Event Phone: 1-610-715-0115

Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 4-Day Livestream Seminar Taught by Kristopher Preacher, Ph.D.

This seminar covers primarily Exploratory Factor Analysis (EFA), which is used extensively in psychology, education, medicine, and management to identify underlying factors or dimensions that explain the variability in a set of observed variables. EFA can be a powerful tool for researchers, providing a number of benefits.

First, EFA allows researchers to represent the observed variables with a smaller number of underlying factors or dimensions. In other words, EFA can help researchers to identify the underlying structure of complex data. This can be helpful when dealing with large data sets, as it simplifies the analysis and can help identify key underlying relationships among the variables. This also can be useful for developing and testing theories and models that explain the behavior of the variables.

Second, EFA can help researchers to identify new variables that may be related to the factors or dimensions identified by the analysis. This can lead to new hypotheses and research questions, as well as new insights into the relationships among variables.

Third, EFA can be used to evaluate the reliability and validity of measurement scales. By identifying the key factors that underlie a set of measurement items, researchers can assess whether the items are measuring the same construct, how well they do so, and whether the scale is correlated in expected ways with other variables.

The seminar covers the theory behind factor analysis, hands-on application to data, exposure to uses of factor analysis in the applied literature, and instruction in popular, freely available EFA software. Key topics include model specification, model fit and evaluation, factor rotation methods, multiple-item instrument and questionnaire development, and sample size and power issues.

The objective of this seminar is to obtain (a) a firm grounding in the statistical theory of exploratory factor analysis as it is employed in the social and behavioral sciences, and (b) practical experience factor analyzing data to better understand its underlying structure. More specifically, you can expect to:

• • Gain an understanding of the central statistical concepts underlying the methods (e.g., parameter estimation, factor rotation, goodness of fit).
  • Learn a variety of factor analytic techniques (e.g., principal axis and maximum likelihood factor extraction, estimating the number of factors, scale construction).
  • Gain experience conducting factor analyses with R, interpreting results, and drawing meaningful substantive conclusions.

In other words, you will become an educated consumer and producer of research involving (or about) factor analysis.

Two books recommended as companion pieces (not required, but highly recommended):

• • Fabrigar & Wegener (2011). Exploratory Factor Analysis.
  • Watkins (2020). A Step-by-Step Guide to Exploratory Factor Analysis with R and RStudio.