Step your way through Path Analysis

This presentation provides a plan to step from regression to a path analysis. Regression analysis sometimes provides less than optimal results using a default model. Path analysis allows you to specify a model and relationships between variables. PROC CALIS provides a method to specify a path analysis model, examine relationships between variables, and provides a comprehensive analysis of your data. Introduction Do you want more information from your data? Would you like more than regression analysis? Path analysis allows specification of relationships between variables. This paper will review the basics of path analysis with structural equation modeling methodology and then present an example comparing results from regression analysis and path analysis Purpose The purpose of this presentation is to offer an alternative approach to regression analysis and a step-by-step approach to path analysis with structural equation modeling methodology (SEM). The goals are to present a powerful, flexible and comprehensive technique for investigating relationships between measured variables and to challenge you to design and plan research where SEM is an appropriate analysis tool. Rationale Analyzing research data and interpreting results could be complex and confusing. Traditional statistical approaches to data analysis specify default models, assume measurement occurs without error, and are somewhat inflexible. However, structural equation modeling requires specification of a model based on theory and research, is a multivariate technique incorporating measured variables and latent constructs, and explicitly specifies measurement error. A model (diagram) allows for specification of relationships between variables. Structural equation modeling (SEM) • is a comprehensive statistical approach to testing hypotheses about relations among observed and latent variables (Hoyle, 1995). • is a methodology for representing, estimating, and testing a theoretical network of (mostly) linear relations between variables (Rigdon, 1998). • tests hypothesized patterns of directional and nondirectional relationships among a set of observed (measured) and unobserved (latent) variables (MacCallum & Austin, 2000). Two goals in SEM are 1) to understand the patterns of correlation/covariance among a set of variables and 2) to explain as much of their variance as possible with the model specified (Kline, 1998). The purpose of the model, in the most common form of SEM, is to account for variation and covariation of the measured variables (MVs). Path analysis (e.g., regression) tests models and relationships among MVs. Confirmatory factor analysis tests models of relationships between latent variables (LVs or common factors) and MVs which are indicators of common factors. Similarities between Regression and Path Analysis Path Analysis with SEM is similar to traditional methods like correlation and regression in many ways. First, both regression and path analysis are based on linear statistical models. Second, statistical tests associated with both methods are valid if certain assumptions are met. Regression methods assume a normal distribution and Path Analysis assumes multivariate normality. Third, neither approach offers a test of causality. Differences Between Regression and Path Analysis Regression analysis differs from Path Analysis in several areas. First, Path Analysis is a highly flexible and comprehensive methodology. This methodology is appropriate for investigating achievement, economic trends, health issues, family and peer dynamics, self-concept, exercise, self-efficacy, depression, psychotherapy, and other phenomenon.