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Developmental Research Methods

• Scott A. Miller - University of Florida, USA
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The Fifth Edition of the classic Developmental Research Methods presents an overview of methods to prepare students to carry out, report on, and evaluate research on human development across the lifespan. The book explores every step in the research process, from the initial concept to the final written product, covering conceptual issues of experimental design, as well as the procedural skills necessary to translate design into research. Incorporating new topics, pedagogy, and references, this edition conveys an appreciation of the issues that must be addressed, the decisions that must be made, and the obstacles that must be overcome at every phase in a research project, capturing both the excitement and the challenge of doing quality research on topics that matter.

See what’s new to this edition by selecting the Features tab on this page. Should you need additional information or have questions regarding the HEOA information provided for this title, including what is new to this edition, please email [email protected] . Please include your name, contact information, and the name of the title for which you would like more information. For information on the HEOA, please go to http://ed.gov/policy/highered/leg/hea08/index.html .

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NEW TO THIS EDITION: 

• New topics expose students to current research issues in human development. Topics include: emotional development, bullying, early forms of moral understanding, the “replication crisis” in psychology, the role of gestures in cognitive development, the study of false belief in infancy, the “teenage brain” and its implications for adolescent behavior, the study of the “oldest old,” and the population of centenarians.
• Key Terms lists now appear at the end of each chapter to help students master the vocabulary of research methods.
• New boxes, exercises, glossary items, and tables and figures bring the book completely up to date.
• Approximately 400 new references reflect recent scholarship in the field

KEY FEATURES: 

• Flexible chapters provide instructors the ability to cover material in any order they prefer.
• A student-friendly design and engaging approach  provides extended coverage of especially interesting and important contemporary topics through chapter boxes, tables, figures, and photos.
• Built-in study tools , including exercises, chapter-ending summaries, key terms lists, a glossary, citation of further sources, and relevant websites, help students master key content.

Sample Materials & Chapters

For instructors, select a purchasing option.

This title is also available on SAGE Knowledge , the ultimate social sciences online library. If your library doesn’t have access, ask your librarian to start a trial .

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Developmental Psychology Research Methods

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

Jose Luis Pelaez Inc/Getty Images 

Cross-Sectional Research Methods

Longitudinal research methods, correlational research methods, experimental research methods.

There are many different developmental psychology research methods, including cross-sectional, longitudinal, correlational, and experimental. Each has its own specific advantages and disadvantages. The one that a scientist chooses depends largely on the aim of the study and the nature of the phenomenon being studied.

Research design provides a standardized framework to test a hypothesis and evaluate whether the hypothesis is correct, incorrect, or inconclusive. Even if the hypothesis is untrue, the research can often provide insights that may prove valuable or move research in an entirely new direction.

At a Glance

In order to study developmental psychology, researchers utilize a number of different research methods. Some involve looking at different cross-sections of a population, while others look at how participants change over time. In other cases, researchers look at how whether certain variables appear to have a relationship with one another. In order to determine if there is a cause-and-effect relationship, however, psychologists much conduct experimental research.

Learn more about each of these different types of developmental psychology research methods, including when they are used and what they can reveal about human development.

Cross-sectional research involves looking at different groups of people with specific characteristics.

For example, a researcher might evaluate a group of young adults and compare the corresponding data from a group of older adults.

The benefit of this type of research is that it can be done relatively quickly; the research data is gathered at the same point in time. The disadvantage is that the research aims to make a direct association between a cause and an effect. This is not always so easy. In some cases, there may be confounding factors that contribute to the effect.

To this end, a cross-sectional study can suggest the odds of an effect occurring both in terms of the absolute risk (the odds of something happening over a period of time) and the relative risk (the odds of something happening in one group compared to another).  

Longitudinal research involves studying the same group of individuals over an extended period of time.

Data is collected at the outset of the study and gathered repeatedly through the course of study. In some cases, longitudinal studies can last for several decades or be open-ended. One such example is the Terman Study of the Gifted , which began in the 1920s and followed 1528 children for over 80 years.

The benefit of this longitudinal research is that it allows researchers to look at changes over time. By contrast, one of the obvious disadvantages is cost. Because of the expense of a long-term study, they tend to be confined to a smaller group of subjects or a narrower field of observation.

Challenges of Longitudinal Research

While revealing, longitudinal studies present a few challenges that make them more difficult to use when studying developmental psychology and other topics.

• Longitudinal studies are difficult to apply to a larger population.
• Another problem is that the participants can often drop out mid-study, shrinking the sample size and relative conclusions.
• Moreover, if certain outside forces change during the course of the study (including economics, politics, and science), they can influence the outcomes in a way that significantly skews the results.

For example, in Lewis Terman's longitudinal study, the correlation between IQ and achievement was blunted by such confounding forces as the Great Depression and World War II (which limited educational attainment) and gender politics of the 1940s and 1950s (which limited a woman's professional prospects).

Correlational research aims to determine if one variable has a measurable association with another.

In this type of non-experimental study, researchers look at relationships between the two variables but do not introduce the variables themselves. Instead, they gather and evaluate the available data and offer a statistical conclusion.

For example, the researchers may look at whether academic success in elementary school leads to better-paying jobs in the future. While the researchers can collect and evaluate the data, they do not manipulate any of the variables in question.

A correlational study can be appropriate and helpful if you cannot manipulate a variable because it is impossible, impractical, or unethical.

For example, imagine that a researcher wants to determine if living in a noisy environment makes people less efficient in the workplace. It would be impractical and unreasonable to artificially inflate the noise level in a working environment. Instead, researchers might collect data and then look for correlations between the variables of interest.

Limitations of Correlational Research

Correlational research has its limitations. While it can identify an association, it does not necessarily suggest a cause for the effect. Just because two variables have a relationship does not mean that changes in one will affect a change in the other.

Unlike correlational research, experimentation involves both the manipulation and measurement of variables . This model of research is the most scientifically conclusive and commonly used in medicine, chemistry, psychology, biology, and sociology.

Experimental research uses manipulation to understand cause and effect in a sampling of subjects. The sample is comprised of two groups: an experimental group in whom the variable (such as a drug or treatment) is introduced and a control group in whom the variable is not introduced.

Deciding the sample groups can be done in a number of ways:

• Population sampling, in which the subjects represent a specific population
• Random selection , in which subjects are chosen randomly to see if the effects of the variable are consistently achieved

Challenges in Experimental Resarch

While the statistical value of an experimental study is robust, it may be affected by confirmation bias . This is when the investigator's desire to publish or achieve an unambiguous result can skew the interpretations, leading to a false-positive conclusion.

One way to avoid this is to conduct a double-blind study in which neither the participants nor researchers are aware of which group is the control. A double-blind randomized controlled trial (RCT) is considered the gold standard of research.

What This Means For You

There are many different types of research methods that scientists use to study developmental psychology and other areas. Knowing more about how each of these methods works can give you a better understanding of what the findings of psychological research might mean for you.

Capili B. Cross-sectional studies .  Am J Nurs . 2021;121(10):59-62. doi:10.1097/01.NAJ.0000794280.73744.fe

Kesmodel US. Cross-sectional studies - what are they good for? .  Acta Obstet Gynecol Scand . 2018;97(4):388–393. doi:10.1111/aogs.13331

Noordzij M, van Diepen M, Caskey FC, Jager KJ. Relative risk versus absolute risk: One cannot be interpreted without the other . Nephrology Dialysis Transplantation. 2017;32(S2):ii13-ii18. doi:10.1093/ndt/gfw465

Kell HJ, Wai J. Terman Study of the Gifted . In: Frey B, ed.  The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation . Vol. 4. Thousand Oaks, CA: SAGE Publications, Inc.; 2018. doi:10.4135/9781506326139.n691

Curtis EA, Comiskey C, Dempsey O. Importance and use of correlational research .  Nurse Res . 2016;23(6):20–25. doi:10.7748/nr.2016.e1382

Misra S.  Randomized double blind placebo control studies, the "Gold Standard" in intervention based studies .  Indian J Sex Transm Dis AIDS . 2012;33(2):131-4. doi:10.4103/2589-0557.102130

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Developmental Research Designs

Margaret Clark-Plaskie; Lumen Learning; Angela Lukowski; Helen Milojevich; and Diana Lang

• Compare advantages and disadvantages of developmental research designs (cross-sectional, longitudinal, and sequential)
• Describe challenges associated with conducting research in lifespan development

Now you know about some tools used to conduct research about human development. Remember,  research methods  are tools that are used to collect information. But it is easy to confuse research methods and research design. Research design is the strategy or blueprint for deciding how to collect and analyze information. Research design dictates which methods are used and how. Developmental research designs are techniques used particularly in lifespan development research. When we are trying to describe development and change, the research designs become especially important because we are interested in what changes and what stays the same with age. These techniques try to examine how age, cohort, gender, and social class impact development. [1]

Cross-sectional designs

The majority of developmental studies use cross-sectional designs because they are less time-consuming and less expensive than other developmental designs. Cross-sectional research designs are used to examine behavior in participants of different ages who are tested at the same point in time (Figure 1). Let’s suppose that researchers are interested in the relationship between intelligence and aging. They might have a hypothesis (an educated guess, based on theory or observations) that intelligence declines as people get older. The researchers might choose to give a certain intelligence test to individuals who are 20 years old, individuals who are 50 years old, and individuals who are 80 years old at the same time and compare the data from each age group. This research is cross-sectional in design because the researchers plan to examine the intelligence scores of individuals of different ages within the same study at the same time; they are taking a “cross-section” of people at one point in time. Let’s say that the comparisons find that the 80-year-old adults score lower on the intelligence test than the 50-year-old adults, and the 50-year-old adults score lower on the intelligence test than the 20-year-old adults. Based on these data, the researchers might conclude that individuals become less intelligent as they get older. Would that be a valid (accurate) interpretation of the results?

No, that would not be a valid conclusion because the researchers did not follow individuals as they aged from 20 to 50 to 80 years old. One of the primary limitations of cross-sectional research is that the results yield information about age differences  not necessarily changes with age or over time. That is, although the study described above can show that in 2010, the 80-year-olds scored lower on the intelligence test than the 50-year-olds, and the 50-year-olds scored lower on the intelligence test than the 20-year-olds, the data used to come up with this conclusion were collected from different individuals (or groups of individuals). It could be, for instance, that when these 20-year-olds get older (50 and eventually 80), they will still score just as high on the intelligence test as they did at age 20. In a similar way, maybe the 80-year-olds would have scored relatively low on the intelligence test even at ages 50 and 20; the researchers don’t know for certain because they did not follow the same individuals as they got older.

It is also possible that the differences found between the age groups are not due to age, per se, but due to cohort effects. The 80-year-olds in this 2010 research grew up during a particular time and experienced certain events as a group. They were born in 1930 and are part of the Traditional or Silent Generation. The 50-year-olds were born in 1960 and are members of the Baby Boomer cohort. The 20-year-olds were born in 1990 and are part of the Millennial or Gen Y Generation. What kinds of things did each of these cohorts experience that the others did not experience or at least not in the same ways?

You may have come up with many differences between these cohorts’ experiences, such as living through certain wars, political and social movements, economic conditions, advances in technology, changes in health and nutrition standards, etc. There may be particular cohort differences that could especially influence their performance on intelligence tests, such as education level and use of computers. That is, many of those born in 1930 probably did not complete high school; those born in 1960 may have high school degrees, on average, but the majority did not attain college degrees; the young adults are probably current college students. And this is not even considering additional factors such as gender, race, or socioeconomic status. The young adults are used to taking tests on computers, but the members of the other two cohorts did not grow up with computers and may not be as comfortable if the intelligence test is administered on computers. These factors could have been a factor in the research results.

Another disadvantage of cross-sectional research is that it is limited to one time of measurement. Data are collected at one point in time and it’s possible that something could have happened in that year in history that affected all of the participants, although possibly each cohort may have been affected differently. Just think about the mindsets of participants in research that was conducted in the United States right after the terrorist attacks on September 11, 2001.

Longitudinal research designs

Longitudinal   research involves beginning with a group of people who may be of the same age and background (cohort) and measuring them repeatedly over a long period of time (Figure 2 & 3). One of the benefits of this type of research is that people can be followed through time and be compared with themselves when they were younger; therefore changes with age over time are measured. What would be the advantages and disadvantages of longitudinal research? Problems with this type of research include being expensive, taking a long time, and subjects dropping out over time. Think about the film, 63 Up , part of the Up Series mentioned earlier, which is an example of following individuals over time. In the videos, filmed every seven years, you see how people change physically, emotionally, and socially through time; and some remain the same in certain ways, too. But many of the participants really disliked being part of the project and repeatedly threatened to quit; one disappeared for several years; another died before her 63rd year. Would you want to be interviewed every seven years? Would you want to have it made public for all to watch?   

Longitudinal research designs are used to examine behavior in the same individuals over time. For instance, with our example of studying intelligence and aging, a researcher might conduct a longitudinal study to examine whether 20-year-olds become less intelligent with age over time. To this end, a researcher might give an intelligence test to individuals when they are 20 years old, again when they are 50 years old, and then again when they are 80 years old. This study is longitudinal in nature because the researcher plans to study the same individuals as they age. Based on these data, the pattern of intelligence and age might look different than from the cross-sectional research; it might be found that participants’ intelligence scores are higher at age 50 than at age 20 and then remain stable or decline a little by age 80. How can that be when cross-sectional research revealed declines in intelligence with age?

Since longitudinal research happens over a period of time (which could be short term, as in months, but is often longer, as in years), there is a risk of attrition. Attrition occurs when participants fail to complete all portions of a study. Participants may move, change their phone numbers, die, or simply become disinterested in participating over time. Researchers should account for the possibility of attrition by enrolling a larger sample into their study initially, as some participants will likely drop out over time. There is also something known as  selective attrition— this means that certain groups of individuals may tend to drop out. It is often the least healthy, least educated, and lower socioeconomic participants who tend to drop out over time. That means that the remaining participants may no longer be representative of the whole population, as they are, in general, healthier, better educated, and have more money. This could be a factor in why our hypothetical research found a more optimistic picture of intelligence and aging as the years went by. What can researchers do about selective attrition? At each time of testing, they could randomly recruit more participants from the same cohort as the original members, to replace those who have dropped out.

The results from longitudinal studies may also be impacted by repeated assessments. Consider how well you would do on a math test if you were given the exact same exam every day for a week. Your performance would likely improve over time, not necessarily because you developed better math abilities, but because you were continuously practicing the same math problems. This phenomenon is known as a practice effect. Practice effects occur when participants become better at a task over time because they have done it again and again (not due to natural psychological development). So our participants may have become familiar with the intelligence test each time (and with the computerized testing administration).

Another limitation of longitudinal research is that the data are limited to only one cohort. As an example, think about how comfortable the participants in the 2010 cohort of 20-year-olds are with computers. Since only one cohort is being studied, there is no way to know if findings would be different from other cohorts. In addition, changes that are found as individuals age over time could be due to age or to time of measurement effects. That is, the participants are tested at different periods in history, so the variables of age and time of measurement could be confounded (mixed up). For example, what if there is a major shift in workplace training and education between 2020 and 2040 and many of the participants experience a lot more formal education in adulthood, which positively impacts their intelligence scores in 2040? Researchers wouldn’t know if the intelligence scores increased due to growing older or due to a more educated workforce over time between measurements.

Sequential research designs

Sequential research designs include elements of both longitudinal and cross-sectional research designs. Similar to longitudinal designs, sequential research features participants who are followed over time; similar to cross-sectional designs, sequential research includes participants of different ages. This research design is also distinct from those that have been discussed previously in that individuals of different ages are enrolled into a study at various points in time to examine age-related changes, development within the same individuals as they age, and to account for the possibility of cohort and/or time of measurement effects. In 1965, Schaie [2] (a leading theorist and researcher on intelligence and aging), described particular sequential designs: cross-sequential, cohort sequential, and time-sequential. The differences between them depended on which variables were focused on for analyses of the data (data could be viewed in terms of multiple cross-sectional designs or multiple longitudinal designs or multiple cohort designs). Ideally, by comparing results from the different types of analyses, the effects of age, cohort, and time in history could be separated out.

Consider, once again, our example of intelligence and aging. In a study with a sequential design, a researcher might recruit three separate groups of participants (Groups A, B, and C). Group A would be recruited when they are 20 years old in 2010 and would be tested again when they are 50 and 80 years old in 2040 and 2070, respectively (similar in design to the longitudinal study described previously). Group B would be recruited when they are 20 years old in 2040 and would be tested again when they are 50 years old in 2070. Group C would be recruited when they are 20 years old in 2070 and so on (Figure 4).

Studies with sequential designs are powerful because they allow for both longitudinal and cross-sectional comparisons—changes and/or stability with age over time can be measured and compared with differences between age and cohort groups. This research design also allows for the examination of cohort and time of measurement effects. For example, the researcher could examine the intelligence scores of 20-year-olds in different times in history and different cohorts (follow the yellow diagonal lines in figure 3). This might be examined by researchers who are interested in sociocultural and historical changes (because we know that lifespan development is multidisciplinary). One way of looking at the usefulness of the various developmental research designs was described by Schaie and Baltes [3] : cross-sectional and longitudinal designs might reveal change patterns while sequential designs might identify developmental origins for the observed change patterns.

Since they include elements of longitudinal and cross-sectional designs, sequential research has many of the same strengths and limitations as these other approaches. For example, sequential work may require less time and effort than longitudinal research (if data are collected more frequently than over the 30-year spans in our example) but more time and effort than cross-sectional research. Although practice effects may be an issue if participants are asked to complete the same tasks or assessments over time, attrition may be less problematic than what is commonly experienced in longitudinal research since participants may not have to remain involved in the study for such a long period of time.

When considering the best research design to use in their research, scientists think about their main research question and the best way to come up with an answer. A table of advantages and disadvantages for each of the described research designs is provided here to help you as you consider what sorts of studies would be best conducted using each of these different approaches.

Challenges Associated with Conducting Developmental Research

The previous sections describe research tools to assess development across the lifespan, as well as the ways that research designs can be used to track age-related changes and development over time. Before you begin conducting developmental research, however, you must also be aware that testing individuals of certain ages (such as infants and children) or making comparisons across ages (such as children compared to teens) comes with its own unique set of challenges. In the final section of this module, let’s look at some of the main issues that are encountered when conducting developmental research, namely ethical concerns, recruitment issues, and participant attrition.

Ethical Concerns

As a student of the social sciences, you may already know that Institutional Review Boards (IRBs) must review and approve all research projects that are conducted at universities, hospitals, and other institutions (each broad discipline or field, such as psychology or social work, often has its own code of ethics that must also be followed, regardless of institutional affiliation). An IRB is typically a panel of experts who read and evaluate proposals for research. IRB members want to ensure that the proposed research will be carried out ethically and that the potential benefits of the research outweigh the risks and potential harm (psychological as well as physical harm) for participants.

What you may not know though, is that the IRB considers some groups of participants to be more vulnerable or at-risk than others. Whereas university students are generally not viewed as vulnerable or at-risk, infants and young children commonly fall into this category. What makes infants and young children more vulnerable during research than young adults? One reason infants and young children are perceived as being at increased risk is due to their limited cognitive capabilities, which makes them unable to state their willingness to participate in research or tell researchers when they would like to drop out of a study. For these reasons, infants and young children require special accommodations as they participate in the research process. Similar issues and accommodations would apply to adults who are deemed to be of limited cognitive capabilities.

When thinking about special accommodations in developmental research, consider the informed consent process. If you have ever participated in scientific research, you may know through your own experience that adults commonly sign an informed consent statement (a contract stating that they agree to participate in research) after learning about a study. As part of this process, participants are informed of the procedures to be used in the research, along with any expected risks or benefits. Infants and young children cannot verbally indicate their willingness to participate, much less understand the balance of potential risks and benefits. As such, researchers are oftentimes required to obtain written informed consent from the parent or legal guardian of the child participant, an adult who is almost always present as the study is conducted. In fact, children are not asked to indicate whether they would like to be involved in a study at all (a process known as assent) until they are approximately seven years old. Because infants and young children cannot easily indicate if they would like to discontinue their participation in a study, researchers must be sensitive to changes in the state of the participant (determining whether a child is too tired or upset to continue) as well as to parent desires (in some cases, parents might want to discontinue their involvement in the research). As in adult studies, researchers must always strive to protect the rights and well-being of the minor participants and their parents when conducting developmental research.

This video from the US Department of Health and Human Services provides an overview of the Institutional Review Board process.

You can view the transcript for “How IRBs Protect Human Research Participants” here (opens in new window) .

Recruitment

An additional challenge in developmental science is participant recruitment. Recruiting university students to participate in adult studies is typically easy. Many colleges and universities offer extra credit for participation in research and have locations such as bulletin boards and school newspapers where research can be advertised. Unfortunately, young children cannot be recruited by making announcements in Introduction to Psychology courses, by posting ads on campuses, or through online platforms such as Amazon Mechanical Turk. Given these limitations, how do researchers go about finding infants and young children to be in their studies?

The answer to this question varies along multiple dimensions. Researchers must consider the number of participants they need and the financial resources available to them, among other things. Location may also be an important consideration. Researchers who need large numbers of infants and children may attempt to recruit them by obtaining infant birth records from the state, county, or province in which they reside. Some areas make this information publicly available for free, whereas birth records must be purchased in other areas (and in some locations birth records may be entirely unavailable as a recruitment tool). If birth records are available, researchers can use the obtained information to call families by phone or mail them letters describing possible research opportunities. All is not lost if this recruitment strategy is unavailable, however. Researchers can choose to pay a recruitment agency to contact and recruit families for them. Although these methods tend to be quick and effective, they can also be quite expensive. More economical recruitment options include posting advertisements and fliers in locations frequented by families, such as mommy-and-me classes, local malls, and preschools or daycare centers. Researchers can also utilize online social media outlets like Facebook, which allows users to post recruitment advertisements for a small fee. Of course, each of these different recruitment techniques requires IRB approval. And if children are recruited and/or tested in school settings, permission would need to be obtained ahead of time from teachers, schools, and school districts (as well as informed consent from parents or guardians).

And what about the recruitment of adults? While it is easy to recruit young college students to participate in research, some would argue that it is too easy and that college students are samples of convenience. They are not randomly selected from the wider population, and they may not represent all young adults in our society (this was particularly true in the past with certain cohorts, as college students tended to be mainly white males of high socioeconomic status). In fact, in the early research on aging, this type of convenience sample was compared with another type of convenience sample—young college students tended to be compared with residents of nursing homes! Fortunately, it didn’t take long for researchers to realize that older adults in nursing homes are not representative of the older population; they tend to be the oldest and sickest (physically and/or psychologically). Those initial studies probably painted an overly negative view of aging, as young adults in college were being compared to older adults who were not healthy, had not been in school nor taken tests in many decades, and probably did not graduate high school, let alone college. As we can see, recruitment and random sampling can be significant issues in research with adults, as well as infants and children. For instance, how and where would you recruit middle-aged adults to participate in your research?

Another important consideration when conducting research with infants and young children is attrition . Although attrition is quite common in longitudinal research in particular (see the previous section on longitudinal designs for an example of high attrition rates and selective attrition in lifespan developmental research), it is also problematic in developmental science more generally, as studies with infants and young children tend to have higher attrition rates than studies with adults. For example, high attrition rates in ERP (event-related potential, which is a technique to understand brain function) studies oftentimes result from the demands of the task: infants are required to sit still and have a tight, wet cap placed on their heads before watching still photographs on a computer screen in a dark, quiet room (Figure 5).

In other cases, attrition may be due to motivation (or a lack thereof). Whereas adults may be motivated to participate in research in order to receive money or extra course credit, infants and young children are not as easily enticed. In addition, infants and young children are more likely to tire easily, become fussy, and lose interest in the study procedures than are adults. For these reasons, research studies should be designed to be as short as possible – it is likely better to break up a large study into multiple short sessions rather than cram all of the tasks into one long visit to the lab. Researchers should also allow time for breaks in their study protocols so that infants can rest or have snacks as needed. Happy, comfortable participants provide the best data.

Conclusions

Lifespan development is a fascinating field of study – but care must be taken to ensure that researchers use appropriate methods to examine human behavior, use the correct experimental design to answer their questions, and be aware of the special challenges that are part-and-parcel of developmental research. After reading this module, you should have a solid understanding of these various issues and be ready to think more critically about research questions that interest you. For example, what types of questions do you have about lifespan development? What types of research would you like to conduct? Many interesting questions remain to be examined by future generations of developmental scientists – maybe you will make one of the next big discoveries!

• attrition : occurs when participants fail to complete all portions of a study
• cross-sectional research : used to examine behavior in participants of different ages who are tested at the same point in time; may confound age and cohort differences
• i nformed consent : a process of informing a research participant what to expect during a study, any risks involved, and the implications of the research, and then obtaining the person’s agreement to participate
• Institutional Review Boards (IRBs) : a panel of experts who review research proposals for any research to be conducted in association with the institution (for example, a university)
• longitudinal research : studying a group of people who may be of the same age and background (cohort), and measuring them repeatedly over a long period of time; may confound age and time of measurement effects
• research design : the strategy or blueprint for deciding how to collect and analyze information; dictates which methods are used and how
• selective attrition : certain groups of individuals may tend to drop out more frequently resulting in the remaining participants no longer being representative of the whole population
• sequential research design : combines aspects of cross-sectional and longitudinal designs, but also adding new cohorts at different times of measurement; allows for analyses to consider effects of age, cohort, time of measurement, and socio-historical change
• This chapter was adapted from Lumen Learning's Lifespan Development , created by Margaret Clark-Plaskie for Lumen Learning and adapted from Research Methods in Developmental Psychology by Angela Lukowski and Helen Milojevich for Noba Psychology, available under a Creative Commons NonCommercial Sharealike Attribution license . ↵
• Schaie, K. W. (1965). A general model for the study of developmental problems. Psychological Bulletin, 64 (2), 92-107. ↵
• Schaie, K.W. & Baltes, B.P. (1975). On sequential strategies in developmental research: Description or Explanation. Human Development, 18,  384-390. ↵

Developmental Research Designs Copyright © 2022 by Margaret Clark-Plaskie; Lumen Learning; Angela Lukowski; Helen Milojevich; and Diana Lang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

42. Developmental Research

The field of instructional technology has traditionally involved a unique blend of theory and practice. This blend is most obvious in developmental research, which involves the production of knowledge with the ultimate aim of improving the processes of instructional design, development, and evaluation. It is based on either situation-specific problem solving or generalized inquiry procedures. Developmental research, as opposed to simple instructional development, has been defined as "the systematic study of designing, developing and evaluating instructional programs, processes, and products that must meet the criteria of internal consistency and effectiveness" (Seels & Richey, 1994, p. 127). In its simplest form, developmental research could be either:

• A situation in which someone is performing instructional design, development, or evaluation activities and studying the process at the same time
• The study of the impact of someone else's instructional design and development efforts
• The study of the instructional design, development, and evaluation process as a whole, or of particular process components

In each case, the distinction is made between performing a process and studying that process. Reports of developmental research may take the form of a case study with retrospective analysis, an evaluation report, or even that of a typical experimental research report.

The purposes of this chapter(see footnote ) are to:

• Explore the nature and background of developmental research
• Describe the major types of developmental research by examining a range of representative projects
• Analyze the methodological approaches used in the various types of developmental research
• Describe the issues, findings, and trends in recent developmental research
• Discuss the future of this type of research in our field

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6 Developmental Research Designs

These designs examine what changes and what stays the same in a human life. Chronological age , cohort membership , and time of measurement are the basic elements of research designs looking at development. The frustrating thing about doing this kind of research is that you only can vary two of these three elements at a time. The two that you choose will determine the third element.  Therefore no single study can definitively tell you about how human beings develop. However, combining results of multiple  studies and using more complex designs, such as cross-sequential designs, can help us get closer to the truth.

Cross-sectional research involves beginning with a sample that represents a cross-section of the population. Respondents who vary in age, gender, ethnicity, and social class might be asked to complete a survey about television program preferences or attitudes toward the use of the Internet. The attitudes of males and females could then be compared as could attitudes based on age. In cross-sectional research, respondents are measured only once. This method is much less expensive than longitudinal research but does not allow the researcher to distinguish between the impact of age and the cohort effect. Different attitudes about the Internet, for example, might not be altered by a person’s biological age as much as their life experiences as members of a cohort.

Longitudinal research involves beginning with a group of people who may be of the same age and background, and measuring them repeatedly over a long period of time. One of the benefits of this type of research is that people can be followed through time and be compared with them when they were younger. A problem with this type of research is that it is very expensive and subjects may drop out over time. (The film 49 Up is a example of following individuals over time. You see how people change physically, emotionally, and socially through time.) What would be the drawbacks of being in a longitudinal study? What about 49 Up? Would you want to be filmed every 7 years? What would be the advantages and disadvantages? Can you imagine why some would continue and others drop out of the project?

Cross-sequential research involves combining aspects of the previous two techniques; beginning with a cross-sectional sample and measuring them through time. This is the perfect model for looking at age, gender, social class, and ethnicity. But here the drawbacks of high costs and attrition are here as well.

the amount of time elapsed since an individual’s birth, typically expressed in terms of months and years.

a group of individuals who share a similar characteristic or experience. The term usually refers to an age (or birth) cohort, that is, a group of individuals who are born in the same year and thus of similar age.

the moment in time when the participants' responses are recorded.

a research design in which individuals, typically of different ages or developmental levels, are compared at a single point in time. An example is a study that involves a direct comparison of 50-year-olds with 80-year-olds. Given its snapshot nature, however, it is difficult to determine causal relationships using a cross-sectional design. Moreover, a cross-sectional study is not suitable for measuring changes over time, for which a longitudinal design is required.

the study of a variable or group of variables in the same cases or participants over a period of time, sometimes several years. An example of a longitudinal design is a multiyear comparative study of the same children in an urban and a suburban school to record their cognitive development in depth. A longitudinal study that evaluates a group of randomly chosen individuals is referred to as a panel study, whereas a longitudinal study that evaluates a group of individuals possessing some common characteristic (usually age) is referred to as a cohort study.

a study in which two or more groups of individuals of different ages are directly compared over a period of time. It is thus a combination of a cross-sectional design and a longitudinal design. For example, an investigator using a cross-sequential design to evaluate children’s mathematical skills might measure a group of 5-year-olds and a group of 10-year-olds at the beginning of the research and then subsequently reassess the same children every 6 months for the next 5 years.

Always Developing Copyright © 2019 by Anne Baird is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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• Published: 15 March 2024

Diverse adolescents’ transcendent thinking predicts young adult psychosocial outcomes via brain network development

• Rebecca J. M. Gotlieb 1   na1 ,
• Xiao-Fei Yang 2   na1 &
• Mary Helen Immordino-Yang 2 , 3  

Scientific Reports volume  14 , Article number:  6254 ( 2024 ) Cite this article

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• Human behaviour
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• Personality
• Social behaviour

Developmental scientists have long described mid-adolescents’ emerging capacities to make deep meaning about the social world and self, here called transcendent thinking, as a hallmark developmental stage. In this 5-years longitudinal study, sixty-five 14–18 years-old youths’ proclivities to grapple psychologically with the ethical, systems-level and personal implications of social stories, predicted future increases in the coordination of two key brain networks: the default-mode network, involved in reflective, autobiographical and free-form thinking, and the executive control network, involved in effortful, focused thinking; findings were independent of IQ, ethnicity, and socioeconomic background. This neural development predicted late-adolescent identity development, which predicted young-adult self-liking and relationship satisfaction, in a developmental cascade. The findings reveal a novel predictor of mid-adolescents’ neural development, and suggest the importance of attending to adolescents’ proclivities to engage agentically with complex perspectives and emotions on the social and personal relevance of issues, such as through civically minded educational approaches.

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Introduction

Adolescence is a period of marked cognitive, emotional and psychosocial growth 1 , as well as a sensitive period for neurological development 2 , the second such period after infancy. It is characterized by sensitivity to the social context and by the emergence of increasingly sophisticated abilities to interpret the social world and react with complex emotions to its happenings 3 , 4 . By middle adolescence, from approximately 14–18 years of age, youth develop the capacity for “transcendent” thinking. That is, mid-adolescents are disposed, and often motivated, to enrich their concrete, empathic, and context-specific interpretations with abstract, systems-level considerations that transcend the current situation 5 , 6 , 7 , 8 , 9 . They invoke broader perspectives on themselves, other people, and social systems, and draw on cultural values and associated emotions to infer social and ethical implications and build deeper understandings 10 , 11 . Moving into the later teenage years, transcendent thinking supports late-adolescents’ identity development, the process of building self-definitions rooted in reflections on experiences, hopes, relationships, values, and beliefs rather than on happenstance. As such, transcendent thinking may contribute to stronger identity achievement and less identity diffusion 12 , 13 , 14 . The identity development process can support a healthy transition to young adulthood 15 , 16 , 17 , in the early twenties, with emotionally fulfilling and stable relationships, a positive sense of self and life purpose 18 , and productive, ethical use of educational and work opportunities 19 , 20 . Especially among ethnically diverse youth, and youth from families of low-socioeconomic circumstances, transcendent social thinking and identity are important developmental assets, given the likelihood these youth will face complex circumstances and social challenges 11 , 21 .

Research in developmental science and education has long documented the academic and social benefits of supporting adolescents in building intellectual agency and developmentally appropriate capacities for thinking about complex social, civic, and academic disciplinary content 11 , 19 , 22 , 23 . Curiously, though, the affordances of mid-adolescents’ transcendent thinking for brain development have not been investigated. Neural maturation across mid-adolescence largely involves increasingly efficient communication among characteristic networks contributing to a range of developing psychological capacities 24 , 25 , 26 . This increasing efficiency is measurable in neural dynamics, i.e., in the real-time correlation between different neural networks’ activity fluctuations, even as individuals rest 24 , 27 . The change over time in the strength of these correlations, therefore, can be used as a metric of functional neural development. Across adolescence, there are considerable individual differences in these metrics. As these differences correlate with psychosocial functioning and mental health 25 , 27 , 28 , 29 , 30 , it is important to understand their origins 31 .

A previous study of ours revealed that mid-adolescents’ transcendent construals of social stories were associated with increased default mode network (DMN) activity, especially when the adolescent reported feeling strongly emotionally engaged with the story, and with decreased executive control network (ECN) activity 32 . In addition, the association between DMN activity and transcendence was strengthened by brief ECN activity early in the trial. These complex findings suggested that adolescents’ transcendent construals involve coordinated activity of the DMN, which supports internally generated reflections and prospections 33 , 34 , 35 , and the ECN, which supports goal-directed thinking and focused attention 36 , 37 . This interpretation is consistent with previous theoretical and empirical work documenting these networks’ coordinated involvement in many forms of creative, episodic, social-emotional and generative thinking 38 , 39 , 40 , 41 , 42 , and with research documenting links between maturation of these networks and social-emotional, cognitive and executive functioning and mental health 43 , 44 , 45 , 46 . Might the developmentally characteristic recruitment of transcendent thinking across mid-adolescence contribute to organizing connectivity between these two networks in ways that support long-term psychosocial growth and well-being?

Brain maturation during mid-adolescence is thought to reflect, in part, social and cognitive experiences, such as effects of socioeconomic and cultural backgrounds, education, and peer influences 4 , 47 , 48 . However, whereas exposure to social circumstances is an important source of experience, equally important may be adolescents’ growing propensities to grapple psychologically with what they witness, and make meaning 49 . Although IQ and demographic variables such as socioeconomic status (SES) have been associated with neurodevelopmental trajectories across childhood and early adolescence 46 , 50 , 51 , it is possible that their effect on longitudinal change across mid-adolescence may attenuate. Potentially, proclivities to expend effort on complex thinking may come to play a more prominent role.

To begin to investigate how mid-adolescents’ propensities for transcendent social-emotional thinking may predict subsequent brain development, with potential psychosocial implications in young adulthood, we launched a longitudinal 5-years study involving sixty-five 14–18 years-old youth of color from low-income urban communities. There is an urgent need in the psychological and brain sciences to study populations that have not traditionally been involved in research, i.e., non-White and low-to-mid SES samples, and in particular to focus on these populations’ normative development, rather than solely on deficits and risks 52 . At the time of recruitment, participants were healthy mid-adolescents from stable families, passing all classes at school, and not under disciplinary action. Our sample is diverse; participants speak English and were attending U.S. public high schools, but their parents had immigrated to the United States from thirteen different countries, primarily in East Asia and Latin America. Asian and Latinx youth are substantial and growing segments of the U.S. population. Our sample also is reflective of the variation in parental education levels and financial circumstances that exists within low-SES communities.

Participants explained their reactions to compelling mini documentaries in 2-h private interviews that were videotaped, transcribed, and coded for transcendent construals. Our interview provided participants with interesting, emotionally compelling true stories and a socially supportive situation conducive to reflection, and then allowed them to respond freely. Participants also underwent resting-state fMRI at the beginning of the study and after 2 years, to capture the longitudinal change in DMN and ECN internetwork connectivity. Identity development was surveyed after 1.5 more years, in late adolescence. In young adulthood, 5 years after initial data collection, when participants were in their early twenties, participants rated their satisfaction with self, relationships, and school to capture psychosocial well-being. We hypothesized that mid-adolescents’ construction of transcendent construals in the interview at the start of the study would predict increases in the DMN and ECN networks’ interconnectivity over the subsequent 2 years, regardless of the initial level of interconnectivity between these networks. We further hypothesized a sequenced developmental cascade in which the increases in these networks’ interconnectivity would in turn positively predict late-adolescent identity development, which would predict self and relationship satisfaction in young adulthood. To differentiate effects of transcendent thinking on brain network development from effects of age, family financial status and parents’ education levels (measures of SES), intelligence as measured by IQ, and other personal characteristics and demographic variables, we also measured, analyzed, and then controlled for these factors.

Every participant produced transcendent construals over the course of the interview at the start of the study (M = 25.14, SD  = 14.00, range 2–64). Transcendent construal scores were not significantly related to IQ ( r [59.9] = 0.23, p  = 0.07, 95% CI [− 0.02, 0.45]), age ( r [63] = 0.22, p  = 0.08, 95% CI [− 0.02, 0.44]), or other demographic covariates (SES [family income/needs ratio and parents’ years of education], sex, ethnic background; all p’s  > 0.44).

Transcendent construal scores predict increases in internetwork connectivity over time

Consistent with our hypothesis, adolescents’ transcendent construal scores predicted the increase in connectivity between the DMN and the left ECN components across the 2-years interval following the interview, controlling for differences in head motion between the two neuroimaging data collections and time between data collections ( b  = 0.007, SE  = 0.003, t [55.9] = 2.70, p  = 0.009; bootstrapped 95% CI [0.002, 0.012]). Results hold in a model additionally controlling for age, sex, IQ, SES and starting level of connectivity between these components ( b  = 0.005, SE  = 0.002, t [49.2] = 2.26, p  = 0.03; bootstrapped 95% CI [0.0005, 0.0094]). The effect of transcendent construals on growth in internetwork connectivity was not significantly moderated by age, sex, IQ or SES (all p’s  > 0.44).

Ethnic background

Given current discussions about the generalizability of psychological effects across ethnically diverse samples 52 , 53 , the effect of transcendent construals on longitudinal change in DMN and left ECN interconnectivity was examined in a model with participants divided into two broad ethnic groups: East-Asian descent ( n  = 29) and Latinx/Afro-Latinx descent ( n  = 36). The effect of transcendent construals holds ( p  = 0.008). Ethnic group did not moderate the effect of transcendent construals on change in internetwork connectivity ( p  = 0.71).

Results were lateralized to the left ECN. Change in connectivity between the DMN and right ECN components across the 2-years interval was not predicted by transcendent construal scores, controlling for difference in head motion between the two data collections and time between data collections, p  = 0.82.

The sequenced developmental cascade from transcendent construals in mid-adolescence to young adult life satisfaction

Psychosocial outcomes varied across participants (identity development, a composite measure of identity achievement and diffusion: M  = 3.69 out of 5, SE  = 0.09; life satisfaction, a composite measure of satisfaction with self, various social relationships, and school [for the 49 still attending school]: M z-score = 0.05, SE  = 0.11).

Developmental measures were chronologically ordered, and a series of regression models revealed significant effects from one to the next. A path analysis then revealed that the complete path is significant, while alternative paths that omit either or both of the intermediate measures are not. The findings together suggest a developmental cascade; see Fig.  1 . All models control for differences in head motion between the two scans and time between scans.

The longitudinal path from transcendent construals in mid-adolescence to life satisfaction in young adulthood, through 2-year change in resting-state internetwork connectivity (Δ Connectivity), and identity development in late adolescence. Consistent with a developmental cascade, only the complete path (black arrows) is significant; see inset. Alternative paths that omit either (blue arrows, red arrows) or both (purple arrow) of the intermediate measures are not significant. Regression coefficients are depicted for the complete path, with standard errors in parentheses. DMN, default mode network; ECN, executive control network; ** p  < 0.01, * p  < 0.05, slashes signify a non-significant relationship; CI, confidence interval.

For at least a century, developmental theorists have described adolescents’ emerging abilities for transcendent social thinking, known also as abstract thinking, as a hallmark developmental stage 6 , 8 , 12 , 54 . Here, we demonstrate that adolescents’ proclivity to engage with such thinking predicts key, large-scale brain networks’ increasing interconnectivity over time and that this neural development is, in turn, associated with personal and social well-being in young adulthood. Importantly, in our socioeconomically and ethnically diverse urban sample, IQ and demographics did not explain the findings.

We focused on the default mode and executive control networks in particular because we had previously demonstrated that mid-adolescents’ transcendent construals were associated with coordinated activity in these networks during a functional task 32 . Extensive research suggests that these networks support reflective, autobiographical and free-form thinking, and effortful, focused thinking, respectively 34 , 35 , 36 . These networks’ coordinated activity is associated with many forms of generative and social-emotional processing 38 , 39 , 40 , 41 , and with mental health, including among adolescents 45 . Our study associates the positive developmental coordination of these networks with mid-adolescents’ emerging dispositions to construct transcendent meaning, inferring the broader, systems-level, ethical and civic implications, and emotionally poignant values and personal lessons, that extend beyond the immediate social situation.

Our study utilized an open-ended interview approach that aimed to capture the patterns of thinking participants spontaneously employed as they reacted to compelling, true social stories. There is a long history in constructivist developmental science of examining individuals’ processes of thinking independent of the specific content; this approach allows researchers to capture developmentally characteristic ways of thinking 6 , 8 , 12 , 55 . Building from this tradition, we analyzed not “what” youth were thinking, whether they agreed with the protagonist’s choices or the values the protagonist appeared to endorse, but “how” they were thinking, whether they showed evidence of considering the broader implications of the story for themselves or the world. Similarly, identity development measures captured the degree to which a participant had deliberated on their values and views, without regard to what they had decided. Notably, every participant produced transcendent construals during the interview, and therefore was capable of transcendent thinking. Given this finding, our method arguably assesses adolescents’ developing agentic dispositions toward transcendent thinking—how much they spontaneously invoke this cognitive-affective process, given a situation or domain that invites it. Ultimately, transcendent thinking may be to the adolescent mind and brain what exercise is to the body: most people can exercise, but only those who do will reap the benefits.

Our findings also speak to the value of utilizing in-depth qualitative research in developmental cognitive neuroscience to begin to understand meaningful sources of individual variation in brain development, and meaningful implications of this variation for outcomes. Our modest sample size was chosen to make possible a natural-feeling experimental protocol that encourages participants to engage genuinely with the true stories we shared, and to feel comfortable taking the time to figure out and explain their responses. Although future research might possibly design a more efficient means of uncovering adolescents’ psychological propensities, the ecological validity intended in our approach allowed us to uncover a critical interindividual difference in intraindividual change 56 . The focus on large-scale neural networks allowed us to align a broad psychological capacity with an equivalently broad neurodevelopmental pattern, facilitating interdisciplinary interpretation of the findings 57 . The data provide a developmental cascade that is consistent with theoretical accounts of self-construction within the dynamic developmental system 8 , i.e., with the notion that youth actively contribute to their own development 23 . Our previous reports that, at the start of the study, participants’ patterns of construals were associated with real-world social and cognitive functioning 7 , and with trial-by-trial activity in the brain 32 , strengthen this interpretation.

Related, the findings speak to the power of longitudinal designs for understanding the ontogeny of interindividual variation in outcomes, and possible targets for intervention. By focusing on participants’ neural connectivity change scores , embedded in a longitudinal path of characteristic psychosocial developmental achievements—we are able to learn about a trajectory of neural change that is associated with later psychosocial health, and about a psychological precursor of that change. The change-score logic is similar to that of pediatricians who record infants’ eating and weight across time not because they are primarily interested in how a baby’s weight compares to that of their same-age peers (assuming the baby is within a normal weight range), but because they aim to describe the infant’s rate of weight gain relative to that of their peers. It is this rate that is associated with health outcomes. Pediatricians would also advise parents about the modifiable factors that effect the rate of weight gain, such as feeding. Analogously, our study found that neural change over time was associated with later psychosocial development indicative of young adult psychosocial health, and that transcendent thinking predicts this neural change. This finding underscores the importance of the developmental process cascading from transcendent thinking, and points the way for future studies that would strategically assess whether and when interventions could be effective.

Future research should also focus on the developmental origins of individual variation in mid-adolescents’ transcendent thinking, especially as transcendent thinking is context dependent and appears to be malleable over time 9 . Given adolescents’ expanding social sphere, our data suggest the possibility that adolescents’ emerging disposition for thinking transcendently about what they encounter may itself be a source of variation in how the brain develops over time, akin to the association between eating well and growing well as an infant. Schooling, community-based programming, and parenting that support adolescents’ capacities to generate culturally relevant meaning of their social world, and to build identity, produce lasting benefits 23 . These can include improved academic performance and persistence in school 19 , 21 , 58 , 59 , increased sense of life purpose 9 , and improved biological markers of health 60 .

Further, future studies linking developmental origins of transcendent thinking to earlier brain development and social circumstances might possibly contribute to sorting out aspects of the trajectory of neural development across adolescence. In general, more protracted periods of structural development, and higher levels of internetwork connectivity and neural network segregation, have been associated with beneficial life circumstances and psychological outcomes, as indexed mainly by SES and IQ 51 , 61 . However, while neural network integration generally increases across this age 24 , research on the relations between SES and functional neural development of resting state network connectivity in adolescence has produced conflicting findings 51 , 62 , 63 . The relative lack of clarity on these topics may reflect in part the complex dynamics of thought and emotion emerging at this age, and in part the limitations of the predictive constructs. In particular, measures of SES and IQ are coming increasingly under scrutiny for their potential cultural biases and, in the case of SES, for their inability to capture critical variation in youths’ family and community-level social supports and cultural assets that may facilitate patterns of thinking like those we capture here 64 , 65 , 66 . Future research should investigate the value of more naturalistic, ecological methods for studying the psychological correlates and predictors of brain development, and especially of methods that capture youths’ strengths and not simply their environmental liabilities.

From a more applied perspective, future research should test the causal nature of the relationship between transcendent thinking and future neural and psychosocial development, and the ways that exposure to educational and clinical practices designed to support increases in transcendent thinking may contribute to future growth. For example, research on the possible neurobiological effects of civically-oriented community schooling 19 and restorative justice approaches 59 , 67 could lead to insights for developmental science while contributing useful evidence for education, mental health, and juvenile justice reform 22 , 57 . Examining the ways that adolescents’ transcendent thinking can be leveraged for both prosocial and antisocial aims, toward healthy and unhealthy outcomes, is another important future direction. Our assessment of transcendent construals was value neutral; we did not judge the prosociality or normativity of a participant’s response. (That said, we note that our participants were screened for psychiatric diagnoses and serious disciplinary infractions, and their transcendent construals were overwhelmingly prosocial.)

We hope that our findings provide a source of developmental hypotheses for larger, longer-term longitudinal studies with the power to examine more nuanced neurological and psychological effects across a wide range of participants 68 , and normative cross-sectional ranges of neural connectivity and transcendent thinking at different ages. Ongoing research is identifying subtle patterns of longitudinal integration and differentiation in neural network functioning 27 , 30 , 47 , 69 . Many of the regions changing with development contribute to the cognitive and affective processing undergirding transcendent thinking and its components, such as emotional feelings, autobiographical memory, motivation and reward processing, and self-processing 28 , 48 , 70 . Youth will almost certainly vary on the kind of transcendent thinking processes they preferentially invoke, e.g., self-relevant versus systems-oriented, and therefore on their relative reliance on the different affective and cognitive component processes. Larger studies would be positioned to explore developmental effects with greater granularity, in youth exposed to a range of social, cultural and educational contexts, and to probe connections to various domains of information processing not explicitly social 57 . Larger studies would also be positioned to investigate the possibility of contextual moderation effects that were not detectable in our moderately-sized sample.

It is hard to imagine a human context in which the capacity to engage in transcendent thinking would not confer benefits, assuming that we collectively aim for wellness and an ethical society capable of interrogating structures and systems, and of innovation. By middle adolescence, youth are oriented to, and even agentically dedicated to, engaging in such thinking. As a result, they can count among society’s most idealistic and committed citizens. The disposition to build complex, values-based inferences about the personal, social and ethical implications of the situations we encounter, and to become curious about the reasoning behind complex societal systems, is uniquely human. The proclivity to think about issues and beliefs that transcend proximal goals and the current context is the basis for adult-like moral values, identity development, civic participation and a sense of purpose 18 . Our study suggests that as mid-adolescents engage in transcendent thinking, trying on their newly expanding capacities for making meaning, they coordinate neural networks involved in effortful thinking and internal reflection. This spontaneous, active coordination across development may contribute to the growth of both their brains and their minds, lifting them over the threshold to productive young adulthood.

Materials and methods

These data were collected as part of a larger project, for which participants also completed psychosocial activities, psychophysiological recordings and neuroimaging unrelated to the present study (e.g., interviews about school; studies of heart-rate variability; diffusion tensor imaging; see https://osf.io/gqs34 for more information). Methods for analyzing participants’ interview responses are described extensively in Gotlieb et al. 7 . The current study is the first to report longitudinal findings, and the first to analyze resting-state network connectivity and psychosocial outcome data. All study activities were approved by and carried out in accordance with the policies of the Institutional Review Board of the University of Southern California (UP-12-00206). All parents/legal guardians and participants gave written informed consent or assent as appropriate, and all participants were compensated for their time.

Participants

65 youth (36 female) were recruited from public high schools in low-SES neighborhoods in Los Angeles. All participants were right-handed, aged between 14 and 18 years at the time of the initial data collection, fluent in English and passing all classes in school; none were under school disciplinary action. None had a history of drug/alcohol use or neurological/psychiatric issues. Participant characteristics are as reported by participants and as confirmed by parents/legal guardians and teachers (to the best of their knowledge). Characteristics of the sample were as follows: 51 participants reported receiving free or reduced-price lunch at school (indicating low income/needs ratio for the family 71 , and therefore low-SES). Parents’ education, an additional factor associated with SES 50 , ranged from 8 to 18 years ( M  = 12.4 years, SD  = 3.8). 34 participants identified as Latinx, 29 as East Asian, and 2 as Afro-Latino; participants’ parents were born in 13 countries. Participants ranged in age from 14 to 18 ( M  = 15.77 years, SD  = 1.05) at the start of the study. IQ scores ranged from 79 to 131 ( M  = 103.6, SE  = 1.52; see below for relevant methodological details).

Adapting a previously established protocol (see Immordino-Yang et al. 72 ), participants reacted to 40 true, compelling stories about living, non-famous adolescents from around the world in a range of circumstances, during a 2-h private video-taped interview. The story corpus was previously piloted to be interesting and to elicit mixes of positively and negatively valenced emotions. The experimenter shared each story using a previously memorized script, and then played an accompanying documentary-style video of approximately 1 min in length depicting footage of the real-life protagonist (not an actor), using PowerPoint (Microsoft Office) displayed on a Lenovo laptop with a 17-inch screen. After showing each video, the experimenter asked, “how does this story make you feel?” The experimenter then looked down and transcribed as much as possible of the participant’s verbatim responses by hand-written notes. Participants were told that notetaking was conducted in case the video camera failed. Actually, these notes also served to standardize the experimenter’s behavior, so that the participant could respond freely. Participants were encouraged to be as candid as possible.

Neuroimaging

Following the interview and a short break, participants underwent a 7-min resting-state BOLD fMRI scan with simultaneous pulse monitoring. Participants were instructed to think about whatever they would like, to stay as still as possible, and to stay awake. An image of a nature scene without people or animals was displayed continuously. Participants returned to the lab to repeat the scan approximately 2 years later ( M time between scans  = 2.10 years, SD  = 0.21, range = 1.94–3.28 years). The protocol was timed so that scans would occur in the middle of the day.

MRI data acquisition

BOLD fMRI scanning at initial data collection was conducted with a 3 Tesla Siemens Trio scanner and a 12-channel matrix head coil. Functional scans were acquired using a T2 ∗ -weighted echo-planar imaging (EPI) sequence (TR = 2 s, TE = 25 ms, flip angle = 90°, acquisition matrix: 64 × 64, FOV = 192 mm) with a voxel resolution of 3 × 3 × 3 mm 3 . Forty-one continuous transverse slices were acquired in interleaved order to cover the whole brain. A total of 210 volumes were acquired during the 7-min resting state scan. Anatomical images were acquired using a magnetization-prepared rapid acquisition gradient echo (MPRAGE) sequence (TI = 800 ms, TR = 2530 ms, TE = 3.09 ms, flip angle = 10º°, isotropic voxel resolution of 1 mm 3 ; acquisition dimensions: 256 × 256 × 176). At the second data collection, a 3 Tesla Siemens Prisma scanner with 20-channel matrix head coils was used due to a system upgrade at the scanning facility. Scanning parameters remained the same. (N.B.: the analysis focuses on interindividual effects, which would be independent from any effects of the scanner upgrade.)

Pulse oximetry data acquisition

Pulse oximetry was acquired using an MRI-compatible oximeter (Nonin Medical Inc, 8600FO MRI, Plymouth, MN, USA) placed over the middle finger of the participant’s left hand; data were output to a BIOPAC MP150 system and recorded using the BIOPAC Acqknowledge software (version 4.1; BIOPAC Systems Inc., Goleta, CA, USA).

After the second fMRI scan, a trained experimenter individually administered the vocabulary and matrix reasoning subtests of the Wechsler Abbreviated Scale of Intelligence, second edition 73 in a private room. Subtests were scored, age normed and summed to produce total IQ scores. One participant completed only the vocabulary subtest due to time constraints; we imputed overall IQ from this score.

Psychosocial survey measures

Approximately 3 years, 4 months after the initial data collection, participants completed a modified and abridged version of the Objective Measure of Ego Identity Status instrument 74 , 75 via an online survey that they received via electronic communication (i.e., email, text message, and/or social media direct message). Three questions measured identity achievement (“I have gone through a period of serious questions about my values”; “I have developed my own viewpoint on what is best for me”; “I engage in self-exploration and discussions with others to figure out my views on life”). Two questions measured identity diffusion (“I just hang with the crowd”; “I sometimes join activities when asked, but I rarely try anything on my own”). All used a 5-point Likert scale, from “not at all true” to “completely true.” A composite identity development score was calculated from the average of responses on the Identity achievement questions and identity diffusion questions (reverse scored).

Approximately 5 years after initial data collection, participants completed an online survey of life satisfaction to capture well-being. Participants reported their satisfaction with their social relationships (as many as pertain; 7-point Likert scale; one question each for parents, siblings, friends, teachers/supervisors, romantic partner, children), with themselves (continuous sliding scale; “how satisfied are you with whom you have become?”), and with school (only if relevant; continuous sliding scale; “how much do you like school?”). A composite life-satisfaction score was calculated from the average of z -scores for: satisfaction with self; satisfaction with school (if relevant); and satisfaction with social relationships (calculated as an average of reported values). All survey instruments were administered using Qualtrics software (Provo, UT).

Transcendent construals

For our previous study, videotaped interviews had been transcribed and verified. Each participant response had then been blind coded and reliability coded for transcendent construals (See Gotlieb et al. 7 , 32 ). Building from our previous work, transcendent construals were defined as utterances reflecting:

(i) systems-level analyses or moral judgements, or curiosities about how and why systems work as they do, e.g.,

“I also find it unfair that the people get undocumented. It’s kind of weird how it’s like a label how like just ‘cause you are from some other place, um, you can’t do certain things in another place. It’s like a question. It’s like something I’ve always wondered…”;

(ii) discussions of broad implications, morals and moral emotions, perspectives, personal lessons or values derived from the story, e.g.,

“I think back to the idea that because children are the future […] we have to be able to inspire people who are growing and have the potential to improve the societies”; “it makes me happy for humanity”;

or (iii) analyses of the protagonist’s qualities of character, mind, or perspective, e.g.,

“[she is] thinking, ‘oh, you’re not alone. You have others who are dependent on you’.”

Importantly, it was not relevant whether the participant endorsed a value or lesson or agreed with the protagonist, e.g.,

“I wouldn’t react that way. I’d just be really mad at the kid instead of, you know, selfless like that and trying to help him. Like I wouldn’t be able to put myself in someone’s shoes like that like he did.”

Construals not considered transcendent pertain mainly to discussions of the protagonist’s immediate situation, e.g., “I’m glad it all worked out,” or evaluating the protagonist’s decisions or actions, e.g., “I feel like they should have planned it more”; or to the empathic emotions of the participant, e.g., “I feel really sad for her, and like, second-hand embarrassment”. Unlike transcendent construals, these examples involve reactive, concrete and context dependent interpretations.

Participants received a score of 1 for each transcendent construal. Scores across all trials were summed to produce a total score for each participant.

Neural data processing

Fmri data pre-processing.

MRI data underwent standard preprocessing using SPM12 (v.7771) implemented in MATLAB 2015b (Wellcome Department of Cognitive Neurology, London, UK; MathWorks, Inc., Natick, MA, USA). Functional images were slice timing and motion corrected, and co-registered to the anatomical image. Anatomical images were normalized to the Montreal Neurological Institute space using the segmentation procedure. The resulting normalization transformation was applied to the functional images. Co-registered and normalized images were visually inspected for each participant to ensure quality, and all were satisfactory. The functional images were resampled into an isotropic voxel resolution of 2 × 2 × 2 mm 3 and smoothed using an 8 mm full width at half maximum Gaussian kernel. To quantify and evaluate head motion, framewise displacement (FD 76 ) was calculated. Across all scans, the number of volumes across the scan with FD over 1 mm ranged from 0 to 63 out of 210 (M = 4.0, SD = 8.6); the average FD across the resting state scan ranged from 0.06 to 0.92 mm (M = 0.19, SD = 0.13). No data were discarded due to head motion, though special care was taken to ensure that network identification and the associated connectivity measures are not biased by motion, either of the head or due to cardiac pulsation; see SI Sect.  1 .

Neural network identification

Resting-state data were run through a 20-component group-level spatial independent component analysis 77 using the Infomax algorithm (as implemented in the GIFT toolbox version 4.0b, http://mialab.mrn.org/software/gift/index.html ). Group-level ICA was run without additional denoising because this approach has been shown to identify the networks of interest most accurately 78 . The 20-component model was chosen because it has been shown to capture large-scale networks, including those of interest here 79 , 80 . The first 5 TRs of the resting state scan were excluded to allow for signal stabilization. Consistent with standard practice, the algorithm was run 20 times with different initial values to evaluate the reliability of results using the ICASSO 81 function in the GIFT toolbox. All components were highly reliable, with stability indices greater than 0.96 81 . Group-level components were then back-reconstructed to create individual-level component spatial maps and corresponding component time-courses for the initial and second data collections. Component spatial maps were visually inspected; components containing the left ECN, right ECN, and the DMN, were identified (see Fig.  2 and SI Sect.  2 ). Cross-correlation values between the identified network component maps and the resting state templates from Smith et al. (2009) 80 were calculated using the fslcc function from the FMRIB Software Library (Version 6.0.6.5). Cross-correlation values for the group-level components are DMN: 0.77; left ECN: 0.74; right ECN: 0.72, considered high 82 .

Depiction of coronal, sagittal, and axial views of the group-level default mode network (DMN; top) and left executive control network (left ECN; bottom) maps, derived from a 20-component group independent component analysis of the resting-state data (concatenating data from all participants from the two data collections), transformed into z -score maps and thresholded at z  = 2.

Network functional connectivity

Network functional connectivity was calculated using the Mancovan toolbox 83 implemented in GIFT. Default corrections and, for additional confidence, motion corrections, were applied for the ECN and DMN component time courses. This step included linear, quadratic, and cubic detrending; de-spiking; low-pass filtering with a high frequency cutoff of 0.15 Hz; and, to remove any residual influence of head motion, regressing out twenty-four expanded motion parameters 84 . Pairwise between-network correlation coefficients were calculated using corrected time courses across the entire scan (205 TRs) and then Fisher z-transformed to capture the strength of functional connectivity between networks for the initial data collection and second data collection separately for each individual. To capture longitudinal change, the difference in between-network correlation coefficients for the initial and second data collections for each participant was calculated.

Statistical analysis

Statistical analyses were carried out using RStudio (Version 2023.06.0 + 421, Posit Software, PBC) and R (Version 4.3.1). All reported statistical tests are 2-tailed. Data and R scripts are available at: https://osf.io/6cejy .

Missing data and multiple imputation

All participants had complete data from the initial collection. Missing neuroimaging data at the second data collection were due to unexplained extensive signal loss (1 participant), acquiring dental braces or metal implants (5 participants), or moving away (4 participants). Only two participants attrited after completing the initial data collections; all other missing data were partial. The percentage of missing values across the variables of interest varied between 0 and 15%. Given the reasons for the missing data are known and unlikely to be related to the measures of interest, the data were assumed to be missing at random and appropriate for multiple imputation 85 . (See also sensitivity analyses, described below.)

Missing data were imputed under fully conditional specification using predictive mean matching with 10 maximum iterations, as implemented in the Multivariate Imputation by Chained Equations (“mice”) package 86  (Version 3.16). All existing data, including measures of interest and covariates, were used to conduct imputation. To stabilize results, 100 imputed datasets were produced. All difference scores were calculated after imputation.

Calculating bootstrapped confidence intervals

5000 bootstrapped samples were generated using the Bootstrap Functions (“boot”) package 87 (Version 1.3–28.1) from each of the 100 imputed datasets, following Wu & Jia’s method 88 for combining bootstrapping with multiple imputation. Effects of interest were estimated using each imputed/bootstrapped sample. The resulting 100 sets of 5000 parameter estimates were combined into one distribution for each effect of interest, which was used to derive the mean and a 95% confidence interval using the percentile method 89 .

Testing hypothesis 1

The effect of transcendent construals on longitudinal changes in between-network functional connectivity was examined using a series of fixed effect linear regression models to examine the hypothesized effect and then to confirm the effect when including additional relevant control and moderation terms. All analyses control for differences in average FD (head motion) between the two scans and time between scans. Models were run based on each imputed dataset. Two methods for significance testing were utilized to assure robustness. In the first, statistics of interest were pooled using Rubin’s rules 85 for averaging regression coefficients, combining associated variances, and calculating degrees of freedom and an associated p value (as implemented in the “mice” package 86 ). In the second, all models were estimated again using each of the imputed/bootstrapped samples to provide a confidence interval as described above.

Testing hypothesis 2

The effect of transcendent construals on life satisfaction through internetwork connectivity change and identity development, and through alternative paths that omit either or both of these intermediate measures, was tested. To do this, relevant measures were chronologically ordered to construct a developmental path model. Then, following the method described in Hayes 90 , for each of the imputed/bootstrapped datasets, three regression models were estimated using ordinary least squares, structured such that each subsequent measure in the path is predicted by the previous measures. These regression models controlled for differences in average FD (head motion) between the two scans and time between scans. Next, effects through the four possible paths (represented by the colors in Fig.  1 ) were calculated as the product of regression coefficients along the path. Significance testing was carried out based on the distribution of resulting parameter estimates, as described above.

Sensitivity analyses

Several analyses were run to give assurance that the findings are not biased by methodological decisions. To confirm that the findings hold in the sample without imputing missing data, the analyses were run with only complete cases. All findings hold; see SI Sect.  3 .

To address the possibility that the missing psychosocial data could have violated the missing-at-random assumption, we used the post-processing procedure from the “mice” package 86  to systematically vary imputed datapoints from what they would be under the missing-at-random assumption, to values corresponding to plus/minus 20% of the range of existing data. All findings hold.

Data availability

The data that support the findings of this study are available at https://osf.io/6cejy .

Code availability

The R scripts for statistical analyses presented in the paper are available at https://osf.io/6cejy .

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Acknowledgements

We thank L.G. Cardona, D. Cremat, E. Jahner, L. Kim, C. Kundrak, H. Rajana, R. Riveros, and C. Simone for assistance with data collection and processing; P. Baniqued, A. Ghaderi, M. Polikoff, M. Lai, A. Montoya for advice on analyses; Artesia High School in Lakewood, CA, Rowland Unified School District in Rowland Heights, CA, and other participating public high schools for help with participant recruitment; A. Blodget, A. Damasio, H. Damasio, D. Daniel, R. Davidson, H. Gardner, A. Ghaderi, D. Knecht, and C.D. Lee for comments on an earlier version of the manuscript. This work was funded by grants from the National Science Foundation (CAREER 11519520; BCS 1522986) and Raikes Foundation (61405837-118286) and by gifts from ECMC Foundation and Stuart Foundation to MHIY; NSF GRFP and USC Provost’s Research Fellowship to RG.

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Center for Dyslexia, Diverse Learners, and Social Justice, School of Education and Information Studies, University of California Los Angeles, Los Angeles, USA

Rebecca J. M. Gotlieb

Center for Affective Neuroscience, Development, Learning and Education; Brain and Creativity Institute; Rossier School of Education, University of Southern California, Los Angeles, CA, USA

Xiao-Fei Yang & Mary Helen Immordino-Yang

Neuroscience Graduate Program; Psychology Department, University of Southern California, Los Angeles, CA, USA

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R.G., X.-F.Y., and M.H.I.-Y. designed research, performed research, analyzed the data and wrote the paper.

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Gotlieb, R.J.M., Yang, XF. & Immordino-Yang, M.H. Diverse adolescents’ transcendent thinking predicts young adult psychosocial outcomes via brain network development. Sci Rep 14 , 6254 (2024). https://doi.org/10.1038/s41598-024-56800-0

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Given that synapses are the connections between our brain cells, one might think that having as many of these as possible is a good thing. However, primate brains do something unexpected: After early childhood, the connections between brain cells start to decrease in a process called "synaptic pruning." Surprisingly, we know very little about the molecular mechanism behind how synapses change as brains mature and this is also a hurdle for the development of cures to neuropsychological disorders such as the autism spectrum disorder.

Both recent developments in the ability to analyze complex protein assemblies and the recent availability of marmosets as non-human primate model organisms for studies on the brain enabled Kobe University neuroscientist TAKUMI Toru to tackle this knowledge gap. He explains, "The Collaboration with experts in proteomics and non-human primate brain has been a critical factor for enabling this study. Also, we have established an analytical pipeline to compare multiple biological data sets using the latest statistical and bioinformatics tools, which was another crucial element." With this, they focused their studies on the analysis of a protein agglomeration found on the signal-receiving side of synapses, the so-called "postsynaptic density," as it has become clear in previous studies that its constituents are key to the development of small mushroom-shaped protrusions on the signal-receiving cells where synapses are formed.

Their results, published in the journal Nature Communications , are science's first look at what is happening at the protein level in synapses during brain development in the first weeks, months and years after birth. The Kobe University-led research team identified a group of proteins that are produced more and others that are produced less as time passes and could confirm that this is due to changes in gene regulation. They also found that the timing of this regulation is different in mice and marmosets: What happens in mice two weeks after birth happens before or around birth in marmosets. In addition, marmosets have a second phase of protein changes that mice don't have. "This may be related to the evolutionary differences between rodent and primate brains," comments KAIZUKA Takeshi, the first author of the paper, also in respect to the process of synaptic pruning.

Takumi's interest didn't stop there. Knowing that the development of autism spectrum disorder is connected to developmental immaturities of synapses, they investigated what this means on the level of the proteins his group had identified to be connected to synapse development. And indeed, they found that the genes reported to be expressed differentially in autism patients also feature prominently in their data. "These data suggest that the postsynaptic density in autism spectrum disorder patients is relatively similar to that in the prenatal or neonatal period compared to healthy subjects," the researchers write in the study. Being thus able to construct hypotheses on the molecular mechanism behind the emergence of the disorder, this might open up the path for the development of treatments.

Takumi sums up the implications of this study. "Synapse development is a crucial issue to consider in the maturation of brains. Its abnormalities are related to neuropsychiatric disorders, including autism spectrum disorders and schizophrenia. The proteome datasets we provided are important for considering molecular mechanisms of synapse development and the difference between rodents and primates."

This research was funded by the Japan Society for the Promotion of Science (grants 16H06316, 16H06463, 18K14830, 21H04813, 23H04233, 23KK0132 and 16J04376), the Japan Agency for Medical Research and Development (grants JP21wm0425011 and JP20dm0207001), the Japan Science and Technology Agency (grants JPMJMS2299 and JPMJMS229B), the National Center of Neurology and Psychiatry (grant 30-9), the Takeda Science Foundation, and the Taiju Life Social Welfare Foundation. It was conducted in collaboration with researchers from the RIKEN Center for Brain Science, the RIKEN Center for Sustainable Resource Science, the Max Planck Institute for Experimental Medicine, Hokkaido University and Keio University.

• Birth Defects
• Pregnancy and Childbirth
• Parkinson's Research
• Learning Disorders
• Child Development
• Biotechnology
• Asperger syndrome
• Autistic spectrum
• Psychometrics
• Personalized medicine
• Social cognition
• Sleep disorder
• Stem cell treatments

Story Source:

Materials provided by Kobe University . Note: Content may be edited for style and length.

Journal Reference :

• Takeshi Kaizuka, Takehiro Suzuki, Noriyuki Kishi, Kota Tamada, Manfred W. Kilimann, Takehiko Ueyama, Masahiko Watanabe, Tomomi Shimogori, Hideyuki Okano, Naoshi Dohmae, Toru Takumi. Remodeling of the postsynaptic proteome in male mice and marmosets during synapse development . Nature Communications , 2024; 15 (1) DOI: 10.1038/s41467-024-46529-9

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Module 1: Lifespan Development

Developmental research designs, learning outcomes.

• Compare advantages and disadvantages of developmental research designs (cross-sectional, longitudinal, and sequential)

Now you know about some tools used to conduct research about human development. Remember,  research methods  are tools that are used to collect information. But it is easy to confuse research methods and research design. Research design is the strategy or blueprint for deciding how to collect and analyze information. Research design dictates which methods are used and how. Developmental research designs are techniques used particularly in lifespan development research. When we are trying to describe development and change, the research designs become especially important because we are interested in what changes and what stays the same with age. These techniques try to examine how age, cohort, gender, and social class impact development.

Cross-sectional designs

The majority of developmental studies use cross-sectional designs because they are less time-consuming and less expensive than other developmental designs. Cross-sectional research designs are used to examine behavior in participants of different ages who are tested at the same point in time. Let’s suppose that researchers are interested in the relationship between intelligence and aging. They might have a hypothesis (an educated guess, based on theory or observations) that intelligence declines as people get older. The researchers might choose to give a certain intelligence test to individuals who are 20 years old, individuals who are 50 years old, and individuals who are 80 years old at the same time and compare the data from each age group. This research is cross-sectional in design because the researchers plan to examine the intelligence scores of individuals of different ages within the same study at the same time; they are taking a “cross-section” of people at one point in time. Let’s say that the comparisons find that the 80-year-old adults score lower on the intelligence test than the 50-year-old adults, and the 50-year-old adults score lower on the intelligence test than the 20-year-old adults. Based on these data, the researchers might conclude that individuals become less intelligent as they get older. Would that be a valid (accurate) interpretation of the results?

Figure 1 . Example of cross-sectional research design

No, that would not be a valid conclusion because the researchers did not follow individuals as they aged from 20 to 50 to 80 years old. One of the primary limitations of cross-sectional research is that the results yield information about age differences  not necessarily changes with age or over time. That is, although the study described above can show that in 2010, the 80-year-olds scored lower on the intelligence test than the 50-year-olds, and the 50-year-olds scored lower on the intelligence test than the 20-year-olds, the data used to come up with this conclusion were collected from different individuals (or groups of individuals). It could be, for instance, that when these 20-year-olds get older (50 and eventually 80), they will still score just as high on the intelligence test as they did at age 20. In a similar way, maybe the 80-year-olds would have scored relatively low on the intelligence test even at ages 50 and 20; the researchers don’t know for certain because they did not follow the same individuals as they got older.

It is also possible that the differences found between the age groups are not due to age, per se, but due to cohort effects. The 80-year-olds in this 2010 research grew up during a particular time and experienced certain events as a group. They were born in 1930 and are part of the Traditional or Silent Generation. The 50-year-olds were born in 1960 and are members of the Baby Boomer cohort. The 20-year-olds were born in 1990 and are part of the Millennial or Gen Y Generation. What kinds of things did each of these cohorts experience that the others did not experience or at least not in the same ways?

You may have come up with many differences between these cohorts’ experiences, such as living through certain wars, political and social movements, economic conditions, advances in technology, changes in health and nutrition standards, etc. There may be particular cohort differences that could especially influence their performance on intelligence tests, such as education level and use of computers. That is, many of those born in 1930 probably did not complete high school; those born in 1960 may have high school degrees, on average, but the majority did not attain college degrees; the young adults are probably current college students. And this is not even considering additional factors such as gender, race, or socioeconomic status. The young adults are used to taking tests on computers, but the members of the other two cohorts did not grow up with computers and may not be as comfortable if the intelligence test is administered on computers. These factors could have been a factor in the research results.

Another disadvantage of cross-sectional research is that it is limited to one time of measurement. Data are collected at one point in time and it’s possible that something could have happened in that year in history that affected all of the participants, although possibly each cohort may have been affected differently. Just think about the mindsets of participants in research that was conducted in the United States right after the terrorist attacks on September 11, 2001.

Longitudinal research designs

Figure 2 . Longitudinal research studies the same person or group of people over an extended period of time.

Longitudinal   research involves beginning with a group of people who may be of the same age and background (cohort) and measuring them repeatedly over a long period of time. One of the benefits of this type of research is that people can be followed through time and be compared with themselves when they were younger; therefore changes with age over time are measured. What would be the advantages and disadvantages of longitudinal research? Problems with this type of research include being expensive, taking a long time, and participants dropping out over time. Think about the film, 63 Up , part of the Up Series mentioned earlier, which is an example of following individuals over time. In the videos, filmed every seven years, you see how people change physically, emotionally, and socially through time; and some remain the same in certain ways, too. But many of the participants really disliked being part of the project and repeatedly threatened to quit; one disappeared for several years; another died before her 63rd year. Would you want to be interviewed every seven years? Would you want to have it made public for all to watch?   

Longitudinal research designs are used to examine behavior in the same individuals over time. For instance, with our example of studying intelligence and aging, a researcher might conduct a longitudinal study to examine whether 20-year-olds become less intelligent with age over time. To this end, a researcher might give an intelligence test to individuals when they are 20 years old, again when they are 50 years old, and then again when they are 80 years old. This study is longitudinal in nature because the researcher plans to study the same individuals as they age. Based on these data, the pattern of intelligence and age might look different than from the cross-sectional research; it might be found that participants’ intelligence scores are higher at age 50 than at age 20 and then remain stable or decline a little by age 80. How can that be when cross-sectional research revealed declines in intelligence with age?

Figure 3 . Example of a longitudinal research design

Since longitudinal research happens over a period of time (which could be short term, as in months, but is often longer, as in years), there is a risk of attrition. Attrition occurs when participants fail to complete all portions of a study. Participants may move, change their phone numbers, die, or simply become disinterested in participating over time. Researchers should account for the possibility of attrition by enrolling a larger sample into their study initially, as some participants will likely drop out over time. There is also something known as  selective attrition— this means that certain groups of individuals may tend to drop out. It is often the least healthy, least educated, and lower socioeconomic participants who tend to drop out over time. That means that the remaining participants may no longer be representative of the whole population, as they are, in general, healthier, better educated, and have more money. This could be a factor in why our hypothetical research found a more optimistic picture of intelligence and aging as the years went by. What can researchers do about selective attrition? At each time of testing, they could randomly recruit more participants from the same cohort as the original members, to replace those who have dropped out.

The results from longitudinal studies may also be impacted by repeated assessments. Consider how well you would do on a math test if you were given the exact same exam every day for a week. Your performance would likely improve over time, not necessarily because you developed better math abilities, but because you were continuously practicing the same math problems. This phenomenon is known as a practice effect. Practice effects occur when participants become better at a task over time because they have done it again and again (not due to natural psychological development). So our participants may have become familiar with the intelligence test each time (and with the computerized testing administration).

Another limitation of longitudinal research is that the data are limited to only one cohort. As an example, think about how comfortable the participants in the 2010 cohort of 20-year-olds are with computers. Since only one cohort is being studied, there is no way to know if findings would be different from other cohorts. In addition, changes that are found as individuals age over time could be due to age or to time of measurement effects. That is, the participants are tested at different periods in history, so the variables of age and time of measurement could be confounded (mixed up). For example, what if there is a major shift in workplace training and education between 2020 and 2040 and many of the participants experience a lot more formal education in adulthood, which positively impacts their intelligence scores in 2040? Researchers wouldn’t know if the intelligence scores increased due to growing older or due to a more educated workforce over time between measurements.

Sequential research designs

Sequential research designs include elements of both longitudinal and cross-sectional research designs. Similar to longitudinal designs, sequential research features participants who are followed over time; similar to cross-sectional designs, sequential research includes participants of different ages. This research design is also distinct from those that have been discussed previously in that individuals of different ages are enrolled into a study at various points in time to examine age-related changes, development within the same individuals as they age, and to account for the possibility of cohort and/or time of measurement effects. In 1965, K. Warner Schaie [1] (a leading theorist and researcher on intelligence and aging), described particular sequential designs: cross-sequential, cohort sequential, and time-sequential. The differences between them depended on which variables were focused on for analyses of the data (data could be viewed in terms of multiple cross-sectional designs or multiple longitudinal designs or multiple cohort designs). Ideally, by comparing results from the different types of analyses, the effects of age, cohort, and time in history could be separated out.

Consider, once again, our example of intelligence and aging. In a study with a sequential design, a researcher might recruit three separate groups of participants (Groups A, B, and C). Group A would be recruited when they are 20 years old in 2010 and would be tested again when they are 50 and 80 years old in 2040 and 2070, respectively (similar in design to the longitudinal study described previously). Group B would be recruited when they are 20 years old in 2040 and would be tested again when they are 50 years old in 2070. Group C would be recruited when they are 20 years old in 2070 and so on.

Figure 4. Example of sequential research design

Studies with sequential designs are powerful because they allow for both longitudinal and cross-sectional comparisons—changes and/or stability with age over time can be measured and compared with differences between age and cohort groups. This research design also allows for the examination of cohort and time of measurement effects. For example, the researcher could examine the intelligence scores of 20-year-olds in different times in history and different cohorts (follow the yellow diagonal lines in figure 3). This might be examined by researchers who are interested in sociocultural and historical changes (because we know that lifespan development is multidisciplinary). One way of looking at the usefulness of the various developmental research designs was described by Schaie and Baltes (1975) [2] : cross-sectional and longitudinal designs might reveal change patterns while sequential designs might identify developmental origins for the observed change patterns.

Since they include elements of longitudinal and cross-sectional designs, sequential research has many of the same strengths and limitations as these other approaches. For example, sequential work may require less time and effort than longitudinal research (if data are collected more frequently than over the 30-year spans in our example) but more time and effort than cross-sectional research. Although practice effects may be an issue if participants are asked to complete the same tasks or assessments over time, attrition may be less problematic than what is commonly experienced in longitudinal research since participants may not have to remain involved in the study for such a long period of time.

When considering the best research design to use in their research, scientists think about their main research question and the best way to come up with an answer. A table of advantages and disadvantages for each of the described research designs is provided here to help you as you consider what sorts of studies would be best conducted using each of these different approaches.

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• Schaie, K.W. (1965). A general model for the study of developmental problems. Psychological Bulletin, 64(2), 92-107. ↵
• Schaie, K.W. & Baltes, B.P. (1975). On sequential strategies in developmental research: Description or Explanation. Human Development, 18: 384-390. ↵
• Modification, adaptation, and original content. Authored by : Margaret Clark-Plaskie for Lumen Learning. Provided by : Lumen Learning. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
• Research Methods in Developmental Psychology. Authored by : Angela Lukowski and Helen Milojevich. Provided by : University of Calfornia, Irvine. Located at : https://nobaproject.com/modules/research-methods-in-developmental-psychology?r=LDcyNTg0 . Project : The Noba Project. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
• Woman holding own photograph. Provided by : Pxhere. Located at : https://pxhere.com/en/photo/221167 . License : CC0: No Rights Reserved

Nevada Today

Establishing the effectiveness of health personnel development aid toward the united nations’ sustainable development goals, researchers find a breakthrough from the perspective of ‘nurses and midwives’.

On Feb 6. Sandun Perera, associate professor of business analytics and operations at the University of Nevada, Reno, and his international coauthors including Yong Wang, Sachin K. Mangla, Linna Han, and Malin Song, unveiled their groundbreaking research aimed at supporting the universal health coverage goals of the United Nations (UN) and the World Health Organization (WHO). 

In the article, “Aligning Development Aid toward Sustainable Development Goals: When and Where is Aid Effective on the Health Workforce?” published in Production and Operations Management journal, one of the Financial Times top 50 journals, the authors study the effectiveness of aid toward the health workforce development in developing countries. The UN's Sustainable Development Goals (SDGs) focus on ensuring universal health coverage by 2030 and WHO highlights the pivotal role of healthcare workers in achieving SDG-3.

The research aimed to boost the confidence of donors and funding managers by demonstrating how effective development aid can be in improving the health workforce. This effort specifically aligns with the SDG 3.c goal set by the UN. Additionally, researchers seek to provide clear guidance to funding managers and policymakers on the most effective ways to use development aid to enhance the growth of the health workforce. This will help ensure that aid is allocated to the right segment within the healthcare sector.

It was evident that even before the COVID-19 pandemic, there weren't enough healthcare workers worldwide. There was a large gap of 18 million between the number of workers there were and what was needed. In 2018, most countries in the African region had less than ten nurses and midwives for every 10,000 people. When the pandemic hit, it made these shortages even worse, and some countries had to ask for help from others. Despite these high needs and the introduction of SDG 3.c, which focuses on improving health financing and the health workforce, only a tiny 0.78% of all the money for healthcare in 2020 actually went towards helping build up the healthcare workforce. This small amount shows a big problem: not enough money is going to where it's most needed. The goal was to show how helpful development aid can be when allocated effectively.

“On one hand, given the dire need for the health workforce in developing countries, we were very surprised to know that only less than one percent of all official health aid has been directed to health workforce development,” Perera said. “On the other hand, the effectiveness of health personnel development aids within individual segments of the health workforce and constraints that determine the effectiveness was not known to policymakers, thereby greatly deterring the provision and allocation of aids.”

In their research, they also observed that during global pandemics like COVID-19, people tend to support giving less money for aid. This happens because the countries donating money also have financial troubles during these crises. However, they argue that support for aid could go up when people see it as a way to stop future outbreaks, especially by helping developing countries first. More interestingly, as a breakthrough finding, researchers show that the money given for aid always helps nurses and midwives irrespective of the current workforce density of this leading segment. Thus, to address global health workforce shortages amid pandemics, the funding managers should focus on showing donors how effective the aid is, especially when targeted toward pandemic-specific health workforce development such as nurses and midwives.

Additionally, when deciding where to send aid, people should think about the government effectiveness of recipient countries as well as the current density of individual segments within the health workforce. For example, if a country doesn't have enough doctors and clinical workers, sending aid might not directly solve that problem. Also, the effectiveness of aid varies depending on how many healthcare workers a country already has.

“Ensuring universal health coverage is one of the top priorities under the United Nations’ Sustainable Development Goals (SDGs). Moreover, the Association to Advance Collegiate Schools of Business (AACSB) is keen on our commitment to SDGs. Our research not only directly supports the UN’s SDG-3 on Good Health and Well-being by urging funding managers and donors to reconsider overall aid provision and allocation for adequate, fair, and effective support for global health workforce development, but also fulfills the AACSB’s mission on the societal impact of Business School research. Lastly, it’s very satisfying to see insights derived from data-driven research shedding light on a real-life humanitarian problem,” Perera said.

The research highlights the urgent need for focused support in developing the global healthcare workforce. Despite the essential role healthcare workers play, especially in times like the COVID-19 pandemic, less than one percent of official health aid targets this critical area. By emphasizing the importance of tailoring aid to match the specific needs of recipient countries and concentrating on key groups like nurses and midwives, the study offers practical insights for policymakers and funding managers.

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3.4: Developmental Research Designs

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Margaret Clark-Plaskie; Lumen Learning; Angela Lukowski; Helen Milojevich; and Diana Lang

• Compare advantages and disadvantages of developmental research designs (cross-sectional, longitudinal, and sequential)
• Describe challenges associated with conducting research in lifespan development

Now you know about some tools used to conduct research about human development. Remember, research methods are tools that are used to collect information. But it is easy to confuse research methods and research design. Research design is the strategy or blueprint for deciding how to collect and analyze information. Research design dictates which methods are used and how. Developmental research designs are techniques used particularly in lifespan development research. When we are trying to describe development and change, the research designs become especially important because we are interested in what changes and what stays the same with age. These techniques try to examine how age, cohort, gender, and social class impact development. [1]

Cross-sectional designs

The majority of developmental studies use cross-sectional designs because they are less time-consuming and less expensive than other developmental designs. Cross-sectional research designs are used to examine behavior in participants of different ages who are tested at the same point in time (Figure 1). Let’s suppose that researchers are interested in the relationship between intelligence and aging. They might have a hypothesis (an educated guess, based on theory or observations) that intelligence declines as people get older. The researchers might choose to give a certain intelligence test to individuals who are 20 years old, individuals who are 50 years old, and individuals who are 80 years old at the same time and compare the data from each age group. This research is cross-sectional in design because the researchers plan to examine the intelligence scores of individuals of different ages within the same study at the same time; they are taking a “cross-section” of people at one point in time. Let’s say that the comparisons find that the 80-year-old adults score lower on the intelligence test than the 50-year-old adults, and the 50-year-old adults score lower on the intelligence test than the 20-year-old adults. Based on these data, the researchers might conclude that individuals become less intelligent as they get older. Would that be a valid (accurate) interpretation of the results?

No, that would not be a valid conclusion because the researchers did not follow individuals as they aged from 20 to 50 to 80 years old. One of the primary limitations of cross-sectional research is that the results yield information about age differences not necessarily changes with age or over time. That is, although the study described above can show that in 2010, the 80-year-olds scored lower on the intelligence test than the 50-year-olds, and the 50-year-olds scored lower on the intelligence test than the 20-year-olds, the data used to come up with this conclusion were collected from different individuals (or groups of individuals). It could be, for instance, that when these 20-year-olds get older (50 and eventually 80), they will still score just as high on the intelligence test as they did at age 20. In a similar way, maybe the 80-year-olds would have scored relatively low on the intelligence test even at ages 50 and 20; the researchers don’t know for certain because they did not follow the same individuals as they got older.

It is also possible that the differences found between the age groups are not due to age, per se, but due to cohort effects. The 80-year-olds in this 2010 research grew up during a particular time and experienced certain events as a group. They were born in 1930 and are part of the Traditional or Silent Generation. The 50-year-olds were born in 1960 and are members of the Baby Boomer cohort. The 20-year-olds were born in 1990 and are part of the Millennial or Gen Y Generation. What kinds of things did each of these cohorts experience that the others did not experience or at least not in the same ways?

You may have come up with many differences between these cohorts’ experiences, such as living through certain wars, political and social movements, economic conditions, advances in technology, changes in health and nutrition standards, etc. There may be particular cohort differences that could especially influence their performance on intelligence tests, such as education level and use of computers. That is, many of those born in 1930 probably did not complete high school; those born in 1960 may have high school degrees, on average, but the majority did not attain college degrees; the young adults are probably current college students. And this is not even considering additional factors such as gender, race, or socioeconomic status. The young adults are used to taking tests on computers, but the members of the other two cohorts did not grow up with computers and may not be as comfortable if the intelligence test is administered on computers. These factors could have been a factor in the research results.

Another disadvantage of cross-sectional research is that it is limited to one time of measurement. Data are collected at one point in time and it’s possible that something could have happened in that year in history that affected all of the participants, although possibly each cohort may have been affected differently. Just think about the mindsets of participants in research that was conducted in the United States right after the terrorist attacks on September 11, 2001.

Longitudinal research designs

Longitudinal research involves beginning with a group of people who may be of the same age and background (cohort) and measuring them repeatedly over a long period of time (Figure 2 & 3). One of the benefits of this type of research is that people can be followed through time and be compared with themselves when they were younger; therefore changes with age over time are measured. What would be the advantages and disadvantages of longitudinal research? Problems with this type of research include being expensive, taking a long time, and subjects dropping out over time. Think about the film, 63 Up , part of the Up Series mentioned earlier, which is an example of following individuals over time. In the videos, filmed every seven years, you see how people change physically, emotionally, and socially through time; and some remain the same in certain ways, too. But many of the participants really disliked being part of the project and repeatedly threatened to quit; one disappeared for several years; another died before her 63rd year. Would you want to be interviewed every seven years? Would you want to have it made public for all to watch?

Longitudinal research designs are used to examine behavior in the same individuals over time. For instance, with our example of studying intelligence and aging, a researcher might conduct a longitudinal study to examine whether 20-year-olds become less intelligent with age over time. To this end, a researcher might give an intelligence test to individuals when they are 20 years old, again when they are 50 years old, and then again when they are 80 years old. This study is longitudinal in nature because the researcher plans to study the same individuals as they age. Based on these data, the pattern of intelligence and age might look different than from the cross-sectional research; it might be found that participants’ intelligence scores are higher at age 50 than at age 20 and then remain stable or decline a little by age 80. How can that be when cross-sectional research revealed declines in intelligence with age?

Since longitudinal research happens over a period of time (which could be short term, as in months, but is often longer, as in years), there is a risk of attrition. Attrition occurs when participants fail to complete all portions of a study. Participants may move, change their phone numbers, die, or simply become disinterested in participating over time. Researchers should account for the possibility of attrition by enrolling a larger sample into their study initially, as some participants will likely drop out over time. There is also something known as selective attrition— this means that certain groups of individuals may tend to drop out. It is often the least healthy, least educated, and lower socioeconomic participants who tend to drop out over time. That means that the remaining participants may no longer be representative of the whole population, as they are, in general, healthier, better educated, and have more money. This could be a factor in why our hypothetical research found a more optimistic picture of intelligence and aging as the years went by. What can researchers do about selective attrition? At each time of testing, they could randomly recruit more participants from the same cohort as the original members, to replace those who have dropped out.

The results from longitudinal studies may also be impacted by repeated assessments. Consider how well you would do on a math test if you were given the exact same exam every day for a week. Your performance would likely improve over time, not necessarily because you developed better math abilities, but because you were continuously practicing the same math problems. This phenomenon is known as a practice effect. Practice effects occur when participants become better at a task over time because they have done it again and again (not due to natural psychological development). So our participants may have become familiar with the intelligence test each time (and with the computerized testing administration).

Another limitation of longitudinal research is that the data are limited to only one cohort. As an example, think about how comfortable the participants in the 2010 cohort of 20-year-olds are with computers. Since only one cohort is being studied, there is no way to know if findings would be different from other cohorts. In addition, changes that are found as individuals age over time could be due to age or to time of measurement effects. That is, the participants are tested at different periods in history, so the variables of age and time of measurement could be confounded (mixed up). For example, what if there is a major shift in workplace training and education between 2020 and 2040 and many of the participants experience a lot more formal education in adulthood, which positively impacts their intelligence scores in 2040? Researchers wouldn’t know if the intelligence scores increased due to growing older or due to a more educated workforce over time between measurements.

Sequential research designs

Sequential research designs include elements of both longitudinal and cross-sectional research designs. Similar to longitudinal designs, sequential research features participants who are followed over time; similar to cross-sectional designs, sequential research includes participants of different ages. This research design is also distinct from those that have been discussed previously in that individuals of different ages are enrolled into a study at various points in time to examine age-related changes, development within the same individuals as they age, and to account for the possibility of cohort and/or time of measurement effects. In 1965, Schaie [2] (a leading theorist and researcher on intelligence and aging), described particular sequential designs: cross-sequential, cohort sequential, and time-sequential. The differences between them depended on which variables were focused on for analyses of the data (data could be viewed in terms of multiple cross-sectional designs or multiple longitudinal designs or multiple cohort designs). Ideally, by comparing results from the different types of analyses, the effects of age, cohort, and time in history could be separated out.

Consider, once again, our example of intelligence and aging. In a study with a sequential design, a researcher might recruit three separate groups of participants (Groups A, B, and C). Group A would be recruited when they are 20 years old in 2010 and would be tested again when they are 50 and 80 years old in 2040 and 2070, respectively (similar in design to the longitudinal study described previously). Group B would be recruited when they are 20 years old in 2040 and would be tested again when they are 50 years old in 2070. Group C would be recruited when they are 20 years old in 2070 and so on (Figure 4).

Studies with sequential designs are powerful because they allow for both longitudinal and cross-sectional comparisons—changes and/or stability with age over time can be measured and compared with differences between age and cohort groups. This research design also allows for the examination of cohort and time of measurement effects. For example, the researcher could examine the intelligence scores of 20-year-olds in different times in history and different cohorts (follow the yellow diagonal lines in figure 3). This might be examined by researchers who are interested in sociocultural and historical changes (because we know that lifespan development is multidisciplinary). One way of looking at the usefulness of the various developmental research designs was described by Schaie and Baltes [3] : cross-sectional and longitudinal designs might reveal change patterns while sequential designs might identify developmental origins for the observed change patterns.

Since they include elements of longitudinal and cross-sectional designs, sequential research has many of the same strengths and limitations as these other approaches. For example, sequential work may require less time and effort than longitudinal research (if data are collected more frequently than over the 30-year spans in our example) but more time and effort than cross-sectional research. Although practice effects may be an issue if participants are asked to complete the same tasks or assessments over time, attrition may be less problematic than what is commonly experienced in longitudinal research since participants may not have to remain involved in the study for such a long period of time.

When considering the best research design to use in their research, scientists think about their main research question and the best way to come up with an answer. A table of advantages and disadvantages for each of the described research designs is provided here to help you as you consider what sorts of studies would be best conducted using each of these different approaches.

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Challenges Associated with Conducting Developmental Research

The previous sections describe research tools to assess development across the lifespan, as well as the ways that research designs can be used to track age-related changes and development over time. Before you begin conducting developmental research, however, you must also be aware that testing individuals of certain ages (such as infants and children) or making comparisons across ages (such as children compared to teens) comes with its own unique set of challenges. In the final section of this module, let’s look at some of the main issues that are encountered when conducting developmental research, namely ethical concerns, recruitment issues, and participant attrition.

Ethical Concerns

As a student of the social sciences, you may already know that Institutional Review Boards (IRBs) must review and approve all research projects that are conducted at universities, hospitals, and other institutions (each broad discipline or field, such as psychology or social work, often has its own code of ethics that must also be followed, regardless of institutional affiliation). An IRB is typically a panel of experts who read and evaluate proposals for research. IRB members want to ensure that the proposed research will be carried out ethically and that the potential benefits of the research outweigh the risks and potential harm (psychological as well as physical harm) for participants.

What you may not know though, is that the IRB considers some groups of participants to be more vulnerable or at-risk than others. Whereas university students are generally not viewed as vulnerable or at-risk, infants and young children commonly fall into this category. What makes infants and young children more vulnerable during research than young adults? One reason infants and young children are perceived as being at increased risk is due to their limited cognitive capabilities, which makes them unable to state their willingness to participate in research or tell researchers when they would like to drop out of a study. For these reasons, infants and young children require special accommodations as they participate in the research process. Similar issues and accommodations would apply to adults who are deemed to be of limited cognitive capabilities.

When thinking about special accommodations in developmental research, consider the informed consent process. If you have ever participated in scientific research, you may know through your own experience that adults commonly sign an informed consent statement (a contract stating that they agree to participate in research) after learning about a study. As part of this process, participants are informed of the procedures to be used in the research, along with any expected risks or benefits. Infants and young children cannot verbally indicate their willingness to participate, much less understand the balance of potential risks and benefits. As such, researchers are oftentimes required to obtain written informed consent from the parent or legal guardian of the child participant, an adult who is almost always present as the study is conducted. In fact, children are not asked to indicate whether they would like to be involved in a study at all (a process known as assent) until they are approximately seven years old. Because infants and young children cannot easily indicate if they would like to discontinue their participation in a study, researchers must be sensitive to changes in the state of the participant (determining whether a child is too tired or upset to continue) as well as to parent desires (in some cases, parents might want to discontinue their involvement in the research). As in adult studies, researchers must always strive to protect the rights and well-being of the minor participants and their parents when conducting developmental research.

This video from the US Department of Health and Human Services provides an overview of the Institutional Review Board process.

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You can view the transcript for “How IRBs Protect Human Research Participants” here (opens in new window) .

Recruitment

An additional challenge in developmental science is participant recruitment. Recruiting university students to participate in adult studies is typically easy. Many colleges and universities offer extra credit for participation in research and have locations such as bulletin boards and school newspapers where research can be advertised. Unfortunately, young children cannot be recruited by making announcements in Introduction to Psychology courses, by posting ads on campuses, or through online platforms such as Amazon Mechanical Turk. Given these limitations, how do researchers go about finding infants and young children to be in their studies?

The answer to this question varies along multiple dimensions. Researchers must consider the number of participants they need and the financial resources available to them, among other things. Location may also be an important consideration. Researchers who need large numbers of infants and children may attempt to recruit them by obtaining infant birth records from the state, county, or province in which they reside. Some areas make this information publicly available for free, whereas birth records must be purchased in other areas (and in some locations birth records may be entirely unavailable as a recruitment tool). If birth records are available, researchers can use the obtained information to call families by phone or mail them letters describing possible research opportunities. All is not lost if this recruitment strategy is unavailable, however. Researchers can choose to pay a recruitment agency to contact and recruit families for them. Although these methods tend to be quick and effective, they can also be quite expensive. More economical recruitment options include posting advertisements and fliers in locations frequented by families, such as mommy-and-me classes, local malls, and preschools or daycare centers. Researchers can also utilize online social media outlets like Facebook, which allows users to post recruitment advertisements for a small fee. Of course, each of these different recruitment techniques requires IRB approval. And if children are recruited and/or tested in school settings, permission would need to be obtained ahead of time from teachers, schools, and school districts (as well as informed consent from parents or guardians).

And what about the recruitment of adults? While it is easy to recruit young college students to participate in research, some would argue that it is too easy and that college students are samples of convenience. They are not randomly selected from the wider population, and they may not represent all young adults in our society (this was particularly true in the past with certain cohorts, as college students tended to be mainly white males of high socioeconomic status). In fact, in the early research on aging, this type of convenience sample was compared with another type of convenience sample—young college students tended to be compared with residents of nursing homes! Fortunately, it didn’t take long for researchers to realize that older adults in nursing homes are not representative of the older population; they tend to be the oldest and sickest (physically and/or psychologically). Those initial studies probably painted an overly negative view of aging, as young adults in college were being compared to older adults who were not healthy, had not been in school nor taken tests in many decades, and probably did not graduate high school, let alone college. As we can see, recruitment and random sampling can be significant issues in research with adults, as well as infants and children. For instance, how and where would you recruit middle-aged adults to participate in your research?

Another important consideration when conducting research with infants and young children is attrition . Although attrition is quite common in longitudinal research in particular (see the previous section on longitudinal designs for an example of high attrition rates and selective attrition in lifespan developmental research), it is also problematic in developmental science more generally, as studies with infants and young children tend to have higher attrition rates than studies with adults. For example, high attrition rates in ERP (event-related potential, which is a technique to understand brain function) studies oftentimes result from the demands of the task: infants are required to sit still and have a tight, wet cap placed on their heads before watching still photographs on a computer screen in a dark, quiet room (Figure 5).

In other cases, attrition may be due to motivation (or a lack thereof). Whereas adults may be motivated to participate in research in order to receive money or extra course credit, infants and young children are not as easily enticed. In addition, infants and young children are more likely to tire easily, become fussy, and lose interest in the study procedures than are adults. For these reasons, research studies should be designed to be as short as possible – it is likely better to break up a large study into multiple short sessions rather than cram all of the tasks into one long visit to the lab. Researchers should also allow time for breaks in their study protocols so that infants can rest or have snacks as needed. Happy, comfortable participants provide the best data.

Conclusions

Lifespan development is a fascinating field of study – but care must be taken to ensure that researchers use appropriate methods to examine human behavior, use the correct experimental design to answer their questions, and be aware of the special challenges that are part-and-parcel of developmental research. After reading this module, you should have a solid understanding of these various issues and be ready to think more critically about research questions that interest you. For example, what types of questions do you have about lifespan development? What types of research would you like to conduct? Many interesting questions remain to be examined by future generations of developmental scientists – maybe you will make one of the next big discoveries!

An interactive H5P element has been excluded from this version of the text. You can view it online here: https://iastate.pressbooks.pub/individualfamilydevelopment/?p=158#h5p-25

• attrition : occurs when participants fail to complete all portions of a study
• cross-sectional research : used to examine behavior in participants of different ages who are tested at the same point in time; may confound age and cohort differences
• i nformed consent : a process of informing a research participant what to expect during a study, any risks involved, and the implications of the research, and then obtaining the person’s agreement to participate
• Institutional Review Boards (IRBs) : a panel of experts who review research proposals for any research to be conducted in association with the institution (for example, a university)
• longitudinal research : studying a group of people who may be of the same age and background (cohort), and measuring them repeatedly over a long period of time; may confound age and time of measurement effects
• research design : the strategy or blueprint for deciding how to collect and analyze information; dictates which methods are used and how
• selective attrition : certain groups of individuals may tend to drop out more frequently resulting in the remaining participants no longer being representative of the whole population
• sequential research design : combines aspects of cross-sectional and longitudinal designs, but also adding new cohorts at different times of measurement; allows for analyses to consider effects of age, cohort, time of measurement, and socio-historical change
• This chapter was adapted from Lumen Learning's Lifespan Development , created by Margaret Clark-Plaskie for Lumen Learning and adapted from Research Methods in Developmental Psychology by Angela Lukowski and Helen Milojevich for Noba Psychology, available under a Creative Commons NonCommercial Sharealike Attribution license . ↵
• Schaie, K. W. (1965). A general model for the study of developmental problems. Psychological Bulletin, 64 (2), 92-107. ↵
• Schaie, K.W. & Baltes, B.P. (1975). On sequential strategies in developmental research: Description or Explanation. Human Development, 18, 384-390. ↵

Encyclopedia of Science Education pp 1–4 Cite as

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• Koos Kortland 2  
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Introduction

Developmental research is a particular way of addressing the basic questions of why and how to teach what to whom. It involves a cyclical process of small-scale in-depth development and evaluation, at a content-specific level, of exemplary teaching-learning sequences. It aims to produce an empirically supported justification of the inner workings of such a sequence, which is claimed to be an important contribution to the expertise of teachers, curriculum developers, and educational researchers.

The Inner Workings of a Teaching-Learning Sequence

Two related elements are involved in the intended justification of a teaching-learning sequence about some topic. First, a detailed description of the desired (by the researcher) development in what students believe, intend to achieve, are pleased about, and so on, in relation to the topical contents. Second, a detailed explanation of why students’ beliefs, intentions, emotions, etc., can be expected to develop as described, given...

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Further Reading

Barab S, Squire K (eds) (2004) Special issue on design-based research. J Learn Sci 13(1)

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Gravemeijer KPE (1994) Developing realistic mathematics education. CDβ Press, Utrecht

Kortland K, Klaassen K (eds) (2010) Designing theory-based teaching-learning sequences for science education. CDβ Press, Utrecht. http://www.staff.science.uu.nl/~kortl101/book_sympPL.pdf

Méheut M, Psillos D (eds) (2004) Special issue on teaching-learning sequences in connection to the aims and tools for science education research. Int J Sci Educ 26(5)

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Klaassen, K., Kortland, K. (2013). Developmental Research. In: Gunstone, R. (eds) Encyclopedia of Science Education. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6165-0_155-1

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