Provides an introduction to the structural equation modeling concepts developed by J. Pearl, discussing the concept he calls "d-separation." Explains how d-separation connects to control variables, partial correlations, causal structuring, and even a potential mistake in regression. (SLD)

This paper examines the estimation of two-stage clustered RCT designs in education research using the Neyman causal inference framework that underlies experiments. The key distinction between the considered causal models is whether potential treatment and control group outcomes are considered to be fixed for the study population (the…

This paper asserts that causality is an intriguing but controversial topic in philosophy, statistics, and educational and psychological research. By supporting the Causal Markov Condition and the faithfulness condition, Clark Glymour attempted to draw causal inferences from structural equation modeling. According to Glymour, in order to make…

Beginning with Version 11, SPSS implemented the MIXED procedure, which is capable of performing many common hierarchical linear model analyses. The purpose of this article was to provide a tutorial for performing cross-sectional and longitudinal analyses using this popular software platform. In doing so, the authors borrowed heavily from Singer's…

A theoretical and philosophical revision of the concept of fit in structural equation modeling and its relation to a confirmation bias is developed. The neutral character of fit indexes regarding this issue is argued, concluding that protection against confirmation bias relies on model modification strategy and scientist behavior. (SLD)

D. B. Rubin's model for causal inference in experiments and observational studies is enlarged to analyze the problem of "causes causing causes" and is compared to path analysis and recursive structural equations models. A special quasiexperimental design, the encouragement design, is used to give concreteness to the discussion by…

Problems in the measurement of latent variables (LVs) in structural equations causal models are presented, with examples from recent empirical studies. Main issues addressed are (1) two general types of increased operative variables; (2) criteria for indicators of LVs; and (3) disattenuation effects in LV models. (SLD)

Discusses linear structural relations, Two Stage Least Squares, and path analysis as statistical procedures that sometimes permit causal inferences from correlational findings. Even though two variables cannot be interpreted causally due to a possible but unknown third variable, these methods are appropriate for handling models with correlated…

Fitted structural equation models to covariances among 9 cognitive abilities Test variables, 11 intelligence test variables, and 3 achievement test scaled scores of 532 primary school children in a twin studies project. Results suggest that individual differences in mental speed are a main causal factor underlying correlation between general…

A group of 149 adult survivors of childhood sexual abuse was tested using a causal model that identifies relationships among sexual abuse situation characteristics, the accomplishment of cognitive coping tasks, and long-term effects. Results indicated the model did not fit the data. A revised model is proposed and examined. (JBJ)

Nonlinear relationships and latent variable assumptions can lead to serious specification errors in structural models. A quadratic relationship, described by a linear structural model with a latent variable, is shown to have less predictive validity than a simple manifest variable regression model. Advocates the use of simpler preliminary…

The logical flaw of using the methods of path analysis and structural equation modeling to make causative conclusions is demonstrated. A proper evaluation of the role of investigation versus experimentation is cited in the work of W. G. Cochran as explicated by D. R. Rubin (1983). (TJH)

Data on academic self-concept, school grades, and teacher ratings of achievement were collected from 603 students for three high school subjects in each of three years. Structural equation models show reciprocal effects for prior achievement and academic self-concept for all subjects. Effects were largest for mathematics. (SLD)

This study compared the strength of competing causal models in explaining the relationship between perceived support, enacted support, and social anxiety in adolescents. The social causation hypothesis postulates that social support causes social anxiety, whereas the social selection hypothesis postulates that social anxiety causes social support.…

This study investigates the interrelationships of academic causal attributions, academic self-concept, learning approaches, and their effects on academic achievement among Hong Kong Chinese tertiary students. It was hypothesized that academic causal attributions and academic self-concept affect the learning approaches students adopt and…