The Making of Psychological Methods

Psychological Methods celebrated its 20-year anniversary recently, having published its first quarterly issue in March 1996. It seemed time to provide a brief overview of the history, the highlights over the years, and the current state of the journal, along with tips for submissions. The article is organized to discuss: (1) the background and development of the journal, (2) the top articles, authors and topics over the years, (3) an overview of the journal today, and (4) a summary of the features of successful articles that usually entail rigorous and novel methodology described in clear and understandable writing and that can be applied in meaningful and relevant areas of psychological research

Reporting Practices and Use of Quantitative Methods in Canadian Journal Articles in Psychology

With recent focus on the state of research in psychology, it is important to assess the nature of the methods and analyses used and reported. To study this, we coded information about the statistical content reported in articles in the four major Canadian psychology journals published in 2013. We first classified whether the articles were quantitative, qualitative, or theoretical in nature. Our main focus was on articles that used quantitative methods; whereby we investigated the prevalence of different statistical procedures, as well as further details of reporting practices. Few articles in any of the journals used qualitative approaches, as 92.9% of empirical articles included a quantitative study. Analysis of variance (ANOVA), t-tests, and multiple regression were the statistical analyses most often reported in the investigated articles. The majority of articles used hypothesis testing, and while most of these tests were accompanied by an effect size, this rarely included a confidence interval. Many of the quantitative studies provided minimal details about their statistical analyses and less than a third of the studies presented on data complications such as missing data and statistical assumptions. Further discussion highlights strengths and areas for improvement for reporting quantitative results. The paper concludes with recommendations for how researchers and reviewers can improve comprehension in statistical reporting

Big Data in Psychology: Introduction to the Special Issue

The introduction to this special issue on psychological research involving big data summarizes the highlights of 10 articles that address a number of important and inspiring perspectives, issues, and applications. Four common themes that emerge in the articles with respect to psychological research conducted in the area of big data are mentioned, including: 1. The benefits of collaboration across disciplines, such as those in the social sciences, applied statistics, and computer science. Doing so assists in grounding big data research in sound theory and practice, as well as in affording effective data retrieval and analysis. 2. Availability of large datasets on Facebook, Twitter, and other social media sites that provide a psychological window into the attitudes and behaviors of a broad spectrum of the population. 3. Identifying, addressing, and being sensitive to ethical considerations when analyzing large datasets gained from public or private sources. 4. The unavoidable necessity of validating predictive models in big data by applying a model developed on one dataset to a separate set of data or hold-out sample. Translational abstracts that summarize the articles in very clear and understandable terms are included in Appendix A, and a glossary of terms relevant to big data research discussed in the articles is presented in Appendix B. Keywords: big data, machine learning, statistical learning theory, social media data, digital footprint, decision trees and forests

Modeling multiple health behaviors and general health

Multiple Health Behavior Change assumes health behaviors are related to one another, although research evidence is mixed. More research is needed to understand which behaviors are most closely related and how they collectively predict health. Principle component analysis and structural equation modeling were used to establish a model showing relations between health behaviors, including fruit/vegetable consumption, aerobic and strength exercise, alcohol intake, and smoking, and how these behaviors relate to general physical and mental health functioning in a large, national sample. Although health behaviors were found to coalesce into a health-promoting factor of diet, and exercise, a better overall model fit was found when all behaviors were modeled as separate independent variables. Results suggest that health behaviors relate to one another in complex ways, with perceived health status serving as a mediating variable between specific health behaviors and a factor of physical and mental health. Future research should further investigate how other health behaviors relate to perceptions and overall health, especially among subpopulations

Exercise and Self-Esteem: Validity of Model Expansion and Exercise Associations

The purpose of this research was to test expansion of the Exercise and Self-Esteem Model (EXSEM) to include two levels of perceived physical competence as operationalized by the Physical Self-Perception Profile (PSPP). Female aerobic dancers (N = 216, age M = 38.4) were administered a Self-Esteem scale (SE), the PSPP to assess a general Physical Self-Worth (PSW), and more specific subdomains of perceived Sport Competence (Sport), Physical Condition (Cond), Attractive Body (Body), and Strength (Stren). Subjects also completed self-efficacy scales for jogging, sitting, and aerobic dancing. Confirmatory factor analysis supported model measurement as hypothesized, ?2 = 1,154.88, df = 681, comparative fit index (CFI) = .913, root mean square residual (RMSR) = .047. Structural equation modeling (SEM) supported EXSEM component relationships as proposed. Further SEM associating two exercise self-reports with EXSEM again displayed satisfactory fit indices and explained up to 27.6% of exercise variance. It was concluded that exercise in adult female aerobic dancers is associated with positive evaluations of their physical condition and with negative evaluations of their bodies

A measurement model of women\u27s behavioral risk taking

The current study was designed to gain a better understanding of the nature of the relationship between substance use and sexual risk taking within a community sample of women (N = 1,004). Using confirmatory factor analysis, the authors examined the factor structure of sexual risk behaviors and substance use to determine whether they are best conceptualized as domains underlying a single, higher order, risk-taking propensity. A 2 higher order factor model (sexual risk behavior and substance use) provided the best fit to the data, suggesting that these 2 general risk domains are correlated but independent factors. Sensation seeking had large general direct effects on the 2 risk domains and large indirect effects on the 4 first-order factors and the individual indicators. Negative affect had smaller, yet still significant, effects. Impulsivity and anxiety were unrelated to sexual health risk domains

Longitudinal analysis of intervention effects on temptations and stages of change for dietary fat using parallel process latent growth modeling

The Dietary Guidelines for Americans recommend a 20–35 percent daily intake of fat. Resisting the temptation to eat high-fat foods, in conjunction with stage of readiness to avoid these foods, has been shown to influence healthy behavior change. Data (N = 6516) from three randomized controlled trials were pooled to examine the relationships among direct intervention effects on temptations and stage of change for limiting high-fat foods. Findings demonstrate separate simultaneous growth processes in which baseline level of temptations, but not the rate of change in temptations, was significantly related to the change in readiness to avoid high-fat foods

Demographics and the Cost of Pharmaceuticals in a Private Third-Party Prescription Program

Objective: To compare variance in the cost of pharmaceuticals attributable to demographic variables with variance explained by plan characteristics, using prescription claims data within various therapeutic categories, and to examine differences in average cost of pharmaceuticals among demographic variables after controlling for covariates. Design: Retrospective, cross-sectional study. Data Collection: Data for this study were obtained from 1996 prescription claims information for the commercial population administered by a Rhode Island-based pharmacy benefit management (PBM) company. Six therapeutic categories with the highest expenditures were analyzed. Information on claims for six drug categories was extracted using database management software. Statistical analyses using multiple regression and analysis of covariance were carried out. Results: Plan characteristics outperformed demographic variables sixteenfold for all drug categories combined in explaining variance in cost of pharmaceuticals among plan enrollees. Average cost of pharmaceuticals differed among demographic variables such as age, gender, location, and place of employment after controlling for average wholesale price and days supply. Conclusions: The results obtained in this study have practical significance in the determination of capitation rates when utilization history of prospective members is not available. In this situation, managed care organizations (MCOs) or PBMs may have to set capitation rates based solely on eligibility data. Significant differences in average drug costs among the members based on place of employment suggest that benefit managers should consider differentiating capitation rates according to their clients\u27 businesses. Finally, the data from this study indicated that commercial members residing in Tennessee had the lowest average cost of pharmaceuticals among all states evaluated. The fact that one PBM manages more than 80% of the TennCare prescription program along with a significant commercial client base suggests that a spillover effect may exist

Keep Your Stats in the Cloud! Evaluating the Use of Google Sheets to Teach Quantitative Methods

Teaching quantitative methods at the undergraduate level is a difficult yet rewarding endeavor due to the challenges instructors face in presenting the material. One way to bolster student learning is through the use of statistical software packages. Google Sheets is a cloud-based spreadsheet program capable of many basic statistical procedures, which has yet to be evaluated for use in quantitative methods courses. This article contains pros and cons to using Google Sheets in the classroom and provides an evaluation of student attitudes toward using Google Sheets in an introductory quantitative methods class. The results suggest favorable student attitudes toward Google Sheets and which attitudes toward Google Sheets show a positive relationship with quantitative self-efficacy. Thus, based on the positive student attitudes and the unique features of Google Sheets, it is a viable program to use in introductory methods classes. However, due to limited functionality, Google Sheets may not be useful for more advanced courses. Future research may want to evaluate the use of third-party Google Sheets applications, which can increase functionality, and the use of Google Sheets in online classes

School Toileting Environment, Bullying, and Lower Urinary Tract Symptoms in a Population of Adolescent and Young Adult Girls:Preventing Lower Urinary Tract Symptoms Consortium Analysis of Avon Longitudinal Study of Parents and Children

AIM: Little is known about the association of the school toilet environment with voiding behaviors and lower urinary tract symptoms (LUTS) in adolescents. The purpose of the present longitudinal, secondary data analysis is to examine whether the school toilet environment at age 13, including bullying, is associated with LUTS at ages 13 and 19. METHODS: The sample comprised 3962 female participants from the Avon Longitudinal Study of Parents and Children (ALSPAC). At age 13, participants reported on 7 school toilet environment characteristics and a range of LUTS items. At age 19, participants completed the Bristol Female Lower Urinary Tract Symptoms (ICIQ-BFLUTS) questionnaire. RESULTS: All toilet environmental factors were associated with at least one LUTS outcome at age 13. Holding behavior was associated with all school toilet environmental factors, with odds ratios (ORs) ranging from 1.36 (95% CI: 1.05, 1.76) for dirty toilets to 2.38 (95% CI: 1.60, 3.52) for feeling bullied at toilets. Bullying was associated with all LUTS symptoms; ORs ranged from 1.60 (95% CI: 1.04, 2.07) for nocturia to 2.90 (95% CI: 1.77, 4.75) for urgency. Associations between age 13 school toilets and age 19 LUTS were in the same direction as age 13 LUTS. CONCLUSION: This is the first examination of associations between school toilets and LUTS. Toileting environments were cross-sectionally associated with LUTS in adolescent girls. While further work is needed to determine whether these associations are causal, school toilet environments are modifiable and thus a promising target for LUTS prevention