. I’m always conscious of the trade-off between accuracy and speed, and I communicate these considerations transparently with stakeholders. For instance, in a past project involving predictive modeling, I used ensemble methods to quickly generate a robust model, then iteratively refined it as time allowed, ensuring that stakeholders had a functional tool at their disposal while I worked on enhancing its precision.”

18. Illustrate how you would conduct a cost-benefit analysis on implementing new technology within an organization.

Conducting a cost-benefit analysis requires strategic thinking. Candidates should be ready to discuss how they evaluate the tangible and intangible factors that contribute to an organization’s ROI.

To respond, you should outline a structured approach, starting with defining the scope of the analysis and identifying all associated costs, such as purchase price, implementation expenses, training, and maintenance. Next, articulate how you would measure the anticipated benefits, which might include increased efficiency, revenue growth, or improved customer satisfaction. Explain how you would consider the time value of money in your analysis, possibly utilizing net present value (NPV) or internal rate of return (IRR) calculations. Then, discuss how you would weigh these factors against each other, possibly including a scenario analysis to account for uncertainty. Finally, describe how you would present your findings, emphasizing clear communication of the potential risks and rewards to stakeholders.

Example: “ In conducting a cost-benefit analysis for new technology implementation, I would begin by meticulously defining the scope and identifying all relevant costs, including upfront capital expenditure, operational costs, and any indirect costs such as potential downtime during the transition. I would then forecast the tangible benefits, such as productivity gains and cost savings, as well as intangible benefits like customer satisfaction and competitive advantage, quantifying these where possible.

To assess the financial viability, I would calculate the net present value (NPV) of the project, ensuring that future cash flows are discounted appropriately to reflect the time value of money. I would also consider the internal rate of return (IRR) to understand the project’s profitability relative to the cost of capital. To address uncertainty, I would perform sensitivity and scenario analyses, varying key assumptions to gauge the robustness of the project under different conditions. My findings would be synthesized into a clear, data-driven recommendation, articulating the strategic rationale and potential impact on the organization’s bottom line, ensuring that decision-makers are fully informed of the risks and potential returns.”

19. What techniques do you apply to forecast demand for a product with little historical data available?

Forecasting demand with limited historical data is challenging. Candidates should be prepared to discuss their approach to making informed predictions in such scenarios.

When responding, emphasize your systematic approach to the problem. You might start by explaining how you gather qualitative insights from market research, expert opinions, and competitive analysis to form initial hypotheses. Then, detail how you use quantitative methods such as time series analysis, regression models, or even newer techniques like machine learning to extrapolate from available data. Highlight any specific tools or software you are proficient with, and describe a past scenario where you successfully forecasted under similar constraints, focusing on the process and the outcome.

Example: “ To forecast demand for a product with limited historical data, I employ a combination of qualitative assessments and exploratory quantitative methods. Initially, I gather insights through market research, including customer surveys, focus groups, and Delphi method sessions with industry experts to establish a foundational understanding of potential demand drivers and market dynamics. Concurrently, I perform a competitive analysis to benchmark against similar products or services, which can provide valuable clues about the market’s response to analogous offerings.

Quantitatively, I lean towards employing Bayesian methods that allow for the incorporation of prior knowledge and expert opinion into the statistical model, which is particularly useful when data is scarce. This approach, coupled with bootstrapping techniques, helps in constructing confidence intervals around forecasts even with small datasets. Additionally, I utilize machine learning algorithms, such as random forests or gradient boosting machines, which can capture complex nonlinear relationships and interactions between variables, even when historical data is not robust. Tools like R or Python, with their extensive libraries for statistical and machine learning methods, are instrumental in this process.

A specific instance where this approach proved successful was when forecasting the demand for a niche technological product entering a new market. By synthesizing market research insights with a Bayesian model that integrated expert assessments, I was able to provide a demand estimate that was later validated to be within 10% of actual sales in the first quarter post-launch. This accuracy was pivotal in optimizing the supply chain and marketing strategy, ultimately contributing to a successful product introduction.”

20. Tell us about a project where you leveraged network analysis to uncover insights into customer behavior.

Network analysis can reveal strategic business insights. Candidates should be ready to discuss how they use network analysis to inform decisions on marketing, product development, and customer engagement.

When responding to this question, candidates should outline a specific project they worked on, detailing the objectives, the nature of the data, the network analysis techniques employed, and the software or tools used. It’s crucial to articulate the unique customer behavior insights gained from the analysis and how these insights led to actionable strategies or decisions within the project. Quantify the impact whenever possible, such as increased customer retention rates or improved product recommendations, to underscore the value of your analytical contributions.

Example: “ In a recent project, the objective was to understand the interconnectedness of customers within a subscription-based service to identify influential users and predict churn. Utilizing a combination of transactional data and social interaction metrics, I constructed a weighted undirected graph where nodes represented customers and edges signified the frequency and quality of interactions between them.

Employing network analysis techniques such as centrality measures and community detection algorithms, I identified clusters of highly interconnected users and pinpointed those with high eigenvector centrality as potential influencers. These insights were instrumental in developing targeted retention strategies. By engaging these key influencers with personalized incentives, we observed a 15% reduction in churn rate within their respective clusters. Additionally, the analysis of structural holes within the network revealed opportunities for cross-selling, leading to a 10% increase in uptake of additional services among identified bridging users. The project leveraged Python’s NetworkX library for the analysis, which facilitated a robust and scalable examination of the network’s properties.”

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Strengths and Weaknesses of Quantitative Interviews

Quantitative interviews offer several benefits. The strengths and weakness of quantitative interviews tend to be couched in comparison to those of administering hard copy questionnaires. For example, response rates tend to be higher with interviews than with mailed questionnaires (Babbie, 2010). 1 That makes sense—don’t you find it easier to say no to a piece of paper than to a person? Quantitative interviews can also help reduce respondent confusion. If a respondent is unsure about the meaning of a question or answer option on a questionnaire, he or she probably won’t have the opportunity to get clarification from the researcher. An interview, on the other hand, gives the researcher an opportunity to clarify or explain any items that may be confusing.

As with every method of data collection we’ve discussed, there are also drawbacks to conducting quantitative interviews. Perhaps the largest, and of most concern to quantitative researchers, is interviewer effect. While questions on hard copy questionnaires may create an impression based on the way they are presented, having a person administer questions introduces a slew of additional variables that might influence a respondent. As I’ve said, consistency is key with quantitative data collection—and human beings are not necessarily known for their consistency. Interviewing respondents is also much more time consuming and expensive than mailing questionnaires. Thus quantitative researchers may opt for written questionnaires over interviews on the grounds that they will be able to reach a large sample at a much lower cost than were they to interact personally with each and every respondent.

KEY TAKEAWAYS

• Unlike qualitative interviews, quantitative interviews usually contain closed-ended questions that are delivered in the same format and same order to every respondent.
• Quantitative interview data are analyzed by assigning a numerical value to participants’ responses.
• While quantitative interviews offer several advantages over self-administered questionnaires such as higher response rates and lower respondent confusion, they have the drawbacks of possible interviewer effect and greater time and expense.
• The General Social Survey (GSS), which we’ve mentioned in previous chapters, is administered via in-person interview, just like quantitative interviewing procedures described here. Read more about the GSS at http://www.norc.uchicago.edu/GSS+Website .
• Take a few of the open-ended questions you created after reading " Qualitative Interview Techniques and Considerations " on qualitative interviewing techniques. See if you can turn them into closed-ended questions.
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3.2.4: Quantitative Interview Techniques and Considerations

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LEARNING OBJECTIVES

• Define and describe standardized interviews.
• Describe how quantitative interviews differ from qualitative interviews.
• Describe the process and some of the drawbacks of telephone interviewing techniques.
• Describe how the analysis of quantitative interview works.
• Identify the strengths and weaknesses of quantitative interviews.

Quantitative interviews are similar to qualitative interviews in that they involve some researcher/respondent interaction. But the process of conducting and analyzing findings from quantitative interviews also differs in several ways from that of qualitative interviews. Each approach also comes with its own unique set of strengths and weaknesses. We’ll explore those differences here.

Conducting Quantitative Interviews

Much of what we learned in the previous chapter on survey research applies to quantitative interviews as well. In fact, quantitative interviews are sometimes referred to as survey interviews because they resemble survey-style question-and-answer formats. They might also be called  standardized interviews . The difference between surveys and standardized interviews is that questions and answer options are read to respondents rather than having respondents complete a questionnaire on their own. As with questionnaires, the questions posed in a standardized interview tend to be closed ended.See  Chapter 3.1 "Survey Research: A Quantitative Technique"  for the definition of closed ended. There are instances in which a quantitative interviewer might pose a few open-ended questions as well. In these cases, the coding process works somewhat differently than coding in-depth interview data. We’ll describe this process in the following subsection.

In quantitative interviews, an  interview schedule  is used to guide the researcher as he or she poses questions and answer options to respondents. An interview schedule is usually more rigid than an interview guide. It contains the list of questions and answer options that the researcher will read to respondents. Whereas qualitative researchers emphasize respondents’ roles in helping to determine how an interview progresses, in a quantitative interview, consistency in the way that questions and answer options are presented is very important. The aim is to pose every question-and-answer option in the very same way to every respondent. This is done to minimize  interviewer effect , or possible changes in the way an interviewee responds based on how or when questions and answer options are presented by the interviewer.

Quantitative interviews may be recorded, but because questions tend to be closed ended, taking notes during the interview is less disruptive than it can be during a qualitative interview. If a quantitative interview contains open-ended questions, however, recording the interview is advised. It may also be helpful to record quantitative interviews if a researcher wishes to assess possible interview effect. Noticeable differences in responses might be more attributable to interviewer effect than to any real respondent differences. Having a recording of the interview can help a researcher make such determinations.

Quantitative interviewers are usually more concerned with gathering data from a large, representative sample. As you might imagine, collecting data from many people via interviews can be quite laborious. Technological advances in telephone interviewing procedures can assist quantitative interviewers in this process. One concern about telephone interviewing is that fewer and fewer people list their telephone numbers these days, but random digit dialing (RDD) takes care of this problem. RDD programs dial randomly generated phone numbers for researchers conducting phone interviews. This means that unlisted numbers are as likely to be included in a sample as listed numbers (though, having used this software for quantitative interviewing myself, I will add that folks with unlisted numbers are not always very pleased to receive calls from unknown researchers). Computer-assisted telephone interviewing (CATI) programs have also been developed to assist quantitative survey researchers. These programs allow an interviewer to enter responses directly into a computer as they are provided, thus saving hours of time that would otherwise have to be spent entering data into an analysis program by hand.

Conducting quantitative interviews over the phone does not come without some drawbacks. Aside from the obvious problem that not everyone has a phone, research shows that phone interviews generate more fence-sitters than in-person interviews (Holbrook, Green, & Krosnick, 2003).Holbrook, A. L., Green, M. C., & Krosnick, J. A. (2003). Telephone versus face-to-face interviewing of national probability samples with long questionnaires: Comparisons of respondent satisficing and social desirability response bias.  Public Opinion Quarterly, 67 , 79–125. Responses to sensitive questions or those that respondents view as invasive are also generally less accurate when data are collected over the phone as compared to when they are collected in person. I can vouch for this latter point from personal experience. While conducting quantitative telephone interviews when I worked at a research firm, it was not terribly uncommon for respondents to tell me that they were green or purple when I asked them to report their racial identity.

Analysis of Quantitative Interview Data

As with the analysis of survey data, analysis of quantitative interview data usually involves coding response options numerically, entering numeric responses into a data analysis computer program, and then running various statistical commands to identify patterns across responses.  Section 3.1.6 "Analysis of Survey Data"  of  Chapter 3.1 "Survey Research: A Quantitative Technique"  describes the coding process for quantitative data. But what happens when quantitative interviews ask open-ended questions? In this case, responses are typically numerically coded, just as closed-ended questions are, but the process is a little more complex than simply giving a “no” a label of 0 and a “yes” a label of 1.

In some cases, quantitatively coding open-ended interview questions may work inductively, as described in  Section 3.2.3 "Analysis of Qualitative Interview Data" . If this is the case, rather than ending with codes, descriptions of codes, and interview excerpts, the researcher will assign a numerical value to codes and may not utilize verbatim excerpts from interviews in later reports of results. Keep in mind, as described in  Chapter 1.1 "Introduction" , that with quantitative methods the aim is to be able to represent and condense data into numbers. The quantitative coding of open-ended interview questions is often a deductive process. The researcher may begin with an idea about likely responses to his or her open-ended questions and assign a numerical value to each likely response. Then the researcher will review participants’ open-ended responses and assign the numerical value that most closely matches the value of his or her expected response.

Strengths and Weaknesses of Quantitative Interviews

Quantitative interviews offer several benefits. The strengths and weakness of quantitative interviews tend to be couched in comparison to those of administering hard copy questionnaires. For example, response rates tend to be higher with interviews than with mailed questionnaires (Babbie, 2010).Babbie, E. (2010).  The practice of social research  (12th ed.). Belmont, CA: Wadsworth. That makes sense—don’t you find it easier to say no to a piece of paper than to a person? Quantitative interviews can also help reduce respondent confusion. If a respondent is unsure about the meaning of a question or answer option on a questionnaire, he or she probably won’t have the opportunity to get clarification from the researcher. An interview, on the other hand, gives the researcher an opportunity to clarify or explain any items that may be confusing.

As with every method of data collection we’ve discussed, there are also drawbacks to conducting quantitative interviews. Perhaps the largest, and of most concern to quantitative researchers, is interviewer effect. While questions on hard copy questionnaires may create an impression based on the way they are presented, having a person administer questions introduces a slew of additional variables that might influence a respondent. As I’ve said, consistency is key with quantitative data collection—and human beings are not necessarily known for their consistency. Interviewing respondents is also much more time consuming and expensive than mailing questionnaires. Thus quantitative researchers may opt for written questionnaires over interviews on the grounds that they will be able to reach a large sample at a much lower cost than were they to interact personally with each and every respondent.

KEY TAKEAWAYS

• Unlike qualitative interviews, quantitative interviews usually contain closed-ended questions that are delivered in the same format and same order to every respondent.
• Quantitative interview data are analyzed by assigning a numerical value to participants’ responses.
• While quantitative interviews offer several advantages over self-administered questionnaires such as higher response rates and lower respondent confusion, they have the drawbacks of possible interviewer effect and greater time and expense.
• The General Social Survey (GSS), which we’ve mentioned in previous chapters, is administered via in-person interview, just like quantitative interviewing procedures described here. Read more about the GSS at  http://www.norc.uchicago.edu/GSS+Website .
• Take a few of the open-ended questions you created after reading  Section 3.2.3 "Qualitative Interview Techniques and Considerations"  on qualitative interviewing techniques. See if you can turn them into closed-ended questions.

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How to Interview a Candidate You Don’t Immediately Click With

Hiring managers often gravitate toward people that are similar to them — but that can be damaging to organizations in the long run.

It’s often easier for interviewers to connect with candidates who have similar backgrounds, pedigrees, credentials, or perspectives. In fact, research shows that implicit bias shapes hiring managers’ perceptions of candidates in profound ways. At the same time, research also attests to the enormous benefits of diversity. As organizations experiment with new ways to attract and retain underrepresented talent, the job interview dynamic merits further attention. Fortunately, there are proven strategies for boosting your chances of “clicking” with an interviewee — and for breathing new life into interviews that appear to be on their last gasps.

When hiring managers “click” with job candidates during interviews, it can feel like magic. When they don’t, it can be tempting to write the candidate off, going through the motions of asking pro forma questions until the allotted time has passed.

• Rae Ringel   is the president of  The Ringel Group , a leadership development consultancy specializing in facilitation, coaching, and training. She is a faculty member at the Georgetown University Institute for Transformational Leadership and founder of the  Executive Certificate in Facilitation  program.

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Quantitative Research Analyst – Micro Intern 2025

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Ab out Five Rings

Five Rings is a proprietary trading firm founded with a vision of combining strategy, innovation and technology to succeed in today’s global markets. With offices in New York, London and Amsterdam, Five Rings trades in various domestic and international markets, both established and esoteric. 

Our team constantly seeks new opportunities, analyzes their risks and rewards, and creates strategies and tools to capitalize on them. We have an open culture and encourage the flow of knowledge and ideas between all areas of the firm.

About the Program

Five Rings offers an intensive 4 week winter internship program that runs from January 6th – January 31st. The program includes immersion in hands-on projects, classroom instruction, in-house built strategy games, and mock trading. Interns will work closely with the research team on development projects. You will have a mentor fully dedicated to you during the internship. 

You will take part in a series of talks to introduce you to key trading concepts. We also offer a variety of activities such as strategic game nights, nights out in NY, dinners, and so much more.  

• Must be working towards a Bachelor’s degree in computer science, economics, mathematics, physics, statistics, or another qualifying field
• Exceptional at math and with other quantitative skills
• Has a hunger to work on some of the most complex mathematical problems we find
• Must enjoy problem solving as this is at the forefront of the role
• Enjoys initiating/acting on a thought or idea as soon as it comes to mind
• Proven track record of success within candidates field of specialty
• Thrive in a highly collaborative environment
• Must have basic programming experience through internships or academia
• Meticulous and detail-oriented
• Eager to learn

Salary: $5,750/week. Additionally, interns receive corporate housing.