Research in education and behavioral sciences often involves the use of latent variable models that are related to indicators, as well as related to covariates or outcomes. Such models are subject to interpretational confounding, which occurs when fitting the model with covariates or outcomes alters the results for the measurement model. This has…

Typical longitudinal growth models assume constant functional growth over time. However, there are often conditions where trajectories may not be constant over time. For example, trajectories of psychological behaviors may vary based on a participant's age, or conversely, participants may experience an intervention that causes trajectories to…

The present study aimed at clarifying the various shortcomings of the Cohen's criteria for the interpretation of the values of the practical significance indicators. The hypothetical data were used for two experimental and control groups and calculating the paired-samples t-test. To clarify the inadequacy of Cohen's criteria in interpreting…

It is widely believed that regression models for binary responses are problematic if we want to compare estimated coefficients from models for different groups or with different explanatory variables. This concern has two forms. The first arises if the binary model is treated as an estimate of a model for an unobserved continuous response and the…

The multilevel social relations model (SRM) is a commonly used statistical method for the analysis of social networks. In this article and accompanying supplemental materials, we demonstrate the estimation and interpretation of the SRM using Stat-JR software. Multiple software templates permit the analysis of different response types, including…

The article serves as a guide for researchers in developing evidence of validity using bivariate correlations, specifically construct validity. The authors outline the steps for calculating and interpreting bivariate correlations. Additionally, they provide an illustrative example and discuss the implications.

There are no absolute standards regarding what teaching evaluation ratings are satisfactory. It is also problematic to compare teaching evaluation ratings with the average or with a cutoff number to determine whether they are adequate. In this paper, we use average and standard deviation charts (X[overbar]-S charts), which are based on the theory…

Hierarchical linear modeling (HLM) is a useful tool when analyzing data collected from groups. There are many decisions to be made when constructing and estimating a model in HLM including which estimation technique to use. Three of the estimation techniques available when analyzing data with HLM are maximum likelihood, restricted maximum…

This case study presents an example of using a generalized linear model with a log-linear link to calculate an adjusted risk ratio to be able to assess the association between educational attainment and obesity in a cohort study of American adults. Both risk ratios and odds ratios can be calculated based on cohort study data, and risk ratios are…

As literature has reported, it is usual that university students in statistics courses, and even statistics teachers, interpret the confidence level associated with a confidence interval as the probability that the parameter value will be between the lower and upper interval limits. To confront this misconception, class activities have been…

Research has shown that forest plots are a gold standard in the visualization of meta-analytic results. However, research on the general interpretation of forest plots and the role of researchers' meta-analysis experience and field of study is still unavailable. Additionally, the traditional display of effect sizes, confidence intervals, and…

Quantitative reasoning is a key intellectual skill, applicable across disciplines and best taught in the context of authentic, relevant problems. Here, I describe and assess a laboratory exercise that has students calculate their "carbon footprint" and evaluate the impacts of various behavior choices on that footprint. Students gather…

The use of confidence intervals (CIs) as an addition or as an alternative to null hypothesis significance testing (NHST) has been promoted as a means to make researchers more aware of the uncertainty that is inherent in statistical inference. Little is known, however, about whether presenting results via CIs affects how readers judge the…

Confidence intervals (CIs) and effect sizes are essential to encourage meta-analytic thinking and to accumulate research findings. CIs provide a range of plausible values for population parameters with a degree of confidence that the parameter is in that particular interval. CIs also give information about how precise the estimates are. Comparison…

Written by a social worker for social work students, this is a nuts and bolts guide to statistics that presents complex calculations and concepts in clear, easy-to-understand language. It includes numerous examples, data sets, and issues that students will encounter in social work practice. The first section introduces basic concepts and terms to…