Stolen elections: How conspiracy beliefs during the 2020 American presidential elections changed over time

Conspiracy beliefs have been studied mostly through cross-sectional designs. We conducted a five-wave longitudinal study (N = 376; two waves before and three waves after the 2020 American presidential elections) to examine if the election results influenced specific conspiracy beliefs and conspiracy mentality, and whether effects differ between election winners (i.e., Biden voters) versus losers (i.e., Trump voters) at the individual level. Results revealed that conspiracy mentality kept unchanged over 2 months, providing first evidence that this indeed is a relatively stable trait. Specific conspiracy beliefs (outgroup and ingroup conspiracy beliefs) did change over time, however. In terms of group-level change, outgroup conspiracy beliefs decreased over time for Biden voters but increased for Trump voters. Ingroup conspiracy beliefs decreased over time across all voters, although those of Trump voters decreased faster. These findings illuminate how specific conspiracy beliefs are, and conspiracy mentality is not, influenced by an election event.     

A maximum likelihood method for latent class regression involving a censored dependent variable

The standard tobit or censored regression model is typically utilized for regression analysis when the dependent variable is censored. This model is generalized by developing a conditional mixture, maximum likelihood method for latent class censored regression. The proposed method simultaneously estimates separate regression functions and subject membership in K latent classes or groups given a censored dependent variable for a cross-section of subjects. Maximum likelihood estimates are obtained using an EM algorithm. The proposed method is illustrated via a consumer psychology application.     

Category discrimination by pigeons using five polymorphous features

Eight pigeons were trained to discriminate between sets of color photographs of natural scenes. The scenes differed along five two-valued dimensions (site, weather, camera distance, camera orientation, and camera height), and all combinations of the feature values were used. One value of each dimension was designated as positive, and slides containing three or more positive feature values were members of the positive stimulus set. Thus, each feature had an equal, low, correlation with reinforcement, and all features had zero correlations with each other. Seven of the 8 pigeons learned this discrimination, and their responding came under the control of all five features. Within the positive and negative stimulus sets, response rates were higher to stimuli that contained more positive feature values. Once discrimination had been achieved, reversal training was given using a subset of the slides. In this subset, only a single feature was correlated with reinforcement. All pigeons learned this reversal successfully and generalized it to additional photographs with the same feature content. After reversal, the original reinforcement contingencies were reinstated, and training was continued using all the slides except those that had been used in reversal. Reversal generalized to these slides to some extent. Analysis of the response rates to individual slides showed that, compared with prereversal training, only the feature that had been subjected to reversal contingencies showed a reversed correlation with response rate. The remaining features showed the same correlation with response rate as they had before reversal training. Thus, reversal on some members of a category following category discrimination training led to generalization to stimuli within the category that were not involved in the reversal, but not to features that were not reversed. It is therefore inappropriate to refer to the pigeons as learning a concept.     

Analyzing sequential categorical data: Individual variation in markov chains

Markov chains are probabilistic models for sequences of categorical events, with applications throughout scientific psychology. This paper provides a method for anlayzing data consisting of event sequences and covariate observations. It is assumed that each sequence is a Markov process characterized by a distinct transition probability matrix. The objective is to use the covariate data to explain differences between individuals in the transition probability matrices characterizing their sequential data. The elements of the transition probability matrices are written as functions of a vector of latent variables, with variation in the latent variables explained through a multivariate regression on the covariates. The regression is estimated using the EM algorithm, and requires the numerical calculation of a multivariate integral. An example using simulated cognitive developmental data is presented, which shows that the estimation of individual variation in the parameters of a probability model may have substantial theoretical importance, even when individual differences are not the focus of the investigator's concerns.Research contributing to this article was supported by B.R.S. Subgrant 5-35345 from the University of Virginia. I thank the DADA Group, Bill Fabricius, Don Hartmann, William Griffin, Jack McArdle, Ivo Molenaar, Ronald Schoenberg, Simon Tavaré, and several anonymous reviewers for their discussion of these points.     

Continuous and discrete latent structure models for item response data

Relations are examined between latent trait and latent class models for item response data. Conditions are given for the two-latent class and two-parameter normal ogive models to agree, and relations between their item parameters are presented. Generalizationss are then made to continuous models with more than one latent trait and discrete models with more than two latent classes, and methods are presented for relating latent class models to factor models for dichotomized variables. Results are illustrated using data from the Law School Admission Test, previously analyzed by several authors.     

The Multitextured Lives of Women of Color

In this commentary, I suggest that our work with women of color should be held together by three primary threads: (a) appreciation for history as we decipher and interpret the adaptive strategies of women of color; (b) recognition of the intersections of race, gender, and class as central to our work; and (c) self-critical examination of the meanings we attach to difference. The papers in the current special issue provide good examples regarding the importance of each thread. I argue that attention to these three themes should help us to keep our focus on contextually driven questions and to move forward our appreciation for the lives of women of color.     

Residualanalysis in the polytomous rasch model

Residuals for check of model fit in the polytomous Rasch model are examined. Comparisons are made between using counts for all response pattern and using item totals for score groups for the construction of the residuals. Comparisons are also, for the residuals based on score group totals, made between using as basis the item totals, or using the estimated item parameters. The developed methods are illustrated by two examples, one from a psychiatric rating scale, one from a Danish Welfare Study.     

An extension of the concept of specific objectivity

Comparisons of subjects are specifically objective if they do not depend on the items involved. Such comparisons are not restricted to the 1-parameter logistic latent trait model, but may also be defined within ordinal independence models and even within the 2-parameter logistic model.     

Content variations and performance on formal operational tasks by gender,social class and ability1

The role of task content, gender, social class and cognitive ability in performance on two formal operational tasks is explored in this paper. The participants were 110 Icelandic 12-year-olds, early and late developers, males and females from all socio-economic groups. Two tasks of variable isolation (the pendulum and three content versions of the “plant” problem) and two measures of volume conservation (three traditional tasks and three tasks formally identical whose content was pictorial social play) were presented individually in a school setting. The results suggest that content variations have differential consequences for the performance of early and late developers at age 12. Both the nature of logical competence and task content affect solutions, as suggested by Overton and collaborators in 1987. Some social class and gender differences were observed on two Piagetian tasks (pendulum and volume), which was reduced by changing the task content in the case of social class but not in that of gender. As the gender difference in volume conservation was not reflected ins chool achievement nor on other cognitive tasks it was interpreted as a gender bias. The findings are discussed in view of recent literature on the development of cognitive theory with increased emphasis on context and meaning in a post-structural intellectual climate.