Cognitive-behavioural therapy (CBT) has proven to be effective for anxiety-based school refusal, but it is still unknown how CBT for school refusal works, or through which mechanisms.
Innovative statistical approaches for analyzing small uncontrolled samples were used to investigate the role of self-efficacy in mediating CBT outcomes for anxiety-based school refusal.
Participants were 19 adolescents (12 to 17 years) who completed a manual-based cognitive-behavioural treatment. Primary outcomes (school attendance; school-related fear; anxiety) and secondary outcomes (depression; internalizing problems) were assessed at post-treatment and 2-month follow-up.
Post-treatment increases in school attendance and decreases in fear about attending school the next day were found to be mediated by self-efficacy. Mediating effects were not observed at 2-month follow-up.
These findings provide partial support for the role of self-efficacy in mediating the outcome of CBT for school refusal. They contribute to a small body of literature suggesting that cognitive change enhances CBT outcomes for young people with internalizing problems. Regarding methodology, the product of coefficient test appears to be a valuable way to study mediation in outcome studies involving small samples.
CBT; mediators; school refusal; anxiety; self-efficacy
Previous studies of different methods of testing mediation models have consistently found two anomalous results. The first result is elevated Type I error rates for the bias-corrected and accelerated bias-corrected bootstrap tests not found in nonresampling tests or in resampling tests that did not include a bias correction. This is of special concern as the bias-corrected bootstrap is often recommended and used due to its higher statistical power compared with other tests. The second result is statistical power reaching an asymptote far below 1.0 and in some conditions even declining slightly as the size of the relationship between X and M, a, increased. Two computer simulations were conducted to examine these findings in greater detail. Results from the first simulation found that the increased Type I error rates for the bias-corrected and accelerated bias-corrected bootstrap are a function of an interaction between the size of the individual paths making up the mediated effect and the sample size, such that elevated Type I error rates occur when the sample size is small and the effect size of the nonzero path is medium or larger. Results from the second simulation found that stagnation and decreases in statistical power as a function of the effect size of the a path occurred primarily when the path between M and Y, b, was small. Two empirical mediation examples are provided using data from a steroid prevention and health promotion program aimed at high school football players (Athletes Training and Learning to Avoid Steroids; Goldberg et al., 1996), one to illustrate a possible Type I error for the bias-corrected bootstrap test and a second to illustrate a loss in power related to the size of a. Implications of these findings are discussed.
Methodologists have developed mediation analysis techniques for a broad range of substantive applications, yet methods for estimating mediating mechanisms with missing data have been understudied. This study outlined a general Bayesian missing data handling approach that can accommodate mediation analyses with any number of manifest variables. Computer simulation studies showed that the Bayesian approach produced frequentist coverage rates and power estimates that were comparable to those of maximum likelihood with the bias-corrected bootstrap. We share a SAS macro that implements Bayesian estimation and use two data analysis examples to demonstrate its use.
Mediation; indirect effects; missing data; Bayesian estimation; bias corrected bootstrap; Sobel test
To understand the influences associated with durability and diffusion of benefits of a fire service wellness program.
Qualitative assessment of group interviews.
Five years following a controlled worksite wellness trial, behavioral improvements were durable and had diffused to control participants. These findings were associated with firefighters’ team orientation, enacted healthy norms and competitiveness regarding the results of annual health assessments. The original intervention trial appeared to initiate individual change that coalesced into group effects. Secondary influences included increasing public awareness about health, newly hired younger firefighters, and a modicum of administrative support. Culture shift was achieved at the workplace.
Although the fire service is a unique occupation, these findings suggest general strategies to achieve durable positive health change in other occupational settings.
worksite; dietary behaviors; physical activity; qualitative
Applied researchers often include mediation effects in applications of advanced methods such as latent variable models and linear growth curve models. Guidance on how to estimate statistical power to detect mediation for these models has not yet been addressed in the literature. We describe a general framework for power analyses for complex mediational models. The approach is based on the well known technique of generating a large number of samples in a Monte Carlo study, and estimating power as the percentage of cases in which an estimate of interest is significantly different from zero. Examples of power calculation for commonly used mediational models are provided. Power analyses for the single mediator, multiple mediators, three-path mediation, mediation with latent variables, moderated mediation, and mediation in longitudinal designs are described. Annotated sample syntax for Mplus is appended and tabled values of required sample sizes are shown for some models.
Mediation; Statistical Power; Monte Carlo; Mplus
To determine the relationship between lifestyle variables including body mass index (BMI) and filing a worker’s compensation claim due to firefighter injury.
A cross-sectional evaluation of firefighter injury related worker compensation claims occurring 5 years after the original PHLAME study intervention.
Logistic regression analysis for variables predicting filing a worker’s compensation claim due to an injury were performed. with a total of 433 participants. The odds of filing a compensation claim were almost three times higher for firefighters with a BMI >30 compared to firefighters with normal BMI (odds ratio=2.89, p<.05).
This study addresses a high priority area of reducing firefighter injuries and worker’s compensation claims. Maintaining a healthy body weight is important to reduce injury and worker’s compensation claims among firefighters.
This article provides an overview of statistical mediation analysis methods in the evaluation of public health dentistry interventions.
Methods and Results
First, reasons for conducting mediation analysis are outlined, followed by a discussion of the link between the mediation model and theoretical bases of interventions. Second, the basic statistical procedures in mediation analysis are presented. An example application to data from a hypothetical intervention is provided in Appendix A. Third, interpretation of the results from statistical mediation analysis is described along with additional information pertinent to identifying true mediation relations.
Guidelines for describing mediation analyses in research articles related to public health dentistry intervention studies are outlined.
mediation; prevention; statistical analysis
The purpose of this article is to describe mediating variables and moderating variables and provide reasons for integrating them in outcome studies. Separate sections describe examples of moderating and mediating variables and the simplest statistical model for investigating each variable. The strengths and limitations of incorporating mediating and moderating variables in a research study are discussed as well as approaches to routinely including these variables in outcome research. The routine inclusion of mediating and moderating variables holds the promise of increasing the amount of information from outcome studies by generating practical information about interventions as well as testing theory. The primary focus is on mediating and moderating variables for intervention research but many issues apply to nonintervention research as well.
methodology; methodological article; intervention; outcome
To develop more effective anti-smoking programs, it is important to understand the factors that influence people to smoke. Guided by attribution theory, a longitudinal study was conducted to investigate how individuals’ cognitive attributions for smoking were associated with subsequent smoking development and through which pathways.
Middle and high school students in seven large cities in China (N=12,382; 48.5% boys and 51.5% girls) completed two annual surveys. Associations between cognitive attributions for smoking and subsequent smoking initiation and progression were tested with multilevel analysis, taking into account plausible moderation effects of gender and baseline smoking status. Mediation effects of susceptibility to smoking were investigated using statistical mediation analysis (MacKinnon, 2008).
Six out of eight tested themes of cognitive attributions were associated with subsequent smoking development. Curiosity (β=0.11, p<0.001) and autonomy (β=0.08, p=0.019) were associated with smoking initiation among baseline non-smokers. Coping (β=0.07, p<0.001) and social image (β=0.10, p=<.0001) were associated with smoking progression among baseline lifetime smokers. Social image (β=0.05, p=0.043), engagement (β=0.07, p=0.003), and mental enhancement (β=0.15, p<0.001) were associated with smoking progression among baseline past 30-day smokers. More attributions were associated with smoking development among males than among females. Susceptibility to smoking partially mediated most of the associations, with the proportion of mediated effects ranging from 4.3% to 30.8%.
This study identifies the roles that cognitive attributions for smoking play in subsequent smoking development. These attributions could be addressed in smoking prevention programs.
Attributions; Smoking; Attribution Theory; Adolescents; China
This paper examines the mechanisms by which PHLAME (Promoting Healthy Lifestyles: Alternative Models’ Effects), a health promotion intervention, improved healthy eating and exercise behavior among firefighters, a population at high risk for health problems due to occupational hazards. In a randomized trial, 397 firefighters participated in either the PHLAME team intervention with their work shift or a control condition. Intervention sessions taught benefits of a healthy diet and regular exercise and sought to improve social norms and social support from coworkers for healthy behavior. At post-test team intervention participants had increased their fruit and vegetable consumption as compared to control participants. An increase in knowledge of fruit and vegetable benefits and improved dietary coworker norms partially mediated these effects. Exercise habits and VO2 max were related to targeted mediators but were not significantly changed by the team intervention. Partial support was found for both the action and conceptual theories underlying the intervention. Our findings illustrate how an effective program’s process can be deconstructed to understand the underpinnings of behavior change and refine interventions. Further, fire stations may improve the health of firefighters by emphasizing the benefits of healthy diet and exercise behaviors while also encouraging behavior change by coworkers as a whole.
health promotion intervention; work team
Aims: To investigate whether ethnic differences in vulnerability to peer norms supportive of alcohol use is a viable, partial explanation for the ethnic differences in reported prevalence and amount of alcohol use during high school. Methods: Survey data from a sample of 680 adolescents from Project STAR (Students Taught Awareness and Resistance) of the Midwestern Prevention Project were used. Hypotheses were tested using sequential, semi-continuous growth curve models. Results: Relative to Black adolescents, White adolescents reported greater peer alcohol use during middle school and were much more likely to consume alcohol during high school. General peer norms in seventh grade and middle school growth in alcohol use norms among close friends was predictive of a greater propensity to consume alcohol in ninth grade among White adolescents. Conclusion: Lower peer norms for alcohol use among Black adolescents might better account for differences between Black and White adolescents than the possibility that White adolescents are more vulnerable to peer norms.
This article describes the RMediation package, which offers various methods for building confidence intervals (CIs) for mediated effects. The mediated effect is the product of two regression coefficients. The distribution-of-the-product method has the best statistical performance of existing methods for building CIs for the mediated effect. RMediation produces CIs using methods based on the distribution of product, Monte Carlo simulations, and an asymptotic normal distribution. Furthermore, RMediation generates percentiles, quantiles, and the plot of the distribution and CI for the mediated effect. An existing program, called PRODCLIN, published in Behavior Research Methods, has been widely cited and used by researchers to build accurate CIs. PRODCLIN has several limitations: The program is somewhat cumbersome to access and yields no result for several cases. RMediation described herein is based on the widely available R software, includes several capabilities not available in PRODCLIN, and provides accurate results that PRODCLIN could not.
Mediation; Indirect effect; R; Confidence intervals
Four applications of permutation tests to the single-mediator model are described and evaluated in this study. Permutation tests work by rearranging data in many possible ways in order to estimate the sampling distribution for the test statistic. The four applications to mediation evaluated here are the permutation test of ab, the permutation joint significance test, and the noniterative and iterative permutation confidence intervals for ab. A Monte Carlo simulation study was used to compare these four tests with the four best available tests for mediation found in previous research: the joint significance test, the distribution of the product test, and the percentile and bias-corrected bootstrap tests. We compared the different methods on Type I error, power, and confidence interval coverage. The noniterative permutation confidence interval for ab was the best performer among the new methods. It successfully controlled Type I error, had power nearly as good as the most powerful existing methods, and had better coverage than any existing method. The iterative permutation confidence interval for ab had lower power than do some existing methods, but it performed better than any other method in terms of coverage. The permutation confidence interval methods are recommended when estimating a confidence interval is a primary concern. SPSS and SAS macros that estimate these confidence intervals are provided.
Mediation; Permutation test
Counselor behaviors that mediate the efficacy of motivational interviewing (MI) are not well understood, especially when applied to health behavior promotion. We hypothesized that client change talk mediates the relationship between counselor variables and subsequent client behavior change.
Purposeful sampling identified individuals from a prospective randomized worksite trial using an MI intervention to promote firefighters’ healthy diet and regular exercise that increased dietary intake of fruits and vegetables (n = 21) or did not increase intake of fruits and vegetables (n = 22). MI interactions were coded using the Motivational Interviewing Skill Code (MISC 2.1) to categorize counselor and firefighter verbal utterances. Both Bayesian and frequentist mediation analyses were used to investigate whether client change talk mediated the relationship between counselor skills and behavior change.
Counselors’ global spirit, empathy, and direction and MI-consistent behavioral counts (e.g., reflections, open questions, affirmations, emphasize control) significantly correlated with firefighters’ total client change talk utterances (rs = 0.42, 0.40, 0.30, and 0.61, respectively), which correlated significantly with their fruit and vegetable intake increase (r = 0.33). Both Bayesian and frequentist mediation analyses demonstrated that findings were consistent with hypotheses, such that total client change talk mediated the relationship between counselor’s skills—MI-consistent behaviors [Bayesian mediated effect: αβ = .06 (.03), 95% CI = .02, .12] and MI spirit [Bayesian mediated effect: αβ = .06 (.03), 95% CI = .01, .13]—and increased fruit and vegetable consumption.
Motivational interviewing is a resource- and time-intensive intervention, and is currently being applied in many arenas. Previous research has identified the importance of counselor behaviors and client change talk in the treatment of substance use disorders. Our results indicate that similar mechanisms may underlie the effects of MI for dietary change. These results inform MI training and application by identifying those processes critical for MI success in health promotion domains.
Motivational interviewing; Dietary change; Occupational health; Firefighters; Bayesian mediation
This article presents an overview of statistical mediation analysis and its application to psychosomatic medicine research. The article begins with a description of the major approaches to mediation analysis and an evaluation of the strengths and limits of each. Emphasis is placed on longitudinal mediation models, and an application using latent growth modeling is presented. The article concludes with a description of recent developments in mediation analysis and suggestions for the use of mediation for future work in psychosomatic medicine research.
statistical mediation; mediation analysis; mechanism; indirect effect
Occupational health promotion programs with documented efficacy have not penetrated worksites. Establishing an implementation model would allow focusing on mediating aspects to enhance installation and use of evidence-based occupational wellness interventions. The purpose of the study was to implement an established wellness program in fire departments and define predictors of program exposure/dose to outcomes to define a cross-sectional model of translational effectiveness. The study is a prospective observational study among 12 NW fire departments. Data were collected before and following installation, and findings were used to conduct mediation analysis and develop a translational effectiveness model. Worker age was examined for its impact. Leadership, scheduling/competing demands, and tailoring were confirmed as model components, while organizational climate was not a factor. The established model fit data well (χ2(9) = 25.57, CFI = 0.99, RMSEA = 0.05, SRMR = 0.03). Older firefighters, nearing retirement, appeared to have influences that both enhanced and hindered participation. Findings can inform implementation of worksite wellness in fire departments, and the prioritized influences and translational model can be validated and manipulated in these and other settings to more efficiently move health promotion science to service.
Occupational wellness; Mediation model; Translation; Firefighter
The paper describes advances in statistical methods for prevention research with a particular focus on substance abuse prevention. Standard analysis methods are extended to the typical research designs and characteristics of the data collected in prevention research. Prevention research often includes longitudinal measurement, clustering of data in units such as schools or clinics, missing data, and categorical as well as continuous outcome variables. Statistical methods to handle these features of prevention data are outlined. Developments in mediation, moderation, and implementation analysis allow for the extraction of more detailed information from a prevention study. Advancements in the interpretation of prevention research results include more widespread calculation of effect size and statistical power, the use of confidence intervals as well as hypothesis testing, detailed causal analysis of research findings, and meta-analysis. The increased availability of statistical software has contributed greatly to the use of new methods in prevention research. It is likely that the Internet will continue to stimulate the development and application of new methods.
prevention; statistical methods; substance abuse
The most commonly used method to test an indirect effect is to divide the estimate of the indirect effect by its standard error and compare the resulting z statistic with a critical value from the standard normal distribution. Confidence limits for the indirect effect are also typically based on critical values from the standard normal distribution. This article uses a simulation study to demonstrate that confidence limits are imbalanced because the distribution of the indirect effect is normal only in special cases. Two alternatives for improving the performance of confidence limits for the indirect effect are evaluated: (a) a method based on the distribution of the product of two normal random variables, and (b) resampling methods. In Study 1, confidence limits based on the distribution of the product are more accurate than methods based on an assumed normal distribution but confidence limits are still imbalanced. Study 2 demonstrates that more accurate confidence limits are obtained using resampling methods, with the bias-corrected bootstrap the best method overall.
This study investigated a method to evaluate mediational processes using latent growth curve modeling. The mediator and the outcome measured across multiple time points were viewed as 2 separate parallel processes. The mediational process was defined as the independent variable influencing the growth of the mediator, which, in turn, affected the growth of the outcome. To illustrate modeling procedures, empirical data from a longitudinal drug prevention program, Adolescents Training and Learning to Avoid Steroids, were used. The program effects on the growth of the mediator and the growth of the outcome were examined first in a 2-group structural equation model. The mediational process was then modeled and tested in a parallel process latent growth curve model by relating the prevention program condition, the growth rate factor of the mediator, and the growth rate factor of the outcome.
Mediating variables continue to play an important role in psychological theory and research. A mediating variable transmits the effect of an antecedent variable on to a dependent variable, thereby providing more detailed understanding of relations among variables. Methods to assess mediation have been an active area of research for the last two decades. This paper describes the current state of methods to investigate mediating variables.
mediation; indirect effect; statistical methods
A Monte Carlo study compared 14 methods to test the statistical significance of the intervening variable effect. An intervening variable (mediator) transmits the effect of an independent variable to a dependent variable. The commonly used R. M. Baron and D. A. Kenny (1986) approach has low statistical power. Two methods based on the distribution of the product and 2 difference-in-coefficients methods have the most accurate Type I error rates and greatest statistical power except in 1 important case in which Type I error rates are too high. The best balance of Type I error and statistical power across all cases is the test of the joint significance of the two effects comprising the intervening variable effect.
Mediation analysis uses measures of hypothesized mediating variables to test theory for how a treatment achieves effects on outcomes and to improve subsequent treatments by identifying the most efficient treatment components. Most current mediation analysis methods rely on untested distributional and functional form assumptions for valid conclusions, especially regarding the relation between the mediator and outcome variables. Propensity score methods offer an alternative whereby the propensity score is used to compare individuals in the treatment and control groups who would have had the same value of the mediator had they been assigned to the same treatment condition. This article describes the use of propensity score weighting for mediation with a focus on explicating the underlying assumptions. Propensity scores have the potential to offer an alternative estimation procedure for mediation analysis with alternative assumptions from those of standard mediation analysis. The methods are illustrated investigating the mediational effects of an intervention to improve sense of mastery to reduce depression using data from the Job Search Intervention Study (JOBS II). We find significant treatment effects for those individuals who would have improved sense of mastery when in the treatment condition but no effects for those who would not have improved sense of mastery under treatment.
This article proposes Bayesian analysis of mediation effects. Compared to conventional frequentist mediation analysis, the Bayesian approach has several advantages. First, it allows researchers to incorporate prior information into the mediation analysis, thus potentially improving the efficiency of estimates. Second, under the Bayesian mediation analysis, inference is straightforward and exact, which makes it appealing for studies with small samples. Third, the Bayesian approach is conceptually simpler for multilevel mediation analysis. Simulation studies and analysis of two datasets are used to illustrate the proposed methods.
Single-level mediation; Multilevel Mediation; Bayesian inference; Credible Interval