research papers on clinical psychology

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• Published: 19 April 2021

A systematic review and meta-analysis of psychological interventions to improve mental wellbeing

• Joep van Agteren   ORCID: orcid.org/0000-0002-7347-7649 1 , 2 ,
• Matthew Iasiello   ORCID: orcid.org/0000-0003-1449-602X 1 , 2 , 3 ,
• Laura Lo 1 ,
• Jonathan Bartholomaeus 1 , 4 , 5 ,
• Zoe Kopsaftis   ORCID: orcid.org/0000-0002-9189-1405 6 , 7 , 8 ,
• Marissa Carey 1 &
• Michael Kyrios   ORCID: orcid.org/0000-0001-9438-9616 1 , 2 , 9  

Nature Human Behaviour volume  5 ,  pages 631–652 ( 2021 ) Cite this article

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• Disease prevention
• Medical humanities
• Outcomes research
• Quality of life

Our current understanding of the efficacy of psychological interventions in improving mental states of wellbeing is incomplete. This study aimed to overcome limitations of previous reviews by examining the efficacy of distinct types of psychological interventions, irrespective of their theoretical underpinning, and the impact of various moderators, in a unified systematic review and meta-analysis. Four-hundred-and-nineteen randomized controlled trials from clinical and non-clinical populations ( n  = 53,288) were identified for inclusion. Mindfulness-based and multi-component positive psychological interventions demonstrated the greatest efficacy in both clinical and non-clinical populations. Meta-analyses also found that singular positive psychological interventions, cognitive and behavioural therapy-based, acceptance and commitment therapy-based, and reminiscence interventions were impactful. Effect sizes were moderate at best, but differed according to target population and moderator, most notably intervention intensity. The evidence quality was generally low to moderate. While the evidence requires further advancement, the review provides insight into how psychological interventions can be designed to improve mental wellbeing.

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Data availability

The datasets that were used in this review are available from the corresponding author on reasonable request.

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Acknowledgements

The authors thank colleagues at the South Australian Health and Medical Research Institute, Wellbeing and Resilience Centre, for their support during the creation of this review, S. Brown and N. May, for their help in crafting the search strategy. This work was supported by a grant by the James and Diana Ramsay Foundation. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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J.v.A.: review methodology, screening of literature, data extraction, risk of bias, meta-analysis and writing. M.I.: review methodology, screening of literature, data extraction, risk of bias and writing. L.L.: screening of literature, data extraction, risk of bias and writing. J.B.: data extraction, risk of bias and writing. Z.K.: risk of bias and writing. M.C.: data extraction and writing. M.K.: input into methodology, focus of review and writing.

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van Agteren, J., Iasiello, M., Lo, L. et al. A systematic review and meta-analysis of psychological interventions to improve mental wellbeing. Nat Hum Behav 5 , 631–652 (2021). https://doi.org/10.1038/s41562-021-01093-w

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DOI : https://doi.org/10.1038/s41562-021-01093-w

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17 Meta-analysis in Clinical Psychology Research

Andy P. Field, School of Psychology, University of Sussex

• Published: 01 August 2013
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Meta-analysis is now the method of choice for assimilating research investigating the same question. This chapter is a nontechnical overview of the process of conducting meta-analysis in the context of clinical psychology. We begin with an overview of what meta-analysis aims to achieve. The process of conducting a meta-analysis is then described in six stages: (1) how to do a literature search; (2) how to decide which studies to include in the analysis (inclusion criteria); (3) how to calculate effect sizes for each study; (4) running a basic meta-analysis using the metaphor package for the free software R; (5) how to look for publication bias and moderators of effect sizes; and (6) how to write up the results for publication.

Introduction

Meta-analysis has become an increasingly popular research methodology, with an exponential increase in papers published seen across both social sciences and science in general. Field (2009) reports data showing that up until 1990 there were very few studies published on the topic of meta-analysis, but after this date the use of this tool has been on a meteoric increase. This trend has occurred in clinical psychology too. Figure 17.1 shows the number of articles with “meta-analysis” in the title published within the domain of “clinical psychology” since the term “meta-analysis” came into common usage. The data show a clear increase in publications after the 1990s, and a staggering acceleration in the number of published meta-analyses in this area in the past 3 to 5 years. Meta-analysis has been used to draw conclusions about the causes ( Bar-Haim, Lamy, Pergamin, Bakermans-Kranenburg, & van Ijzendoorn, 2007 ; Brewin, Kleiner, Vasterling, & Field, 2007 ; Burt, 2009 ; Chan, Xu, Heinrichs, Yu, & Wang, 2010 ; Kashdan, 2007 ; Ruocco, 2005 ), diagnosis ( Bloch, Landeros-Weisenberger, Rosario, Pittenger, & Leckman, 2008 ; Cuijpers, Li, Hofmann, & Andersson, 2010 ), and preferred treatments ( Barbato & D'Avanzo, 2008 ; Bradley, Greene, Russ, Dutra, & Westen, 2005 ; Cartwright-Hatton, Roberts, Chitsabesan, Fothergill, & Harrington, 2004 ; Covin, Ouimet, Seeds, & Dozois, 2008 ; Hendriks, Voshaar, Keijsers, Hoogduin, & van Balkom, 2008 ; Kleinstaeuber, Witthoeft, & Hiller, 2011 ; Malouff, Thorsteinsson, Rooke, Bhullar, & Schutte, 2008 ; Parsons & Rizzo, 2008 ; Roberts, Kitchiner, Kenardy, Bisson, & Psych, 2009 ; Rosa-Alcazar, Sanchez-Meca, Gomez-Conesa, & Marin-Martinez, 2008 ; Singh, Singh, Kar, & Chan, 2010 ; Spreckley & Boyd, 2009 ; Stewart & Chambless, 2009 ; Villeneuve, Potvin, Lesage, & Nicole, 2010 ) of a variety of mental health problems. This illustrative selection of articles shows that meta-analysis has been used to determine the efficacy of behavioral, cognitive, couple-based, cognitive-behavioral (CBT), virtual reality, and psychopharmacological interventions and on problems as diverse as schizophrenia, anxiety disorders, depression, chronic fatigue, personality disorders, and autism. There is little in the world of clinical psychology that has not been subjected to meta-analysis.

The number of studies using meta-analysis in clinical psychology.

This chapter provides a practical introduction to meta-analysis. For mathematical details, see other sources (e.g., H. M. Cooper, 2010 ; Field, 2001 , 2005a , 2009 ; Field & Gillett, 2010 ; Hedges & Olkin, 1985 ; Hedges & Vevea, 1998 ; Hunter & Schmidt, 2004 ; Overton, 1998 ; Rosenthal & DiMatteo, 2001 ; Schulze, 2004 ). This chapter overviews the important issues when conducting meta-analysis and shows an example of how to conduct a meta-analysis using the free software R ( R Development Core Team, 2010 ).

What Is Meta-analysis?

Clinical psychologists are typically interested in reaching conclusions that can be applied generally. These questions might whether CBT is efficacious as a treatment for obsessive-compulsive disorder ( Rosa-Alcazar et al., 2008 ), whether antidepressant medication treats the negative symptoms of schizophrenia ( Singh et al., 2010 ), whether virtual reality can be effective in treating specific phobias ( Parsons & Rizzo, 2008 ), whether school-based prevention programs reduce anxiety and/or depression in youth (Mychailyszyn, Brodman, Read, & Kendall, 2011), whether there are memory deficits for emotional information in posttraumatic stress disorder (PTSD; Brewin et al., 2007 ), what the magnitude of association between exposure to disasters and youth PTSD is ( Furr, Corner, Edmunds, & Kendall, 2010 ), or what the magnitude of threat-related attentional biases in anxious individuals is ( Bar-Haim, et al., 2007 ). Although answers to these questions may be attainable in a single study, single studies have two limitations: (1) they are at the mercy of their sample size because estimates of effects in small samples will be more biased than large sample studies and (2) replication is an important means to deal with the problems created by measurement error in research ( Fisher, 1935 ). Meta-analysis pools the results from similar studies in the hope of generating more accurate estimates of the true effect in the population. A meta-analysis can tell us:

The mean and variance of underlying population effects— for example, the effects in the population of conducting CBT with depressed adolescents compared to waitlist controls. You can also compute confidence intervals for the population effects.

Variability in effects across studies. It is possible to estimate the variability between effect sizes across studies (the homogeneity of effect sizes). There is accumulating evidence that effect sizes should be heterogeneous across studies (see, e.g., National Research Council, 1992 ). Therefore, variability statistics should be reported routinely. (You will often see significance tests reported for these estimates of variability; however, these tests typically have low power and are probably best ignored.)

Moderator variables. If there is variability in effect sizes, and in most cases there is ( Field, 2005a ), this variability can be explored in terms of moderator variables ( Field, 2003b ; Overton, 1998 ). For example, we might find that attentional biases to threat in anxious individuals are stronger when picture stimuli are used to measure these biases than when words are used.

A Bit of History

More than 70 years ago, Fisher and Pearson discussed ways to combine studies to find an overall probability ( Fisher, 1938 ; Pearson, 1938 ), and over 60 years ago, Stouffer presented a method for combining effect sizes ( Stouffer, 1949 ). The roots of meta-analysis are buried deep within the psychological and statistical earth. However, clinical psychology has some claim over the popularization of the method: in 1977, Smith and Glass published an influential paper in which they combined effects from 375 studies that had looked at the effects of psychotherapy ( Smith & Glass, 1977 ). They concluded that psychotherapy was effective, and that the type of psychotherapy did not matter. A year earlier, Glass (1976) published a paper in which he coined the term “meta-analysis” (if this wasn't the first usage of the term, then it was certainly one of the first) and summarized the basic principles. Shortly after these two seminal papers, Rosenthal published an influential theoretical paper on meta-analysis, and a meta-analysis combining 345 studies to show that interpersonal expectancies affected behavior ( Rosenthal, 1978 ; Rosenthal & Rubin, 1978 ). It is probably fair to say that these papers put “meta-analysis” in the spotlight of psychology. However, it was not until the early 1980s that three books were published by Rosenthal (1984 , 1991 ), Hedges and Olkin (1985) , and Hunter and Schmidt (1990) . These books were the first to provide detailed and accessible accounts of how to conduct a meta-analysis. Given a few years for researchers to assimilate these works, it is no surprise that the use and discussion of meta-analysis accelerated after 1990 (see Fig. 17.1 ). The even more dramatic acceleration in the number of published meta-analyses in the past 5 years is almost certainly due to the widespread availability of computer software packages that make the job of meta-analysis easier than before.

Computer Software for Doing Meta-analysis

An overview of the options.

There are several standalone packages for conducting meta-analyses: for example, the Cochrane Collaboration's Review Manager (RevMan) software ( The Cochrane Collaboration, 2008 ). There is also a package called Comprehensive Meta-Analysis ( Borenstein, Hedges, Higgins, & Rothstein, 2005 ). There are two add-ins for Microsoft Excel: Mix ( Bax, Yu, Ikeda, Tsuruta, & Moons, 2006 ) and MetaEasy ( Kontopantelis & Reeves, 2009 ). These packages implement many different meta-analysis methods, convert effect sizes, and create plots of study effects. Although it is not 100 percent clear from their website, Comprehensive Meta-Analysis appears to be available only for Windows, Mix works only with Excel 2007 and 2010 in Windows, and MetaEasy works with Excel 2007 (again Windows). RevMan uses Java and so is available for Windows, Linux, and MacOS operating systems. Although RevMan and MetaEasy are free and Mix comes in a free light version, Comprehensive Meta-Analysis and the pro version of Mix are commercial products.

SPSS (a commercial statistics package commonly used by clinical psychologists) does not incorporate a menu-driven option for conducting meta-analysis, but it is possible to use its syntax to run a meta-analysis. Field and Gillett (2010) provide a tutorial on meta-analysis and also include syntax files and examples showing how to run a meta-analysis using SPSS. Other SPSS syntax files can be obtained from Lavesque (2001) and Wilson (2004) .

Meta-analysis can also be conducted with R ( R Development Core Team, 2010 ), a freely available package for conducting a staggering array of statistical procedures. R is free, open-source software available for Windows, MacOS, and Linux that is growing in popularity in the psychology community. Scripts for running a variety of meta-analysis procedures on d are available in the meta package that can be installed into R ( Schwarzer, 2005 ). However, my favorite package for conducting meta-analysis in R is metafor ( Viechtbauer, 2010 ) because it has functions to compute effect sizes from raw data, can work with a wide array of different effect sizes ( d, r , odds ratios, relative risks, risk differences, proportions, and incidence rates), produces publication-standard graphics, and implements moderator analysis and fixed- and random-effects methods (more on this later). It is a brilliant package, and given that it can be used for free across Windows, Linux, and MacOS, I have based this chapter on using this package within R .

Getting Started with R

R ( R Development Core Team, 2010 ) is an environment/language for statistical analysis and is the fastest-growing statistics software. R is a command language: we type in commands that we then execute to see the results. Clinical psychologists are likely to be familiar with the point-and-click graphical user interfaces (GUIs) of packages like SPSS and Excel, and so at first R might appear bewildering. However, I will walk through the process step by step assuming that the reader has no knowledge of R . I cannot, obviously, explain everything there is to know about R , and readers are advised to become familiar with the software by reading one of the many good introductory books (e.g., Crawley, 2007 ; Quick, 2010 ; Verzani, 2004 ; Zuur, Ieno, & Meesters, 2009 ), the best of which, in my entirely objective opinion, is Field, Miles, and Field (2012) .

Once you have installed R on your computer and opened the software, you will see the console window, which contains a prompt at which you type commands. Once a command has been typed, you press the return key to execute it. You can also write commands in a script window and execute them from there, which is my preference—see Chapter 3 of Field and colleagues (2012) . R comes with a basic set of functionality, which can be expanded by installing packages stored at a central online location that has mirrors around the globe. To install the metafor package you need to execute this command:

install.packages(“metafor”)

This command installs the package (you need to be connected to the Internet for this command to work). You need to install the package only once (although whenever you update R you will have to reinstall it), but you need to load the package every time that you want to use it. You do this by executing this command:

library(metafor)

The library() function tells R that you want to use a package (in this case metafor ) in the current session. If you close the program and restart it, then you would need to re-execute the library command to use the metafor package.

The Six Basic Steps of Meta-analysis: An Example

Broadly speaking, there are six sequential steps to conducting a quality meta-analysis: (1) Do a literature search; (2) Decide on inclusion criteria; (3) Calculate the effect sizes; (4) Do the basic meta-analysis; (5) Do some more advanced analysis; and (6) Write it up. In this chapter, to illustrate these six steps, we will use a real dataset from a meta-analysis in which I was involved ( Hanrahan, Field, Jones, & Davey, 2013 ). This meta-analysis looked at the efficacy of cognitive-based treatments for worry in generalized anxiety disorder (GAD), and the part of the analysis that we will use here simply aimed to estimate the efficacy of treatment postintervention and to see whether the type of control group used moderated the effects obtained. This meta-analysis is representative of clinical research in that relatively few studies had addressed this question (it is a small analysis) and sample sizes within each study were relatively small. These data are used as our main example, and the most benefit can be gained from reading the original meta-analysis in conjunction with this chapter. We will now look at each stage of the process of doing a meta-analysis.

Step 1: Do a Literature Search

The first step is to search the literature for studies that have addressed the core/central/same research question using electronic databases such as the ISI Web of Knowledge, PubMed, and PsycInfo. Although the obvious reason for doing this is to find articles, it is also helpful in identifying authors who might have unpublished data (see below). It is often useful to hand-search the reference sections of the articles that you have found to check for articles that you have missed, and to consult directly with noted experts in this literature to ensure that relevant papers have not been missed.

Although it is tempting to assume that meta-analysis is a wonderfully objective tool, it is not without a dash of bias. Possibly the main source of bias is the “file-drawer problem,” or publication bias ( Rosenthal, 1979 ). This bias stems from the reality that significant findings are more likely to be published than nonsignificant findings: significant findings are estimated to be eight times more likely to be submitted than nonsignificant ones ( Greenwald, 1975 ), studies with positive findings are around seven times more likely to be published than studies with results supporting the null hypothesis ( Coursol & Wagner, 1986 ), and 97 percent of articles in psychology journals report significant results ( Sterling, 1959 ). Without rigorous attempts to counteract publication bias, meta-analytic reviews could overestimate population effects because effect sizes in unpublished studies will be smaller ( McLeod & Weisz, 2004 )—up to half the size ( Shadish, 1992 )—of published studies of comparable methodological quality. The best way to minimize the bias is to extend your search to relevant conference proceedings and to contact experts in the field to see if they have or know of any unpublished data. This can be done by direct email to authors in the field, but also by posting a message to a topic-specific newsgroup or email listserv.

In our study, we gathered articles by searching PsycInfo, Web of Science, and Medline for English-language studies using keywords considered relevant. The reference lists of previous meta-analyses and retrieved articles were scanned for relevant studies. Finally, email addresses of published researchers were compiled from retrieved papers, and 52 researchers were emailed and invited to send any unpublished data fitting the inclusion criteria. This search strategy highlights the use of varied resources to ensure that all potentially relevant studies are included and to reduce bias due to the file-drawer problem.

Step 2: Decide on Inclusion Criteria

A second source of bias in a meta-analysis is the inclusion of poorly conducted research. As Field and Gillett (2010) put it:

Although meta-analysis might seem to solve the problem of variance in study quality because these differences will “come out in the wash,” even one red sock (bad study) amongst the white clothes (good studies) can ruin the laundry. (pp. 667–668)

Inclusion criteria depend on the research question being addressed and any specific methodological issues in the field, but the guiding principle is that you want to compare apples with apples, not apples with pears ( Eysenck, 1978 ). In a meta-analysis of CBT, for example, you might decide on a working definition of what constitutes CBT, and maybe exclude studies that do not have proper control groups. It is important to use a precise, reliable, set of criteria that is applied consistently to each potential study so as not to introduce subjective bias into the analysis. In your write-up, you should be explicit about your inclusion criteria and report the number of studies that were excluded at each step of the selection process.

In our analysis, at a first pass we excluded studies based on the following criteria: (a) treatments were considered too distinct to be meaningfully compared to face-to-face therapies (e.g., bibliotherapy, telephone, or computer-administered treatment); (b) subsamples of the data were already included in the meta-analysis because they were published over several papers; and (c) information was insufficient to enable us to compute effect sizes. Within this pool of studies, we set the following inclusion criteria:

Studies that included only those participants who met criteria for a diagnosis of GAD outlined by the DSM since GAD was recognized as an independent disorder; that is, the DSM-III-R, DSM-IV, or DSM-IV-TR (prior to DSM-III-R, GAD was simply a poorly characterized residual diagnostic category). This was to avoid samples being heterogeneous.

Studies in which the majority of participants were aged 18 to 65 years. This was because there may be developmental issues that affect the efficacy of therapy in younger samples.

The Penn State Worry Questionnaire (PSWQ) was used to capture symptom change.

Treatments included were defined as any treatment that used cognitive techniques, either in combination with, or without, behavioral techniques.

To ensure that the highest possible quality of data was included, only studies that used a randomized controlled design were included.

Step 3: Calculate the Effect Sizes

What are effect sizes and how do i calculate them.

Your selected studies are likely to have used different outcome measures, and of course we cannot directly compare raw change on a children's self-report inventory to that being measured using a diagnostic tool such as the Anxiety Disorders Interview Schedule (ADIS). Therefore, we need to standardize the effects within each study so that they can be combined and compared. To do this we convert each effect in each study into one of many standard effect size measures. When quantifying group differences on a continuous measure (such as the PSWQ) people tend to favor Cohen's d ; Pearson's r is used more when looking at associations between measures; and if recovery rates are the primary interest, then it is common to see odds ratios used as the effect size measure.

Once an effect size measure is chosen, you need to compute it for each effect that you want to compare for every paper you want to include in the meta-analysis. A given paper may contain several effect sizes depending on the sorts of questions you are trying to address with your meta-analysis. For example, in a meta-analysis on cognitive impairment in PTSD in which I was involved ( Brewin et al., 2007 ), impairment was measured in a variety of ways in individual studies, and so we had to compute several effect sizes within many of the studies. In this situation, we have to make a decision about how to treat the studies that have produced multiple effect sizes that address the same question. A common solution is to calculate the average effect size across all measures of the same outcome within a study ( Rosenthal, 1991 ), so that every study contributes only one effect to the main analysis (as in Brewin et al., 2007 ).

Computing effect sizes is probably the hardest part of a meta-analysis because the data contained within published articles will vary in their detail and specificity. Some articles will report effect sizes, but many will not; articles might use different effect size metrics; you will feel as though some studies have a grudge against you and are trying to make it as hard for you as possible to extract an effect size. If no effect sizes are reported, then you need to try to use the reported data to calculate one. If using d , then you can use means and standard deviations, odds ratios are easily obtained from frequency data, and most effect size measures (including r ) can be obtained from test statistics such as t , z , χ 2 , and F , or probability values for effects (by converting first to z ). A full description of the various ways in which effect sizes can be computed is beyond the present scope, but there are many freely available means to compute effect sizes from raw data and test statistics; some examples are Wilson (2001 , 2004 ) and DeCoster (1998) . To do the meta-analysis you need not just the effect size, but the corresponding value of its sampling variance ( v ) or standard error ( se ); Wilson (2001) , for example, will give you an estimate of the effect size and the sampling variance.

If a paper does not include sufficient data to calculate an effect size, contact the authors for the raw data, or relevant statistics from which an effect size can be computed. (If you are on the receiving end of such an email please be sympathetic, as attempts to get data out of researchers can be like climbing up a jelly mountain.)

Effect Sizes for Hanrahan and Colleagues’ Study

When reporting a meta-analysis it is a good idea to tabulate the effect sizes with other helpful information (such as the sample size on which the effect size is based, N ) and also to present a stem-and-leaf plot of the effect sizes. For the study conducted by Hanrahan and colleagues, we used d as the effect size measure and corrected for the known bias that d has in small samples using the adjustment described

by Hedges (1981) . In meta-analysis, a stem-and-leaf plot graphically organizes included effect sizes to visualize the shape and central tendency of the effect size distribution across studies included. Table 17.1 shows a stem-and-leaf plot of the resulting effect sizes, and this should be included in the write-up. This stem-and-leaf plot tells us the effect sizes to one decimal place, with the stem reflecting the value before the decimal point and the leaf showing the first decimal place; for example, we know the smallest effect size was d = −0.2, the largest was d = −3.2, and there were effect sizes of 1.2 and 1.4 (for example). Table 17.2 shows the studies included in the Hanrahan and colleagues’ paper, with their corresponding effect sizes (expressed as d ), the sample sizes on which these d s are based, and the standard errors associated with each effect size. Note that the d s match those reported in Table 2 of Hanrahan and colleagues (2013) .

Step 4: Do the Basic Meta-Analysis

Initial considerations.

Meta-analysis aims to estimate the effect in the population (and a confidence interval around it) by combining the effect sizes from different studies using a weighted mean of the effect sizes. The “weight” that is used is usually a value reflecting the sampling precision of the effect size, which is typically a function of sample size. As such, effect sizes with better precision are weighted more highly than effect sizes that are imprecise. There are different methods for estimating the population effects, and these methods have pros and cons. There are two related issues to consider: (1) which method to use and (2) how to conceptualize your data. There are other issues, too, but we will focus on these two because there are articles elsewhere that can be consulted as a next step (e.g., Field, 2001 , 2003a , 2003b , 2005a , 2005b ; Hall & Brannick, 2002 ; Hunter & Schmidt, 2004 ; Rosenthal & DiMatteo, 2001 ; Schulze, 2004 ).

Choosing a Model

It is tempting simply to tell you to use a random-effects model and end the discussion: however, in the interests of informed decision making I will explain why. Meta-analysis can be conceptualized in two ways: fixed- and random-effects models ( Hedges, 1992 ; Hedges & Vevea, 1998 ; Hunter & Schmidt, 2000 ). We can assume that studies in a meta-analysis are sampled from a population in which the average effect size is fixed ( Hunter & Schmidt, 2000 ). Consequently, sample effect sizes should be homogenous. This is the fixed-effects model. The alternative assumption is that the average effect size in the population varies randomly from study to study: population effect sizes can be thought of as being sampled from a “superpopulation” ( Hedges, 1992 ). In this case, because effect sizes come from populations with varying average effect sizes, they should be heterogeneous. This is the random-effects model. Essentially, the researcher using a random-effects model assumes that the studies included represent a mere random sampling of the larger population of studies that could have been conducted on the topic, whereas the researcher using a fixed-effects model assumes that the studies included are the comprehensive set of representative studies. Therefore, the fixed-effects model can be thought to characterize the scope of existing research, and the random-effects model can be thought to afford inferences about a broader population than just the sample of studies analyzed. When effect size variability is explained by a moderator variable that is treated as “fixed,” then the random-effects model becomes a mixed-effects model (see Overton, 1998 ).

Statistically speaking, fixed- and random-effects models differ in the sources of error. Fixed-effects models have error derived from sampling studies from a population of studies. Random-effects models have this error too, but in addition there is error created by sampling the populations from a superpopulation.

The two most widely used methods of meta-analysis are those by Hunter and Schmidt (2004) , which is a random-effects method, and the method by Hedges and colleagues (e.g., Hedges, 1992 ; Hedges & Olkin, 1985 ; Hedges & Vevea, 1998 ), who provide both fixed- and random-effects methods. However, multilevel models can also be used in the context of meta-analysis (see Hox, 2002 , Chapter 8).

Your first decision is whether to conceptualize your model as fixed- or random-effects. You might consider the assumptions that can be realistically made about the populations from which your studies are sampled. There is compelling evidence that real-world data in the social sciences are likely to have variable population parameters ( Field, 2003b ; Hunter & Schmidt, 2000 , 2004 ; National Research Council, 1992 ; Osburn & Callender, 1992 ). Field (2005a) found that the standard deviations of effect sizes for all meta-analytic studies (using r ) published in Psychological Bulletin from 1997 to 2002 ranged from 0 to 0.3 and were most frequently in the region of 0.10 to 0.16; similarly, Barrick and Mount (1991) reported that the standard deviation of effect sizes ( r s) in published datasets was around 0.16.

Second, consider the inferences that you wish to make ( Hedges & Vevea, 1998 ): if you want to make inferences that extend only to the studies included in the meta-analysis ( conditional inferences ), then fixed-effect models are appropriate; however, for inferences that generalize beyond the studies in the meta-analysis ( unconditional inferences ), a random-effects model is appropriate.

Third, consider the consequences of making the “wrong” choice. The consequences of applying fixed-effects methods to random-effects data can be quite dramatic: (1) it inflates the significance tests of the estimate of the population effect from the normal 5 percent to 11 to 28 percent ( Hunter & Schmidt, 2000 ) and 43 to 80 percent ( Field, 2003b ) and (2) published fixed-effects confidence intervals around mean effect sizes have been shown to be, on average, 52 percent narrower than their actual width—these nominal 95 percent fixed-effects confidence intervals were on average 56 percent confidence intervals ( Schmidt, Oh, & Hayes, 2009 ). The consequences of applying random-effects methods to fixed-effects data are considerably less dramatic: in Hedges’ method, for example, when sample effect sizes are homogenous, the additional between-study effect size variance becomes zero, yielding the same result as the fixed-effects method.

This leads me neatly back to my opening sentence of this section: unless you can find a good reason not to, use a random-effects method because (1) social science data normally have heterogeneous effect sizes; (2) psychologists generally want to make inferences that extend beyond the studies in the meta-analysis; and (3) if you apply a random-effects method to homogenous effect sizes, it does not affect the results (certainly not as dramatically as if you apply a fixed-effects model to heterogeneous effect sizes).

Choosing a Method

Let's assume that you trust me (I have an honest face) and opt for a random-effects model. You then need to decide whether to use Hunter and Schmidt, H-S (2004) or Hedges and colleagues’ method (H-V). The technical differences between these methods have been summarized elsewhere ( Field, 2005a ) and will not be repeated here. In a series of Monte Carlo simulations comparing the performance of the Hunter and Schmidt and Hedges and Vevea (fixed- and random-effects) methods, Field (2001 ; but see Hafdahl & Williams, 2009 ) found that when comparing random-effects methods, the Hunter-Schmidt method yielded the most accurate estimates of population correlations across a variety of situations (a view echoed by Hall & Brannick, 2002 , in a similar study). Based on a more extensive set of stimulations, Field (2005a) concluded that in general both H-V and H-S random-effects methods produce accurate estimates of the population effect size. Although there were subtle differences in the accuracy of population effect size estimates across the two methods, in practical terms the bias in both methods was negligible. In terms of 95 percent confidence intervals around the population estimate, Hedges’ method was in general better at achieving these intervals (the intervals for Hunter and Schmidt's method tended to be too narrow, probably because they recommend using credibility intervals and not confidence intervals).

Hunter and Schmidt's method involves psychometric corrections for the attenuation of observed effect sizes that can be caused by measurement error ( Hunter, Schmidt, & Le, 2006 ), and these psychometric corrections can be incorporated into the H-V method if correlations are used as the effect size, but these corrections were not explored in the studies mentioned above, which limits what they can tell us. Therefore, diligent researchers might consult the various tables in Field (2005a) to assess which method might be most accurate for the given parameters of the meta-analysis that they are about to conduct; however, the small differences between the methods will probably not make a substantive impact on the conclusions that will be drawn from the analysis.

Entering the Data into R

Having computed the effect sizes, we need to enter these into R . In R , commands follow the basic structure of:

Object 〈- Instructions about how to create the object

Therefore, to create an object that is a variable, we give it a name on the left-hand side of the arrow, and on the right-hand side input the data that makes up the variable. To input data we use the c() function, which simply binds things together into a single object (in this case it binds the different values of d into a single object or variable). To enter the value of d from Table 17.2 , we would execute:

d 〈- c(1.42, 0.68, −0.17, 2.57, 0.82, 0.13, 0.45, 0.31, 2.44, 3.22, −0.08, 2.25, 0.89, 1.23, 0.27, 2.22, 0.31, 0.83, 0.26)

Executing this command creates an object that we have named “d” (we could have named it “Thelma” if we wanted to, but “d” seems like a fairly descriptive name in the circumstances). If we want to view this variable, we simply execute its name:

〉d [1] 1.42 0.68 −0.17 2.57 0.82 0.13 0.45 0.31 2.44 3.22 −0.08 2.25 0.89 1.23 0.27 2.22 0.31 0.83 0.26

We can enter the standard errors from Table 17.2 in a similar way; this time we create an object that we have decided to call “sed”:

sed 〈- c(0.278, 0.186, 0.218, 0.590, 0.313, 0.293, 0.230, 0.263, 0.468, 0.609, 0.394, 0.587, 0.284, 0.299, 0.291, 0.490, 0.343, 0.336, 0.323)

Next I'm going to create a variable that gives each effect size a label of the first author of the study from which the effect came and the year. We can do this by executing (note that to enter text strings instead of numbers, we place the text in quotes so that R knows the data are text strings):

study 〈-c(“v.d. Heiden (2010)”, “v.d. Heiden (2010)”, “Newman (2010)”, “Wells (2010)”, “Dugas (2010)”, “Dugas (2010)”, “Westra (2009)”, “Leichsenring (2009)”, “Roemer (2008)”, “Rezvan (2008)”, “Rezvan (2008)”, “Zinbarg (2007)”, “Gosselin (2006)”, “Dugas (2003)”, “Borkovec (2002)”, “Ladouceur (2000)”, “Ost (2000)”, “Borkovec (1993)”, “Borkovec (1993)”)

We also have some information about the type of control group used. We'll come back to this later, but if we want to record this information, we can do so using a coding variable. We need to enter values that represent the different types of control group, and then to tell R what these values represent. Let's imagine that we want to code non-therapy controls as 0, CT as 1, and non-CT as 2. First we can enter these values into R :

controlType 〈- c(0, 1, 1, 2, 0, 2, 1, 2, 0, 0, 1, 0, 2, 0, 1, 0, 2, 2, 2)

Next, we need to tell R that this variable is a coding variable (a.k.a. a factor), using the factor() function. Within this function we name the variable that we want to convert (in this case controlType ), we tell R what numerical values we have used to code levels of the factor by specifying levels = 0:2 (0:2 means zero to 2 inclusive, so we are specifying levels of 0, 1, 2), we then tell it what labels to attach to those levels (in the order of the numerical codes) by including labels = c( “Non-Therapy”, “CT”, “Non-CT”) . Therefore, to turn controlType into a factor based on itself, we execute:

controlType 〈- factor(controlType, levels = 0:2, labels = c(“Non-Therapy”, “CT”, “Non-CT”))

We now have four variables containing data: d (the effect sizes), sed (their standard errors), study (a string variable that identifies the study from which the effect came), and controlType (a categorical variable that defines what control group was used for each effect size). We can combine these variables into a data frame by executing:

GAD.data 〈- data.frame(study, controlType, d, sed)

This creates an object called GAD.data (note that in R you cannot use spaces when you name objects) that is a data frame made up of the four variables that we have just created. To “see” this data frame, execute its name:

〉 GAD.data study controlType d sed 1 v.d. Heiden (2010) Non-Therapy 1.42 0.278 2 v.d. Heiden (2010) CT 0.68 0.186 3 Newman (2010) CT -0.17 0.218 4 Wells (2010) Non-CT 2.57 0.590 5 Dugas (2010) Non-Therapy 0.82 0.313 6 Dugas (2010) Non-CT 0.13 0.293 7 Westra (2009) CT 0.45 0.230 8 Leichsenring (2009) Non-CT 0.31 0.263 9 Roemer (2008) Non-Therapy 2.44 0.468 10 Rezvan (2008) Non-Therapy 3.22 0.609 11 Rezvan (2008) CT -0.08 0.394 12 Zinbarg (2007) Non-Therapy 2.25 0.587 13 Gosselin (2006) Non-CT 0.89 0.284 14 Dugas (2003) Non-Therapy 1.23 0.299 15 Borkovec (2002) CT 0.27 0.291 16 Ladouceur (2000) Non-Therapy 2.22 0.490 17 Ost (2000) Non-CT 0.31 0.343 18 Borkovec (1993) Non-CT 0.83 0.336 19 Borkovec (1993) Non-CT 0.26 0.323

You can also prepare the data as a tab-delimited or comma-separated text file (using Excel, SPSS, Stata, or whatever other software you like) and read this file into R using the read.delim() or read.csv() functions. In both cases, you could use the choose.file() function to open a standard dialogue box that lets you choose the file by navigating your file system. 1 For example, to create a data frame called “GAD.data” from a tab-delimited file (.dat), you would execute:

GAD.data 〈 - read.delim(file.choose(), header = TRUE)

Similarly, to create a data frame from a comma-separated text file, you would execute:

GAD.data 〈- read.csv(file.choose(), header = TRUE)

In both cases the header = TRUE option is used if you have variable names in the first row of the data file; if you do not have variable names in the file, omit this option (the default value is false). If this data-entry section has confused you, then read Chapter 3 of Field and colleagues (2012) or your preferred introductory book on R .

Doing the Meta-analysis

To do a basic meta-analysis you use the rma() function. This function has the following general format when using the standard error of effect sizes:

maModel 〈- rma(yi = variable containing effect sizes , sei = variable containing standard error of effect sizes , data = dataFrame , method = “DL”)

maModel is whatever name you want to give your model (but remember you can't use spaces), variable containing effect sizes is replaced with the name of the variable containing your effect sizes, variable containing standard error of effect sizes is replaced with the name of the variable that contains the standard errors, and dataFrame is the name of the data frame containing these variables.

When using the variance of effect sizes we substitute the sei option with vi :

maModel 〈- rma(yi = variable containing effect sizes , vi = variable containing variance of effect sizes , data = dataFrame , method = “DL”)

Therefore, for our GAD analysis, we can execute:

maGAD 〈- rma(yi = d, sei = sed, data = GAD.data, method = “DL”)

This creates an object called maGAD by using the rma() function. Within this function, we have told R that we want to use the object GAD.data as our data frame, and that within this data frame, the variable d contains the effect sizes ( yi = d ) and the variable sed contains the standard errors ( sei = sed ). Finally, we have set the method to be “DL,” which will use the DerSimonian-Laird estimator (which is used in the H-V random-effects method). We can change how the model is estimated by changing this option, which can be set to the following:

method = “FE”: produces a fixed-effects meta-analysis

method = “HS” = random effects using the Hunter-Schmidt estimator

method = “HE” = random effects using the Hedges estimator

method = “DL” = random effects using the DerSimonian-Laird estimator

method = “SJ” = random effects using the Sidik-Jonkman estimator

method = “ML” = random effects using the maximum-likelihood estimator

method = “REML” = random effects using the restricted maximum-likelihood estimator (this is the default if you don't specify a method at all)

method = “EB” = random effects using the empirical Bayes estimator.

To see the results of the analysis we need to use the summary() function and put the name of the model within it:

summary(maGAD)

The resulting output can be seen in Figure 17.2 . This output is fairly self-explanatory 2 . For example, we can see that for Hedges and Vevea's method, the Q statistic, which measures heterogeneity in effect sizes, is highly significant, χ 2 (18) = 100.50, p 〈 .001. The estimate of between-study variability, τ 2 = 0.44 (most important, this is not zero), and the proportion of variability due to heterogeneity, I   2 , was 82.09 percent. In other words, there was a lot of variability in study effects. The population effect size and its 95 percent confidence interval are: 0.93 (CI .95 = 0.59 (lower), 1.27 (upper)). We can also see that this population effect size is significant, z = 5.40, p 〈 .001.

R output for a basic meta-analysis.

Based on the homogeneity estimates and tests, we could say that there was considerable variation in effect sizes overall. Also, based on the estimate of population effect size and its confidence interval, we could conclude that there was a strong effect of CT for GAD compared to controls.

Creating a forest plot of the studies and their effect sizes is very easy after having created the meta-analysis model. We simply place the name of the model within the forest() command and execute:

forest(maGAD)

However, I want to add the study labels to the plot, so let's execute:

forest(maGAD, slab = GAD.data$study)

By adding slab = GAD.data$study to the command we introduce study labels (that's what slab stands for) and the labels we use are in the variable called study within the GAD.data data frame (that's what GAD.data$study means). The resulting figure is in Figure 17.3 . It shows each study with a square indicating the effect size from that study (the size of the square is proportional to the weight used in the meta-analysis, so we can see that the first three studies were weighted fairly heavily). The branches of each effect size represent the confidence interval of the effect size. Also note that because we added the slab option, our effects have been annotated using the names in the variable called study in our data frame. Looking at this plot, we can see that there are five studies that produced fairly substantially bigger effects than the rest, and two studies with effect sizes below zero (the dotted line), which therefore showed that CBT was worse than controls. The diamond at the bottom shows the population effect size based on these individual studies (it is the value of the population effect size from our analysis). The forest plot is a very useful way to summarize the studies in the meta-analysis.

Step 5: Do Some More Advanced Analysis

Estimating publication bias.

Various techniques have been developed to estimate the effect of publication bias and to correct for it. The earliest and most commonly reported estimate of publication bias is Rosenthal's (1979) fail-safe N . This was an elegant and easily understood method for estimating the number of unpublished studies that would need to exist to turn a significant population effect size estimate into a nonsignificant one. However, because significance testing the estimate of the population effect size is not really the reason for doing a meta-analysis, the fail-safe N is fairly limited.

The funnel plot ( Light & Pillerner, 1984 ) is a simple and effective graphical technique for exploring potential publication bias. A funnel plot displays effect sizes plotted against the sample size, standard error, conditional variance, or some other measure of the precision of the estimate. An unbiased sample would ideally show a cloud of data points that is symmetrical around the population effect size and has the shape of a funnel. This funnel shape reflects the greater variability in effect sizes from studies with small sample sizes/less precision, and the estimates drawn from larger/more precise studies converging around the population effect size. A sample with publication bias will lack symmetry because studies based on small samples that showed small effects will be less likely to be published than studies based on the same-sized samples that showed larger effects ( Macaskill, Walter, & Irwig, 2001 ).

Forest plot of the GAD data.

Funnel plots should be used as a first step before further analysis because factors other than publication bias can cause asymmetry. Some examples are data irregularities including fraud and poor study design ( Egger, Smith, Schneider, & Minder, 1997 ), true heterogeneity of effect sizes (in intervention studies this can happen because the intervention is more intensely delivered in smaller, more personalized studies), and English-language bias (studies with smaller effects are often found in non–English-language journals and get overlooked in the literature search).

To get a funnel plot for a meta-analysis model created in R , we simply place that model into the funnel() function and execute:

funnel(maGAD)

Figure 17.4 shows the resulting funnel plot, which is clearly not symmetrical. The studies with large standard errors (bottom right) consistently produce the largest effect sizes, and the studies are not evenly distributed around the mean effect size (or within the unshaded triangle). This graph shows clear publication bias.

Funnel plots offer no means to correct for any bias detected. Trim and fill ( Duval & Tweedie, 2000 ) is a method in which a biased funnel plot is truncated (“trimmed”) and the number ( k ) of missing studies from the truncated part is estimated. Next, k artificial studies are added (“filled”) to the negative side of the funnel plot (and therefore have small effect sizes) so that in effect the study now contains k new “studies” with effect sizes as small in magnitude as the k largest effect sizes that were trimmed. The new “filled” effects are presumed to represent the magnitude of effects identified in hypothetical unpublished studies. A new estimate of the population effect size is then calculated including these artificially small effect sizes. Vevea and Woods (2005) point out that this method can lead to overcorrection because it relies on the strict assumption that all of the “missing” studies are those with the smallest effect sizes. Vevea and Woods propose a more sophisticated correction method based on weight function models of publication bias. These methods use weights to model the process through which the likelihood of a study being published varies (usually based on a criterion such as the significance of a study). Their method can be applied to even small meta-analyses and is relatively flexible in allowing meta-analysts to specify the likely conditions of publication bias in their particular research scenario. (The downside of this flexibility is that it can be hard to know what the precise conditions are.) They specify four typical weight functions: “moderate one-tailed selection,” “severe one-tailed selection,” “moderate two-tailed selection,” and “severe two-tailed selection”; however, they recommend adapting the weight functions based on what the funnel plot reveals (see Vevea & Woods, 2005 ). These corrections can be applied in R (see Field & Gillett, 2010 , for a tutorial) but do not form part of the metafor package and are a little too technical for this introductory chapter.

Funnel plot of the GAD data.

Moderator Analysis

When there is variability in effect sizes, it can be useful to try to explain this variability using theoretically driven predictors of effect sizes. For example, in our dataset there were three different types of control group used: non-therapy (waitlist), non-CT therapy, and CT therapy. We might reasonably expect effects to be stronger if a waitlist control was used in the study compared to a CT control because the waitlist control gets no treatment at all, whereas CT controls get some treatment. We can test for this using a mixed model (i.e., a random-effects model in which we add a fixed effect).

Moderator models assume a general linear model in which each effect size can be predicted from the moderator effect (represented by β 1 ):

The within-study error variance is represented by e i . To calculate the moderator effect, β 1 , a generalized least squares (GLS) estimate is calculated. It is not necessary to know the mathematics behind the process (if you are interested, then read Field, 2003b ; Overton, 1998 ); the main thing to understand is that we're just doing a regression in which effect sizes are predicted. Like any form of regression we can, therefore, predict effect sizes from either continuous variables (such as study quality) or categorical ones (which will be dummy coded using contrast weights).

The package metafor allows both continuous and categorical predictors (moderators) to be entered into the regression model that a researcher wishes to test. Moderator variables are added by including the mods option to the basic meta-analysis command. You can enter a single moderator by specifying mods = variableName (in which variableName is the name of the moderator variable that you want to enter into the model) or enter several moderator variables by including mods = matrixName (in which matrixName is the name of a matrix that contains values of moderator variables in each of its columns). Continuous variables are treated as they are; for categorical variables, you should either dummy code them manually or use the factor() function, as we did earlier, in which case R does the dummy coding for you.

Therefore, in our example, we can add the variable controlType as a moderator by rerunning the model including mods = controlType into the command. This variable is categorical, but because we converted it to a factor earlier on, R will treat it as a dummy-coded categorical variable. The rest of the command is identical to before:

modGAD 〈- rma(yi = d, sei = sed, data = GAD.data, mods = controlType, method = “DL”) summary(modGAD)

The resulting output is shown in Figure 17.5 . This output is fairly self-explanatory; for example, we can see that for Hedges and Vevea's method, the estimate of between-study variability, χ 2 = 0.33, is less than it was before (it was 0.44), which means that our moderator variable has explained some variance. However, there is still a significant amount left to explain, χ 2 (17) = 76.35, p 〈 .001.

Output from R for moderation analysis.

The Q statistic shows that the amount of variance explained by controlType is highly significant, χ 2 (1) = 8.93, p =.0028. In other words, it is a significant predictor of effect sizes. The beta parameter for the moderator and its 95 percent confidence interval are: −0.55, CI .95 = −0.92 (lower), −0.19 (upper). We can also see that this parameter is significant, z = −3.11, p = .0028 (note that the p value is identical to the Q statistic because they're testing the same thing). In a nutshell, then, the type of control group had a significant impact on the effect that CT had on GAD (measured by the PSWQ). We could break this effect apart by running the main meta-analysis on the three control groups separately.

Step 6: Write It Up

There are several detailed guidelines on how to write up a meta-analysis. For clinical trials, the QUORUM and PRISMA guidelines are particularly useful ( Moher et al., 1999 ; Moher, Liberati, Tetzlaff, Altman, & Grp, 2009 ), and more generally the American Psychological Association (APA) has published its own Meta-Analysis Reporting Standards (MARS; H. Cooper, Maxwell, Stone, Sher, & Board, 2008 ). In addition, there are individual articles that offer advice (e.g., Field & Gillett, 2010 ; Rosenthal, 1995 ). There is a lot of overlap in these guidelines, and Table 17.3 assimilates them in an attempt to give a thorough overview of the structure and content of a meta-analysis article. This table should need no elaboration, but it is worth highlighting some of the key messages:

Introduction : Be clear about the rationale for the meta-analysis: What are the theoretical, practical, or policy drivers of the research? Why is a meta-analysis necessary? What hypotheses do you have?

Methods : Be clear about your search and inclusion criteria. How did you reach the final sample of studies? The PRISMA guidelines suggest including a flowchart of the selection process: Figure 17.6 shows the type of flowchart suggested by PRISMA, which outlines the number of studies retained and eliminated at each phase of the selection process. Also, state your computational and analytic methods in sufficient detail: Which effect size measure are you using (and did you have any issues in computing these)? Which meta-analytic technique did you apply to the data and why? Did you do a subgroup or moderator analysis?

Results : Include a graphical summary of the effect sizes included in the study. A forest plot is a very effective way to show the reader the raw data. When there are too many studies for a forest plot, consider a stem-and-leaf plot. A summary table of studies and any important study characteristics/moderator variables is helpful. If you have carried out a moderator analysis, then you might also provide stem-and-leaf plots or forest plots for subgroups of the analysis. Always report statistics relating to the variability of effect sizes (these should include the actual estimate of variability as well as statistical tests of variability), and obviously the estimate of the population effect size and its associated confidence interval (or credibility interval). Report information on publication bias (e.g., a forest plot) and preferably a sensitivity analysis (e.g., Vevea and Woods’ method).

Discussion : Pull out the key theoretical, policy, or practical messages that emerge from the analysis. Discuss any limitations or potential sources of bias within the analysis. Finally, it is helpful to make a clear statement about how the results inform the future research agenda.

The PRISMA-recommended flowchart.

This chapter offered a preliminary but comprehensive overview of the main issues when conducting a meta-analysis. We also used some real data to show how the metafor package in R can be used to conduct the analysis. The analysis begins by collecting articles about the research question you are trying to address through a variety of methods: emailing people in the field for unpublished studies, electronic searches, searches of conference abstracts, and so forth. Next, inclusion criteria should be devised that reflect the concerns pertinent to the particular research question (which might include the type of control group used, diagnostic measures, quality of outcome measure, type of treatment used, or other factors that ensure a minimum level of research quality). Statistical details are then extracted from the papers from which effect sizes can be calculated; the same effect size metric should be used for all studies, and you need to compute the variance or standard error for each effect size too. Choose the type of analysis appropriate for your particular situation (fixed- vs. random-effects, Hedges’ method or Hunter and Schmidt's, etc.), and then apply this method to the data. Describe the effect of publication bias descriptively (e.g., funnel plots), and consider investigating how to re-estimate the population effect under various publication-bias models using Vevea and Woods’ (2005) model. Finally, when reporting the results, make sure that the reader has clear information about the distribution of effect sizes (e.g., a stem-and-leaf plot), the effect size variability, the estimate of the population effect and its 95 percent confidence interval, the extent of publication bias, and whether any moderator variables were explored.

Useful Web Links

Comprehensive Meta-Analysis: http://www.meta-analysis.com/

MetaEasy: http://www.statanalysis.co.uk/meta-analysis.html

metafor package for R : http://www.metafor-project.org/

Mix: http://www.meta-analysis-made-easy.com/

PRISMA (guidelines and checklists for reporting meta-analysis): http://www.prisma-statement.org/

R : http://www.r-project.org/

Review Manager: http://ims.cochrane.org/revman

SPSS (materials accompanying Field & Gillett, 2010 ): http://www.discoveringstatistics.com/meta_analysis/how_to_do_a_meta_analysis.html

I generally find it easier to export from SPSS to a tab-delimited file because this format can also be read by software packages other than R. However, you can read SPSS data files (.sav) into R directly using the read.spss() function, but you need to first install and load a package called foreign .

There are slight differences in the decimal places between the results reported here and those on page 125 of Hanrahan and colleagues’ papers because we did not round effect sizes and their standard errors to 2 and 3 decimal places respectively before conducting the analysis.

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Clinical Psychology Research Topics

Stumped for ideas? Start here

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.

Clinical psychology research is one of the most popular subfields in psychology. With such a wide range of topics to cover, figuring out clinical psychology research topics for papers, presentations, and experiments can be tricky.

Clinical Psychology Research Topic Ideas

Topic choices are only as limited as your imagination and assignment, so try narrowing the possibilities down from general questions to the specifics that apply to your area of specialization.

Here are just a few ideas to start the process:

• How does social media influence how people interact and behave?
• Compare and contrast two different types of therapy . When is each type best used? What disorders are best treated with these forms of therapy? What are the possible limitations of each type?
• Compare two psychological disorders . What are the signs and symptoms of each? How are they diagnosed and treated?
• How does "pro ana," "pro mia," " thinspo ," and similar content contribute to eating disorders? What can people do to overcome the influence of these sites?​
• Explore how aging influences mental illness. What particular challenges elderly people diagnosed with mental illness face?
• Explore factors that influence adolescent mental health. Self-esteem and peer pressure are just a couple of the topics you might explore in greater depth.
• Explore the use and effectiveness of online therapy . What are some of its advantages and disadvantages ? How do those without technical literacy navigate it?
• Investigate current research on the impact of media violence on children's behavior.
• Explore anxiety disorders and their impact on daily functioning. What new therapies are available?
• What are the risk factors for depression ? Explore the potential risks as well as any preventative strategies that can be used.
• How do political and social climates affect mental health?
• What are the long-term effects of childhood trauma? Do children continue to experience the effects later in adulthood? What treatments are available for PTSD (post-traumatic stress disorder) in childhood ?
• What impact does substance use disorder have on the family? How can family members help with treatment?
• What types of therapy are most effective for childhood behavioral issues ?

Think of books you have read, research you have studied, and even experiences and interests from your own life. If you've ever wanted to dig further into something that interested you, this is a great opportunity. The more engaged you are with the topic, the more excited you will be to put the work in for a great research paper or presentation.

Consider Scope, Difficulty, and Suitability

Picking a good research topic is one of the most important steps of the research process. A too-general topic can feel overwhelming; likewise, one that's very specific might have limited supporting information. Spend time reading online or exploring your library to make sure that plenty of sources to support your paper, presentation, or experiment are available.

If you are doing an experiment , checking with your instructor is a must. In many cases, you might have to submit a proposal to your school's human subjects committee for approval. This committee will ensure that any potential research involving human subjects is done in a safe and ethical way.

Once you have chosen a topic that interests you, run the idea past your course instructor. (In some cases, this is required.) Even if you don't need permission from the instructor, getting feedback before you delve into the research process is helpful.

Your instructor can draw from a wealth of experience to offer good suggestions and ideas for your research, including the best available resources pertaining to the topic. Your school librarian may also be able to provide assistance regarding the resources available for use at the library, including online journal databases.

Kim WO.  Institutional review board (IRB) and ethical issues in clinical research .  Korean Journal of Anesthesiology . 2012;62(1):3-12. doi:10.4097/kjae.2012.62.1.3

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

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The Impact Of Social Media On Mental Health: A Mixed-Methods Research Of Service Providers’ Awareness , Sarah Nichole Koehler, Bobbie Rose Parrell California State University, San Bernardino

The Impact Of Social Media On Mental Health: A Mixed-Methods Research Of Service Providers’ Awareness , Sarah Nichole Koehler, Bobbie Rose Parrell

Electronic theses, projects, and dissertations.

The relationship between heavy use of social media and an increase in mental health disorders has long been established. However, there is a gap in the literature regarding mental health practitioners/providers’ responses to this issue. This mixed-methods research embraced two theoretical perspectives—Ecological Model and Generalist Intervention Model—toward determining the extent to which mental health practitioners/providers assess for the impact of heightened use of social media on mental health. Qualitative and quantitative data were collected from 95 mental health practitioners (N = 95) via Qualtrics.

Non-parametric tests and descriptive statistics showed that prior training, agency’s values, and credentials impact mental health …

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The Impact Of Social Media On The Self-Esteem Of Youth 10–17 Years Old: A Review Of The Literature , Jasmine M. Daniels National Louis University

The Impact Of Social Media On The Self-Esteem Of Youth 10–17 Years Old: A Review Of The Literature , Jasmine M. Daniels

Dissertations.

The world of technology has expanded quickly and vastly since its inception. The creation of social media sites and applications has changed the ways in which youth interact, connect, and share with one another. As the number of social media sites and applications increases, so does their use by adolescents. During adolescence, youth are undergoing the process of identity development and self-esteem is an important part of this development. During this developmental period, adolescents’ self-esteem is likely to be affected by the feedback they receive online through social media sites. There is limited research available that specifically evaluated the impact …

Factors Affecting Academic Procrastination , John Paul Reynolds Western Kentucky University

Factors Affecting Academic Procrastination , John Paul Reynolds

Masters theses & specialist projects.

This study sought to understand the relationships among locus of control, parenting style, academic procrastination, and financial independence with a population of undergraduate students. A sample of 61 students (39 females, 21 males, 1 other) completed measures of demographics, locus of control, parenting style, and academic procrastination. Participants were recruited within the last two weeks of the semester. Therefore, the sample probably contained a higher percentage of procrastinators than the general population. There were no significant correlations across the total sample. There was a significant positive correlation between higher scores on the Parental Authority Questionnaire authoritative scale and the Procrastination …

The Relationship Between Dominant And Submissive Sexual Fantasies And Behaviors And Sexual Satisfaction , Lilian Rae Dalton University of Vermont

The Relationship Between Dominant And Submissive Sexual Fantasies And Behaviors And Sexual Satisfaction , Lilian Rae Dalton

Graduate college dissertations and theses.

Sexual fantasies of dominance and submission are commonly reported as preferred fantasies in healthy women, yet little is understood regarding whether these fantasies affect other aspects of sexuality (Leitenberg et al., 1995). Even less research exists regarding associations between dominant and submissive fantasies, dominant and submissive sexual behavior, and resulting sexual satisfaction. It is not well understood how sexual fantasy may translate into sexual behavior in non-clinical populations, and what factors might facilitate or inhibit individuals’ engagement in sexual behavior that matches their sexual fantasies.

The current study measured the relationship between dominant and submissive sexual fantasies, dominant and submissive …

Diagnosing The Demonic , David W. Appleby Liberty University

Diagnosing The Demonic , David W. Appleby

Faculty publications and presentations.

One of the most difficult tasks for a therapist is to determine whether a client’s difficulty is psychologically based or spiritually based. Scripture shows us that virtually any physical, psychological, or social symptoms might be attributed to demonic spirits. The enemy attacks on all possible fronts, which complicates the whole process of diagnosis. The most accurate diagnoses come not from looking only at symptoms, but at predictive life experiences. If the individual has opened certain doors there is an increased probability that demonic involvement is present. These include (1) generational curses, oaths, and soul ties, (2) occult involvement, (3) trauma …

Toxic Masculinity: An Exploration Of Traditional Masculine Norms In Relation To Mental Health Outcomes And Help-Seeking Behaviors In College-Aged Males , Benjamin Harris University of South Carolina - Columbia

Toxic Masculinity: An Exploration Of Traditional Masculine Norms In Relation To Mental Health Outcomes And Help-Seeking Behaviors In College-Aged Males , Benjamin Harris

Senior theses.

This study examines the relationships between traits of toxic masculinity, mental health outcomes, and help-seeking behaviors in college-aged males. To perform this analysis, a thorough literature review was conducted, and survey data was collected and analyzed to draw conclusions and implications for future research

Trauma And Effects Of Being Raised By A Parent With Bipolar I Disorder: A Theoretical Approach Utilizing Internal Family Systems , Daniella Vasquez National Louis University

Trauma And Effects Of Being Raised By A Parent With Bipolar I Disorder: A Theoretical Approach Utilizing Internal Family Systems , Daniella Vasquez

Bipolar I disorder (BD) is a somewhat rare mental health disorder that impacts not only the person diagnosed but also their family (Chang et al., 2001; Cook et al., 2005; Wearden et al., 2008). Children of parents with BD may experience complex trauma as inconsistencies in parenting, conflict within the household, and possible neglect or abuse may be present (Chang et al., 2001; Rusner et al., 2009; Wearden et al., 2008). Multiple developmental areas can be impacted for individuals who have endured complex trauma to include attachment, somatic distress, affect regulation, biology, behaviors, cognition, and identity (Cook et al., 2005; …

Using Toys To Support Infant-Toddler Learning And Development , Gabriel Guyton Bank Street College of Education

Using Toys To Support Infant-Toddler Learning And Development , Gabriel Guyton

All faculty and staff papers and presentations.

Being mindful of the basic principles of child development and the role of play, teachers can intentionally select toys to meet young children's unique needs and interests, supporting learning.

The Impacts Of Discrimination On Mental And Physical Health , Mahal Alvarez-Backus Colby College

The Impacts Of Discrimination On Mental And Physical Health , Mahal Alvarez-Backus

Honors theses.

The present studies investigated the role of campus climate and discrimination on college students’ mental and physical health. Young adults completed measures that assessed their perceptions of campus climate, sense of belonging, depressive symptoms, anxiety levels, sleep quality, physical health symptoms, and experiences with discrimination. Despite the fact that there were not any differences in physical health or sleep quality, there were significant differences in phycological health, specifically with depressive symptoms. Across both studies, there were significant group differences in mental health, particularly that students of color reported higher levels of depressive symptoms than white students. Having empathetic faculty was …

The Influences Of Social Media: Depression, Anxiety, And Self-Concept , Emily Baker Eastern Illinois University

The Influences Of Social Media: Depression, Anxiety, And Self-Concept , Emily Baker

Masters theses.

The current study examined correlations between social media use and its effects on depression, anxiety, and changes in self-concept through quantitative and qualitative data. Variables included in the analysis of Study 1 were depression, anxiety, time spent using social media, number of platforms used, perception of addiction, and type of use. Study 1 showed no significant correlations between social media used and depression or anxiety, however observational analyses of correlation tables revealed a relationship between time spent using and number of platforms used; time spent using and perception of addiction; time spent using and perception of addiction; time spent using …

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An Exploration Of The Contributions Of Parenting Styles And Peer Relationships On The Emotional Expression Of Second-Generation Indian-Americans , Smitha M. Kashi 2024 University of San Francisco

An Exploration Of The Contributions Of Parenting Styles And Peer Relationships On The Emotional Expression Of Second-Generation Indian-Americans , Smitha M. Kashi

Doctoral dissertations.

This dissertation used a qualitative analysis methodology to study the contributions of parenting styles and peer relationships on the emotional expressivity of second generation Asian Indian-Americans. Seven participants participated in a 60 - 90 minute long semi-structured interview and the content was analyzed using a thematic analysis approach. Most previous research regarding this topic has been conducted on White American or European individuals and does not take into consideration the many intersectionalities that second-generation Indian-Americans hold. Many participants discussed the lack of direction they experienced in regards to learning about their emotions and disclosed that college and young adulthood provided …

Does Self-Care Moderate The Association Between Adverse Childhood Experiences, Trauma Symptoms, And Parental Reflective Functioning? , Vinaya Thomas 2024 East Tennessee State University

Does Self-Care Moderate The Association Between Adverse Childhood Experiences, Trauma Symptoms, And Parental Reflective Functioning? , Vinaya Thomas

Electronic theses and dissertations.

Unresolved adverse and traumatic experiences in parents can amplify the risk of perpetuating intergenerational transmission of trauma. Parents’ positive and adaptive practices such as self-care might act as a protective factor against this ripple effect. This study investigated whether self-care moderated the relationship between parental ACEs, post-traumatic stress symptoms, and parental reflective functioning in mothers who are experiencing high psychosocial stress (n = 59) residing in rural Appalachia. Within our sample, there was no statistically significant linear relationship between our predictors, ACE score and PTSD symptoms, and our outcome of interest, maternal pre-mentalizing. We did not find empirical support for …

An Exploration Of Adult Children’S Attachment To Their Parents Across Two Cultural Groups: Indians In India And Indians Who Immigrated To The United States , Vilasini Meenakshi Arun 2024 The University of San Francisco

An Exploration Of Adult Children’S Attachment To Their Parents Across Two Cultural Groups: Indians In India And Indians Who Immigrated To The United States , Vilasini Meenakshi Arun

Typically, attachment theory has been studied and explored with western populations. I ndividuals seeking mental health treatment within the United States include western and nonwestern cultural groups and research, theories and interventions that apply to diverse populations are necessary. Attachment relationships are often a part of, or reasons for clients to seek therapy either overtly or covertly, thus allowing research on attachment to better inform treatment plans and practice. An attachment relationship between a parent and child can be influenced by several factors and may change over the course of development, but little is known about this process among Indians …

Derogatory, Racist, And Discriminatory Speech (Drds) In Video Gaming , Sean Woodford 2024 National Louis University

Derogatory, Racist, And Discriminatory Speech (Drds) In Video Gaming , Sean Woodford

Video games have been examined for their effects on cognition, learning, health, and physiological arousal, yet research on social dynamics within video gaming is limited. Studies have documented the presence of derogation, racism, and discrimination in this anonymous medium. However, gamers‟ firsthand experiences are typically examined qualitatively. Thus, this study aimed to establish a quantitative baseline for the frequency of derogatory, racist, and discriminatory speech (DRDS) in gaming. DRDS frequency, sexual harassment, and hate speech measures were administered to 150 individuals from online forums and social media groups. Descriptive and inferential analyses were used to gauge which factors affected DRDS …

Drug Use And Harm Reduction: Community Readiness As Pathway To Well-Being And Reintegration , Lauretta Ekanem Omale 2024 National Louis University

Drug Use And Harm Reduction: Community Readiness As Pathway To Well-Being And Reintegration , Lauretta Ekanem Omale

Drug abuse negatively impacts the life and well-being of those who use drugs; this harm often extends to their loved ones, communities, and society. One presumptive set of psychological explanations for drug abuse is an addictive personality, a psychological susceptibility resulting from challenging family relationships, inadequate reinforcement, the absence of healthy role models, conflicting parental expectations, and a lack of love and respect. Harm reduction is a public health approach that focuses on minimizing the harmful effects of drugs and reducing judgment. It aims to meet people where they are in life and provide judgment-free, empathetic, supportive, and needed medical …

The Implications, Magnitude, And Development Of Traumatic Brain Injury For Individuals Undergoing Treatment For Opioid Use Disorder , Hannah G. Mitchell 2024 East Tennessee State University

The Implications, Magnitude, And Development Of Traumatic Brain Injury For Individuals Undergoing Treatment For Opioid Use Disorder , Hannah G. Mitchell

There is an established bidirectional relation between substance use and traumatic brain injuries (TBIs). Despite the high rate of prescribing opioids for pain management following a TBI and the epidemic of opioid use disorder (OUD) in the United States, scarce research has specifically analyzed the association between TBI and OUD. In a series of three interrelated manuscripts, the present study will first examine the prevalence and features of TBIs among persons seeking treatment for OUD. Next, the present study will evaluate the association between TBI and indicators of risky health behaviors and OUD severity, including the risk of overdose and …

Adverse Childhood Experiences And Sexual Functioning: A Mediation Analysis Of Difficulties In Emotional Regulation , Haven Travis 2024 East Tennessee State University

Adverse Childhood Experiences And Sexual Functioning: A Mediation Analysis Of Difficulties In Emotional Regulation , Haven Travis

Undergraduate honors theses.

Sexual dysfunction can lead to a negative impact upon a person’s mental and relational health, including relational and overall distress, poor relationship and sexual satisfaction, and clinical mood disorders such as depression. Moving upstream to identify factors that may predict sexual dysfunction would therefore be beneficial for early intervention in at-risk populations. History of childhood trauma is one such factor that may influence sexual functioning later in life. While adverse childhood experiences (ACEs) have been less studied in association with sexual dysfunction, there is some evidence to indicate that they may be related. ACEs have been shown to increase the …

Examining The Effects Of Menstrual Cycle Phase And Hormonal Contraceptive Use On Women's Sleep , Charles Ethan Coombs 2024 University of Arkansas, Fayetteville

Examining The Effects Of Menstrual Cycle Phase And Hormonal Contraceptive Use On Women's Sleep , Charles Ethan Coombs

Psychological science undergraduate honors theses.

Women overrepresent men for sub-optimal sleep, a consequence of hormone fluctuation in the menstrual cycle affecting sleep regulatory pathways. While research has examined the prevalence of sub-optimal sleep through cycle phases, little research has examined how hormonal contraceptives (HC’s) could similarly affect women’s sleep, while also neglecting to utilize subjective sleep measures. In this study, we examine subjective sleep quality among naturally cycling (NC) women, women using different HC types, and between active and inactive phase pill users by subjecting 463 women to a subjective sleep battery. We hypothesized that HC users would report more sub-optimal sleep than NC women. …

The Effect Of Animal-Assisted Therapy On Prosocial Behaviors In Children With Autism Spectrum Disorders And Developmental Delay: A Pilot Study , Emma Mitchell 2024 University of Arkansas, Fayetteville

The Effect Of Animal-Assisted Therapy On Prosocial Behaviors In Children With Autism Spectrum Disorders And Developmental Delay: A Pilot Study , Emma Mitchell

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by difficulties in social interaction, nonverbal communication, and repetitive patterns (American Psychiatric Association [APA], 2013). Indeed, autistic individuals have been shown to have greater challenges with developing, maintaining, and understanding social relationships which can persist over their lifetime. Early intervention services are recommended for autistic children when they are young to help promote positive skills to enhance their functioning within society and increase their wellbeing over time. This is an especially pressing issue as ASD rates are rising – with the latest data now suggesting 1 in 36 children have the …

U.S. Military Veteran Identity And Civilian Adjustment , Darren A. Sosa 2024 University of the Pacific

U.S. Military Veteran Identity And Civilian Adjustment , Darren A. Sosa

Pacific journal of health.

U.S. military veterans transitioning back into the civilian sector often experience a variety of challenges and need to readjust into non-military environments. In order to examine the psychological challenges involved in the transition and readjustment processes among U. S. active military veterans, a qualitative study was conducted. For 6 months, data was collected from veterans in California, Nevada, Texas, Florida, and New York. Following an IRB approval, semi-structured open-ended self-developed interviews were developed and conducted with sixteen military veterans who have served in 4 branches of the U.S. Armed Forces: Army, Navy, Air Force, and Marine Corps. Data and information …

Radical Youth Work: A Community Based Approach To Working With Youth, Young Adults And Families , Weston J. Robins 2024 Georgia Southern University

Radical Youth Work: A Community Based Approach To Working With Youth, Young Adults And Families , Weston J. Robins

National youth advocacy and resilience conference.

Radical Youth Work: A Community Based Approach to Working with Youth, Young Adults and Families

A focus on experiential mentoring, humanistic counseling and community engagement as a way to work with youth, young adults and families to provide true holistic therapeutic support and guidance.

Power And Control Wheel Tactics: Assessing Posttraumatic Stress Disorder (Ptsd) Epistemology Among Domestic Violence Survivors , Lisa Hady 2024 Augsburg University

Power And Control Wheel Tactics: Assessing Posttraumatic Stress Disorder (Ptsd) Epistemology Among Domestic Violence Survivors , Lisa Hady

Theses and graduate projects.

Domestic violence affects more than 10 million adults in the United States annually (National Network to End Domestic Violence, 2020). The Power and Control Wheel (PCW) places power and control at the center of physical and sexual violence, with eight surrounding tactics representing the abusive behaviors perpetrators use to retain power and control over survivors. Although the PCW has been extended to understand various abusive relationships, and the PCW tactics have been examined in both Intimate Partner Violence (IPV) and Non-Intimate Partner Violence (NPV; Basile et al., 2004; Scott, 2018), limited research compares tactics used based on the perpetrator’s relationship …

Psychological Athetosis: The Disjunctive Force Of The Unrepresentable , 2024 Cal Poly Humboldt

Psychological Athetosis: The Disjunctive Force Of The Unrepresentable

The international journal of ecopsychology (ije).

In contrast to descriptions of a familiar and bonded-with “sense of place,” S. Freud employed a German definition of the term and experience as “unhomely” ( Unheimliche ) (1919, Das Unheimliche ) -- “The Uncanny.” He argued that the uncanny is an intrusion of the dreadful into the familiar and thus, it is here proposed, signals a radical departure from known ground . Similarly, Kaplan and Kaplan (1974, 1977, 1989), in their studies of landscape preferences, employed the dimensions of ‘mystery’ and ‘complexity’ as a means for understanding an innate evolutionary rubric for assessing a given terrain in terms of …

Integrating Trauma-Informed Services In Out-Of-School Time Programs To Mitigate The Impact Of Community Gun Violence On Youth Mental Health , Angela T. Clarke, Stevie N. Grassetti, Lauren Brumley, Kyle Y. Ross, Courtney Erdly, Sarah Richter, Emily R. Brown, Michele Pole 2024 West Chester University of Pennsylvania

Integrating Trauma-Informed Services In Out-Of-School Time Programs To Mitigate The Impact Of Community Gun Violence On Youth Mental Health , Angela T. Clarke, Stevie N. Grassetti, Lauren Brumley, Kyle Y. Ross, Courtney Erdly, Sarah Richter, Emily R. Brown, Michele Pole

Psychology faculty publications.

Community gun violence disproportionately impacts youth in low-income urban neighborhoods. Integrating trauma informed mental health care in community-based out-of-school time (OST) programs is an innovative method of service delivery for these youth. This article provides justification for integrating evidence-based, trauma-informed services in OST programs within communities characterized by high rates of violent crime to minimize the impact of violence exposure on youth mental health. We describe the initial feasibility of a model program, the Violence Intervention and Prevention (VIP) Initiative, implemented in a small city in southeastern Pennsylvania. Within the first six months of the VIP Initiative, 95 community residents …

Generation Z Predictors Of Attitudes Towards Mental Health Services And Perceptions Of Stress , Dr. Tina Onikoyi 2024 Walden University

Generation Z Predictors Of Attitudes Towards Mental Health Services And Perceptions Of Stress , Dr. Tina Onikoyi

Walden dissertations and doctoral studies.

No abstract provided.

Pathways From Childhood Sexual Abuse To Sexual Risk Behaviour In Msm: A Mixed Methods Study , Daniel Provenzano 2024 University of Windsor

Pathways From Childhood Sexual Abuse To Sexual Risk Behaviour In Msm: A Mixed Methods Study , Daniel Provenzano

Men who have sex with men (MSM) are disproportionately affected by childhood sexual abuse (CSA) compared to heterosexual men. CSA has been associated with a wide range of adverse outcomes later in life, including engagement in sexual risk behaviours (e.g., condomless anal intercourse), that place MSM at greater risk for HIV acquisition and transmission. Although the links between CSA and sexual risk are well-established among MSM, little research has investigated psychosocial factors that may mediate this relationship. Using a mixed-method approach, this study (a) explored psychosocial factors as pathways linking a composite of complex CSA experiences (i.e., CSA complexities) with …

Resisting Internalized Stigma (Ris): Acceptability And Feasibility Of A Cognitive Behavioral Stigma Intervention For Early Psychosis , Francesca Maria Crump 2024 Rowan University

Resisting Internalized Stigma (Ris): Acceptability And Feasibility Of A Cognitive Behavioral Stigma Intervention For Early Psychosis , Francesca Maria Crump

Theses and dissertations.

The clinical high-risk state for psychosis (CHR-P) was created to help identify individuals experiencing early signs of psychosis to help forestall worsening symptoms. CHR-P individuals may experience stigma that may stem from internal or external processes, including from receiving specialized care. Research has demonstrated associations between internalized stigma and psychosocial and functional outcomes, which underscores the need for interventions to help mitigate the impact of stigma while balancing the need for treatment. To date, there is only one stigma intervention specifically designed for individuals designated as CHR-P, which is psychoeducational in nature. Based on the recent call to action that …

Cameroonian Parent And Teacher Perspectives Of Corporal Punishment And Its Psychological Effects On Children , Joan Mbeng Bakia-William 2024 Walden University

Cameroonian Parent And Teacher Perspectives Of Corporal Punishment And Its Psychological Effects On Children , Joan Mbeng Bakia-William

Do Professor Characteristics Influence College Students' Mental Health Disclosure? , Giselle Solorio, Kenneth Barideaux Jr. 2024 University of South Carolina Upstate

Do Professor Characteristics Influence College Students' Mental Health Disclosure? , Giselle Solorio, Kenneth Barideaux Jr.

University of south carolina upstate student research journal.

Previous studies have provided some evidence that college students may hesitate to disclose their mental health status because of social stigma; however, more research is needed to identify and understand the factors that influence students’ willingness to disclose. For example, it is unclear how professor characteristics impact the likelihood of disclosure. In the current study we examined whether the gender of the professor (male vs. female) and the professor’s teaching discipline (STEM vs. humanities) affected students' likelihood to disclose a mental health problem. Participants read a fictitious syllabus where the professor was either male or female and taught a chemistry …

Bullying Involvement And Depression Among Youth With Adhd: Is Positive Parenting Protective? , Jessica A. Simmons 2024 Syracuse University

Bullying Involvement And Depression Among Youth With Adhd: Is Positive Parenting Protective? , Jessica A. Simmons

Dissertations - all.

Abstract Background: Youth with attention deficit / hyperactivity disorder (ADHD) are at increased independent risk for bullying involvement or depression yet the topic of bullying involvement and depression in ADHD is poorly understood and largely considered without a guiding theoretical framework. Research on the relation between ADHD and bullying is still in its early stages and less is known about the risk and protective factors that may contribute to the development or buffering of depression in these youth. Family factors such as positive parenting promote resilience in children with ADHD, help reduce the risk for youth depression and help to …

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