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.     

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.     

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.     

Social integration and social support in a life crisis: Effects of macrosocial change in east Germany

This process evaluation study examined what variables moderate and mediate treatment effects on days in stable housing and client satisfaction for homeless mentally ill clients. In general, demographic characteristics did not moderate either outcome variable. Housing contacts, entitlement contacts, mental health contacts, and supportive services were significant mediators of the treatment effect on days in stable housing. Program contacts, mental health contacts, and supportive services were significant mediators of client satisfaction. The authors thank the National Institute of Mental Health for their financial support (MH43248, MH42357) for this project, Dorothy Gano for word processing and editorial assistance, and Gitry Heydebrand, Melissa Dannelet, Betty Tempelhoff, Ruth Smith, and Laeeq Ahmad for assisting in the interviews of clients. We are also appreciative of the cooperation given to us by the staffs of Malcolm Bliss Mental Health Center, St. Patrick's Center, the Community Advocacy and Support Alliance Program, and the St. Louisemergency shelters. Finally, we thank all of the participants in this study; we hope that their homelessness has now ended. (The conclusions presented in this paper are the sole responsibility of the authors and do not necessarily reflect official National Institute for Mental Health policy.)     

Neurobehavioral deficits,adolescent traumatic brain injury,and transition to college

Outcome following traumatic brain injury (TBI) has been frequently evaluated for adults, although there has been minimal research on adolescents with TBI. It has been argued that TBI sequelae may be more difficult for adolescents to adjust to given developmental changes in physical (puberty), interpersonal (self-concept), and environmental domains (transition to college). In addition, it is commonly acknowledged that moderator variables such as psychiatric history, family functioning, substance use, and sexuality impact functional outcome following TBI, although it is often difficult to objectively evaluate these variables. The current study examined relationships among TBI-related deficits, moderator variables, and academic outcomes for six adolescents transitioning to college. The findings suggest that it may not be appropriate to predict functional outcome based solely on objective neuropsychological results. However, moderator variables appear to have a more direct relationship with outcome, depending on the moderator variable and the individual.     

军队初级指挥官PM领导行为类型的研究——与中、日企业领导行为的比较

本研究采用日本三隅二不二教授的PM领导行为理论 ,用中国修订版的PM量表对 2 64名初级军官的领导行为类型进行了评定 ,并与对中、日企业界的有关研究结果进行了比较 ,目的是研究初级军官所特有的领导行为特征以及与其相对应的情境因素。研究结果显示 :①在军事组织中 ,PM型是较理想的领导类型 ,其次为P型。这与中、日企业的结果有所不同。②方差分析结果表示 ,在与领导行为类型相关的八项情境因素中 ,初级军官在工作激励、连队满意度、团队精神和会议成效等因子上高于中、日企业界 ;但在心理卫生、对待遇满意度、信息沟通和绩效规范等因子上均低于中国企业界。③ χ2 分析结果显示 ,三种组织与四种PM行为类型之间在六项情境因素上的差异显著 ,提示三种组织在领导行为和情境因素上各有其特点 ;另外 ,与军事指挥官领导行为类型关系最为密切的是工作激励因素和团队精神 ,这是军事集团突出的特征之一。④中、日企业界在会议成效和信息沟通两因素上的差异较大 ,反映了两国在企业管理方面不同的特点。