Previous research explicates ordinal and disordinal interactions through the concept of the "crossover point." This point is determined via simple regression models of a focal predictor at specific moderator values and signifies the intersection of these models. An interaction effect is labeled as disordinal (or ordinal) when the…

Post-instrument covariates are often included as controls in instrumental variable (IV) analyses to address a violation of the exclusion restriction. However, we show that such analyses are subject to biases unless strong assumptions hold. Using linear constant-effects models, we present asymptotic bias formulas for three estimators (with and…

The following study shows how reparameterizations and constraints of the general linear model can serve to parse quantitative and qualitative aspects of predictors. We demonstrate three different approaches. The study uses data from the High School Longitudinal Study of 2009 on mathematics course-taking and achievement as an example. Results show…

Given the simultaneous rise in time-to-graduation and college GPA, it may be that students reduce their course load to improve their performance. Yet, evidence to date only shows increased course loads "increase" GPA. We provide a mathematical model showing many unobservable factors -- beyond student ability -- can generate a positive…

Contemporary case studies rely on verbal arguments and set theory to build or evaluate theoretical claims. While existing procedures excel in the use of qualitative information (information about kind), they ignore quantitative information (information about degree) at central points of the analysis. Effectively, contemporary case studies rely on…

When estimating causal effects, unmeasured confounding and model misspecification are both potential sources of bias. We propose a method to simultaneously address both issues in the form of a semi-parametric sensitivity analysis. In particular, our approach incorporates Bayesian Additive Regression Trees into a two-parameter sensitivity analysis…

It is common in regression discontinuity analysis to control for high order (third, fourth, or higher) polynomials of the forcing variable. We argue that estimators for causal effects based on such methods can be misleading, and we recommend researchers do not use them, and instead use estimators based on local linear or quadratic polynomials or…

If students really understand the systems they study, they would be able to tell how changes in the system would affect a result. This demands that the students understand the mechanisms that drive its behaviour. The study investigates potential merits of learning how to explicitly model the causal structure of systems. The approach and…

Linearization is a standard part of modeling and control design theory for a class of nonlinear dynamical systems taught in basic undergraduate courses. Although linearization is a straight-line methodology, it is not applied correctly by many students since they often forget to keep the operating point in mind. This paper explains the topic and…

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)

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)

An integration of two different approaches to the psychology of causal induction is proposed that overcomes the problems associated with each. The proposal results in a causal power theory of the probabilistic contrast model of P. W. Cheng and L. R. Novick (1990). (SLD)

A causal model of halo error (HE) is derived. Three hypotheses are formulated to explain findings of negative HE. It is suggested that apparent negative HE may have been misinferred from existing correlational measures of HE, and that positive HE is more prevalent than had previously been thought. (SLD)

Biases and models usually offered by cognitive and social psychology and by philosophy to explain causal induction are evaluated with respect to focal sets (contextually determined sets of events over which covariation is computed). A probabilistic contrast model is proposed as underlying covariation computation in natural causal induction. (SLD)

An author of a paper (EC 602 060) that presents two causal models of learning disabilities comments on his interest in the research, how the research design developed, limitations of the research, and the need for replication with other disability groups. (JDD)