Biomedical subjects
Publications and source records attributed to David P MacKinnon.
Mediating variables are prominent in psychological theory and research. A mediating variable transmits the effect of an independent variable on a dependent variable. Differences between mediating variables and confounders, moderators, and covariates are outlined. Statistical methods to assess mediation and modern comprehensive approaches are described. Future directions for mediation analysis are discussed.
OBJECTIVE: Prior research has shown that designated drivers (DD) are widely used as a preventive measure for driving under the influence. Despite the prevalence of alcohol involvement in boating accidents, much less is known about the use of a designated boat operator (DBO). The current study investigated the prevalence of DBO use in recreational boating and compared the characteristics of DD users and DBO users. METHOD: Several survey questionnaires were distributed to a group of undergraduate students at a large southwestern university for the purpose of investigating demographic characteristics, alcohol-use pattern, and other alcohol-related problem behaviors, such as driving and boating under the influence. RESULTS: Approximately 45% of the boaters reported they had drunk alcohol while boating, and approximately 70% had used a DBO in the most recent boating event. The DBO users were found to be similar to the DD users in terms of drinking pattern, age of drinking onset, and driving behaviors. CONCLUSIONS: High rates of alcohol use in recreational boating suggest the need for prevention strategies. Furthermore, future studies are needed to investigate the selection process of the DBOs and the differences between passengers and boat operators, which could shed light on strategies to prevent alcohol- involved boating injuries.
Mediated moderation occurs when the interaction between two variables affects a mediator, which then affects a dependent variable. In this article, we describe the mediated moderation model and evaluate it with a statistical simulation using an adaptation of product-of-coefficients methods to assess mediation. We also demonstrate the use of this method with a substantive example from the adolescent tobacco literature. In the simulation, relative bias (RB) in point estimates and standard errors did not exceed problematic levels of +/- 10% although systematic variability in RB was accounted for by parameter size, sample size, and nonzero direct effects. Power to detect mediated moderation effects appears to be severely compromised under one particular combination of conditions: when the component variables that make up the interaction terms are correlated and partial mediated moderation exists. Implications for the estimation of mediated moderation effects in experimental and nonexperimental research are discussed.
This paper investigates baseline by treatment interactions (BTI) of a randomized anabolic steroid prevention program delivered to high school football players. Baseline by treatment interactions occur when a participant's score on an outcome variable is associated with both their pretreatment standing on the outcome variable and the treatment itself. The program was delivered to 31 high school football teams (Control=16, Treatment=15) in Oregon and Washington over the course of 3 years (Total N=3207). Although most interactions were nonsignificant, consistent baseline by treatment interactions were obtained for knowledge of the effects of steroid use and intentions to use steroids. Both of these interactions were beneficial in that they increased the effectiveness of the program for participants lower in knowledge and higher in intentions at baseline.
This study presents a reanalysis of data from an effective preventive intervention for children from divorced families to test mediation of program effects. The study involved 157 children, age 9-12 years, who were randomly assigned to a parenting program or a literature control condition. Program effects to reduce posttest internalizing problems were mediated through improvement in mother-child relationship quality. Program effects to reduce externalizing problems at posttest and 6 months were mediated through improvement in posttest parental methods of discipline and mother-child relationship quality. The study also describes a new methodology to test mediation of Program x Baseline Status interactions. Analyses demonstrate mediation effects primarily for children who began the program with poorer scores on discipline, mother-child relationship quality, and externalizing problems.
The current study tested a model examining both the direct and mediated effects of Ethnic Cultural Norms (ECN) on cigarette use in a sample of Mexican American youth (ages 11-14; N=921). Contextual risk factors (peer smoking and family smoking) were included as potential moderators of this mediational relationship. A product of coefficient (alpha beta) method to test the significance of the mediated effect [Evaluation Review 17 (1993) 144.] was adapted to assess the mediation of interaction effects. Results suggested that Tobacco Avoidance Self-Efficacy mediated the protective influence of ECN on cigarette use. However, as peer smoking increased, the influence of ECN on cigarette use diminished, though it remained a significant, protective influence on cigarette use. Results are discussed in terms of the potential synergy between ECN and social influence training in tobacco preventive intervention development among youth of Latin descent.
PURPOSE: To assess the deterrent effect of mandatory, random drug testing among high school (HS) athletes in a controlled setting. METHODS: Two high schools, one with mandatory drug testing (DT) consent before sports participation, and a control school (C), without DT, were assessed during the 1999-2000 school year. Athletes (A) and nonathletes (NA) in each school completed confidential (A) or anonymous (NA) questionnaires developed for this study, respectively, at the beginning and end of the school year. Positive alcohol or drug tests required parent notification and mandatory counseling without team or school suspension. Thirty percent of the DT athletes were tested. Data were analyzed using the end of the school year measure, adjusted for the initial questionnaire results. Demographics of the athlete sample revealed that mean age was 15.5 years with 81.5% white, 9.6% Hispanic, 4.5% Asian, 2.6% American Indian/Native Alaskan, 1.3% African-American, and 1.3% Native Hawaiian/Pacific Islander. RESULTS: A (n = 276) and NA (n = 507) were assessed at the beginning (baseline) and at the end of the school year (A, n = 159; NA, n = 338). The past 30-day index of illicit drugs (4-fold difference) and athletic enhancing substances (3-fold difference) were lower (p < .05) among DT athletes at follow-up without difference in alcohol use. However, most drug use risk factors, including norms of use, belief in lower risk of drugs, and poorer attitudes toward the school, increased among DT athletes (p < .05). Although a reduction in the illicit drug use index was present among nonathletes at the DT school, at the end of the school year, it did not achieve statistical significance (p < .10). CONCLUSIONS: Random DT may have reduced substance use among athletes. However, worsening of risk factors and small sample size suggests caution to this drug prevention approach. A larger long-term study to confirm these findings is necessary.
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.
Compares the use of relative risk versus population attributable fraction in determining the target population for multirisk prevention programs in psychology. Results show that relative risk generally increases as a function of cumulative risk. Guided by this measure, prevention programs should target populations with the largest cumulative risk. However, relative risk does not account for the prevalence of a particular level of cumulative risk in the population. Therefore, because the largest cumulative risk is experienced by only a small portion of the population, prevention programs guided by this measure will not always have the greatest public health benefit to reduce the incidence of problem outcomes in the population. On the other hand, the population attributable fraction, which does take into account the prevalence of a particular level of cumulative risk, does not increase appreciably after a cumulative risk of one, two, or three because the majority of people in the population will experience these levels of cumulative risk. Guided by this measure, prevention programs that target the higher proportion of people who have a more moderate level of risk would have the maximum impact on the population. National data sets from Great Britain (the British Births Cohort Study [BCS]) and the United States (National Longitudinal Study of Youth [NLSY]) are used to explore this pattern of effects.
This paper describes research designs and statistical analyses to investigate how tobacco prevention programs achieve their effects on tobacco use. A theoretical approach to program development and evaluation useful for any prevention program guides the analysis. The theoretical approach focuses on action theory for how the program affects mediating variables and on conceptual theory for how mediating variables are related to tobacco use. Information on the mediating mechanisms by which tobacco prevention programs achieve effects is useful for the development of efficient programs and provides a test of the theoretical basis of prevention efforts. Examples of these potential mediating mechanisms are described including mediated effects through attitudes, social norms, beliefs about positive consequences, and accessibility to tobacco. Prior research provides evidence that changes in social norms are a critical mediating mechanism for successful tobacco prevention. Analysis of mediating variables in single group designs with multiple mediators are described as well as multiple group randomized designs which are the most likely to accurately uncover important mediating mechanisms. More complicated dismantling and constructive designs are described and illustrated based on current findings from tobacco research. Mediation analysis for categorical outcomes and more complicated statistical methods are outlined.
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.