Nonrecursive Causal Model Research Articles

An empirical investigation of the Malcolm Baldridge National Quality Award framework using causal Latent Semantic Analysis

Numerous studies have investigated the linkages implied in the Malcolm Baldrige National Quality Award (MBNQA) framework. Those studies posited that the MBNQA quality experts defaulted to the premise that each construct is related to all others in the MBNQA framework because of the lack of specific knowledge about the causative relationships. Therefore, there is a need for both academicians and managers to explore the MBNQA framework as a non-recursive causal model as it was originally developed. This study uses a causal latent semantic analysis methodology to test the MBNQA as a non-recursive causal model using textual data obtained from scholarly MBNQA publications. Though the MBNQA framework is yet to be fully explored by both academicians and practitioners, this is the first study to show that the cumulative finding of prior research supports the contention that the constructs in the framework have substantial influence on each other.

Institutional Quality and Generalized Trust: A Nonrecursive Causal Model

This paper investigates the association between institutional quality and generalized trust. Despite the importance of the topic, little quantitative empirical evidence exists to support either unidirectional or bidirectional causality for the reason that cross-sectional studies rarely model the reciprocal relationship between institutional quality and generalized trust. Using data from the World Values Survey, World Bank, and other data sources in an identified nonrecursive structural equation model, results show that generalized trust and institutional quality form a positive reciprocal relationship, where the connection is stronger from generalized trust to institutional quality. The conclusion discusses implications for theory and policy in this area.

Impact of current grief on memory for past grief in spousal bereavement

This study examined the impact of changes in level of current grief over time on changes in memory for past grief. Following from previous work on the impact of current affective state on memory for past affectively salient events, bereaved individuals who underwent a greater reduction in grief over time were expected to show a similar pattern of changes in memory for past grief. A sample of conjugally bereaved participants completed measures of current grief and memory for past grief at the time of the loss of their spouse at each of the 2, 6, 13, and 30 month post-loss time periods. As predicted, those who reported greater reduction in grief over the course of the study were more likely to remember their past grief as progressively less severe relative to those who underwent less reduction in grief over time. A nonrecursive causal model analysis verified that changes in memory for past grief were explained by changes in current grief. These results were discussed in terms of contemporary reconstructive memory accounts of the effect of affective state on memory.

An Axiomatic Characterization of Causal Counterfactuals

This paper studies the causal interpretation of counterfactual sentences using a modifiable structural equation model. It is shown that two properties of counterfactuals, namely, composition and effectiveness, are sound and complete relative to this interpretation, when recursive (i.e., feedback-less) models are considered. Composition and effectiveness also hold in Lewis's closest-world semantics, which implies that for recursive models the causal interpretation imposes no restrictions beyond those embodied in Lewis's framework. A third property, called reversibility, holds in nonrecursive causal models but not in Lewis's closest-world semantics, which implies that Lewis's axioms do not capture some properties of systems with feedback. Causal inferences based on counterfactual analysis are exemplified and compared to those based on graphical models.

Markov properties of nonrecursive causal models

This paper aims to solve an often noted incompatibility between graphical chain models which elucidate the conditional independence structure of a set of random variables and simultaneous equations systems which focus on direct linear interactions and correlations between random variables. Various authors have argued that the incompatibility arises mainly from the fact that in a simultaneous equations system (e.g., a LISREL model) reciprocal causality is possible whereas this is not so in the case of graphical chain models. In this article it is shown that this view is not correct. In fact, the definition of the Markov property embodied in a graph can be generalized to a wider class of graphs which includes certain nonrecursive graphs. The resulting class of reciprocal graph probability models strictly includes the class of chain graph probability models. The class of lattice conditional independence probability models is also strictly included. It is shown that the resulting methodology is directly applicable to quite general simultaneous equations systems that are subject to mild restrictions only. Provided some adjustments are made, general simultaneous equations systems can be handled as well. In all cases, consistency with the LISREL methodology is maintained.

Earthquake Preparedness: Predictors in a Community Survey1

We measured the relationship between earthquake concern and preparation in a community with high seismic risk. Five samples of approximately 800 people were interviewed by telephone about their earthquake concern, but this study focuses on the last two samples in which respondents reported their preparatory behaviors. The findings were consistent in showing greater likelihood of concern for those who had experienced an earthquake, were female, younger, and non‐Anglo and a greater likelihood of preparation for those who had more concern, were married, and had lived longer at their present address. In a nonrecursive causal model, no evidence was found for a reciprocal effect of preparation on concern. These results were discussed in terms of health belief models including the self‐efficacy and “stages of change” perspectives. Implications were drawn for interventions to increase preparation in ready‐to‐change groups and compensate for the resistance of those less willing or able to prepare.

Investigating reciprocal causation in organizational behavior research

AbstractThis paper examines issues that should be considered by researchers interested in testing nonrecursive causal models. It identifies choices that can be made in specifying those models which will minimize bias in estimation as well as facilitate the overall interpretation. A series of analyses demonstrate the importance of these issues. Implications for organizational behavior research and recommendations are discussed.

Specifying Nonrecursive Models in Organizational Research.

This paper examines issues that need to be considered by researchers interested in testing nonrecursive causal models. It identifies choices that can made to minimize bias in estimation as well as facilitate the interpretation of these models. This leads to a series of analyses illustrating the importance of these issues. Implications for organizational behavior research and recommendations are discussed.

Depression and Physical Illness: A Multiwave, Nonrecursive Causal Model

Causal influence has frequently been assumed, but rarely demonstrated, in studies documenting a positive association between physical illness and psychological distress. The present study assesses a latent variable causal model of subjectively experienced physical illness and depression in order to disentangle reciprocal patterns of influence. The data consist of interviews from a community sample of adults (N = 744) interviewed four times over one year. For the total sample, the exogenous variables of socioeconomic status, age, and sex are shown to affect both constructs of physical illness and depression. Illness has a large, contemporaneous effect of increasing depressive symptomatology over previous levels, and depression is found to have a smaller, 4-month lagged effect of increasing levels of physical illness. Multiple group analyses reveal some differences by age and sex, but demonstrate an overall consistency in the reciprocal relationship between depression and physical illness.

A Nonrecursive Causal Model of the Health Care Delivery System

The phrase “health care delivery system” is often used in both the popular and scientific literature. However, most research only examines one aspect of the system. We specify a causal system that links client characteristics and the structure, process, and outcomes of medical care. We also specify reciprocal effects among three outcomes of medical care: health status, client satisfaction, and visiting the docior. For each effect, we test a medical model hypothesis and two critiques of the medical model; a moderate critique and a radical critique.

Effective Teaching: A Communicative Nonrecursive Causal Model

(1981). Effective Teaching: A Communicative Nonrecursive Causal Model. Annals of the International Communication Association: Vol. 5, Communication Yearbook 5, pp. 737-749.

Estimating and Interpreting Correlations between Disturbances and Residual Path Coefficients in Nonrecursive (and Recursive) Causal Models

This paper explores the estimation and meaning, in both recursive and (especially) nonrecursive causal models, of two kinds of parameters that describe the disturbances: correlations between the disturbances of different structural equations and residual path coefficients. The emphasis is on nonrecursive models because it is there alone that correlations between disturbances conventionally appear and because the estimation and interpretation of residual path coefficients are familiar and unproblematic in conventional recursive models. The paper presents an easy, straightforward, and statistically consistent method of calculating estimates of both types of parameters -a method that is applicable to any identified model in which these parameters appear, whether nonrecursive or recursive. The paper also discusses, and sounds a note of caution about, the interpretation of both kinds of parameters. In the case of correlations between disturbances, the caveat is that a high value can occur even when the factors responsible for the correlation are not truly worthy of note and a low value can occur even when they are. For this reason, estimated correlations between disturbances should always be juxtaposed with estimates of the strength of the disturbances involved. For residual path coefficients, the caveat is that, in nonrecursive structures, their estimators do not represent, as they do in ordinary recursive structures, the proportion of variance in a dependent variable that the model leaves unexplained.

Causal Models in Educational Research: Nonrecursive Models

This paper extends the causal modelling technique described in an earlier article (Anderson and Evans, 1974) to nonrecursive causal models that involve feedback and/or reciprocal causation. The problem of identification is discussed and a rule provided that can be used to determine whether or not a unique set of parameter estimates can be found for each equation that makes up the model. Three different procedures are described for estimating the parameters of these equations, namely, ordinary least squares, indirect least squares, and two-stage least squares. A formula is provided for the derivation of the reduced form of the model. The reduced form provides information concerning the total effect of exogenous variables on endogenous variables in the model. Data from an empirical study have been used to illustrate the causal modelling technique that is described.

Political Protest and Political Violence: A Nonrecursive Causal Model

This paper presents a nonrecursive causal model of violence at protest demonstrations; it seeks to account for violence by both the police and the protestors. The model is tested using data on 126 protest events in the United States. The results of the analysis suggest strongly that the outbreak of violence at protest demonstrations is the result of a dynamic process resulting from the interaction of police and protestors; exogenous variables are found to have little predictive power when the reciprocal causal link between protestor violence and police violence is included in the model. Political protest and political violence have become more frequent in the United States in the last fifteen years; correspondingly, they have come under increased scrutiny by social scientists during this period. Various theories have been proposed to account for both political protest and political violence, and to draw the links between them (Eisinger; Fogelson; Gurr; Lipsky; Lupsha; Muller; Schneider; Schumaker; Skolnick; Von Eschen et al.; Wilson). In this research note, I will examine empirically one small segment of the related questions of political protest and political violence: What accounts for the outbreak of violence at incidents of political protest?' The causal model described and tested below suggests that the outbreak of violence at protest events is, in part, a dynamic process. The analysis assesses the importance of a number of explanatory variables. Unlike a number of theories that view the outbreak of violence primarily as a psychological process (e.g., Gurr; Sears and McConahay), this model suggests that specific outbreaks of violence in settings of protest are primarily the result of events specific to the settings themselves. (For a similar view in a slightly different context, see McPhail; and Moinat et al.). Before turning to the model to be tested, I must define what is meant, for the purposes of this analysis, by political protest and point out an important distinction in the study of political protest. Political protest refers to group activity utilizing confrontation politics to apply stress to specific targets for the purpose of affecting public policy (Terchek, 133). There are many other ways to define political protest but this is adequate for the analysis here. In looking at political protest, whether one is interested in explaining the impact of protest or what *The collection of data for this analysis was supported by NIMH grant No. 5 ROI MH 17421-02 SP, A. Paul Hare principal investigator. I would like to thank Dean MacCannell for making the data available. George Rabinowitz, Burdett Loomis, James Pierson, Michael Boss and several anonymous reviewers provided helpful comments on earlier drafts. Computer time was provided by Rice University, Indiana University, and the University of North Carolina; typing support was provided by the Departments of Political Science at Rice and Indiana Universities.

Political Protest and Political Violence: A Nonrecursive Causal Model

This paper presents a nonrecursive causal model of violence at protest demonstrations; it seeks to account for violence by both the police and the protestors. The model is tested using data on 126 protest events in the United States. The results of the analysis suggest strongly that the outbreak of violence at protest demonstrations is the result of a dynamic process resulting from the interaction of police and protestors; exogenous variables are found to have little predictive power when the reciprocal causal link between protestor violence and police violence is included in the model.

Marital instability by race, sex, education, and occupation using 1960 census data.

Previous articleNext article No AccessResearch NotesMarital Instability by Race, Sex, Education, and Occupation Using 1960 Census DataJ. Richard UdryJ. Richard Udry Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by American Journal of Sociology Volume 72, Number 2Sep., 1966 Article DOIhttps://doi.org/10.1086/224279 Views: 19Total views on this site Citations: 29Citations are reported from Crossref Copyright 1966 University of ChicagoPDF download Crossref reports the following articles citing this article:Yue Yu, Di Wu, Jiang-Meng Wang, Yu-Chen Wang Dark personality, marital quality, and marital instability of Chinese couples: An actor-partner interdependence mediation model, Personality and Individual Differences 154 (Feb 2020): 109689.https://doi.org/10.1016/j.paid.2019.109689Qiong He, Yuchen Wang, Yaxin Xing, Yue Yu Dark personality, interpersonal rejection, and marital stability of Chinese couples: An actor–partner interdependence mediation model, Personality and Individual Differences 134 (Nov 2018): 232–238.https://doi.org/10.1016/j.paid.2018.06.003Catherine McNamee, Kelly Raley A note on race, ethnicity and nativity differentials in remarriage in the United States, Demographic Research 24 (Feb 2011): 293–312.https://doi.org/10.4054/DemRes.2011.24.13Michael W. 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