Exploratory factor analysis (EFA) is one of the most commonly-reported quantitative methodology in the social sciences, yet much of the detail regarding what happens during an EFA remains unclear. The goal of this brief technical note is to explore what "rotation" is, what exactly is rotating, and why we use rotation when performing…

Osborne and Waters (2002) focused on checking some of the assumptions of multiple linear regression. In a critique of that paper, Williams, Grajales, and Kurkiewicz correctly clarify that regression models estimated using ordinary least squares require the assumption of normally distributed errors, but not the assumption of normally distributed…

The present study was designed to test for item order effects by measuring four distinct constructs that contribute substantively to anxiety-related psychopathology (i.e., anxiety sensitivity, fear of negative evaluation, injury/illness sensitivity, and intolerance of uncertainty). Participants (n = 999; 71% women) were randomly assigned to…

Logistic regression is slowly gaining acceptance in the social sciences, and fills an important niche in the researcher's toolkit: being able to predict important outcomes that are not continuous in nature. While OLS regression is a valuable tool, it cannot routinely be used to predict outcomes that are binary or categorical in nature. These…

Exploratory Factor Analysis (EFA) is a powerful and commonly-used tool for investigating the underlying variable structure of a psychometric instrument. However, there is much controversy in the social sciences with regard to the techniques used in EFA (Ford, MacCallum, & Tait, 1986; Henson & Roberts, 2006) and the reliability of the outcome.…

Large surveys often use probability sampling in order to obtain representative samples, and these data sets are valuable tools for researchers in all areas of science. Yet many researchers are not formally prepared to appropriately utilize these resources. Indeed, users of one popular dataset were generally found "not" to have modeled the analyses…

Many of us in the social sciences deal with data that do not conform to assumptions of normality and/or homoscedasticity/homogeneity of variance. Some research has shown that parametric tests (e.g., multiple regression, ANOVA) can be robust to modest violations of these assumptions. Yet the reality is that almost all analyses (even nonparametric…

Before the mid 20th century most scientific writing was solely authored (Claxton, 2005; Greene, 2007) and thus it is only relatively recently, as science has grown more complex, that the ethical and procedural issues around authorship have arisen. Fields as diverse as medicine (International Committee of Medical Journal Editors, 2008), mathematics…

Introduces the problem of hierarchical, or nested, data structures and discusses how this problem is dealt with effectively. Provides examples of the pitfalls of not doing appropriate analyses. New software is making hierarchical linear modeling more user-friendly and accessible. (SLD)

Presents the concept of prediction via multiple regression (MR) and discusses the assumptions underlying multiple regression analyses. Also discusses shrinkage, cross-validation, and double cross-validation of prediction equations and describes how to calculate confidence intervals around individual predictions. (SLD)