In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptually and strategically, the many ways in which moderators and mediators differ. We then go beyond this largely pedagogical function and delineate the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena, including control and stress, attitudes, and personality traits. We also provide a specific compendium of analytic procedures appropriate for making the most effective use of the moderator and mediator distinction, both separately and in terms of a broader causal system that includes both moderators and mediators.

/pdf/the-moderator-mediator-variable-distinction-in-social-2bmijritn2.pdf

The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations.

This article examines the adequacy of the “rules of thumb” conventional cutoff criteria and several new alternatives for various fit indexes used to evaluate model fit in practice. Using a 2‐index presentation strategy, which includes using the maximum likelihood (ML)‐based standardized root mean squared residual (SRMR) and supplementing it with either Tucker‐Lewis Index (TLI), Bollen's (1989) Fit Index (BL89), Relative Noncentrality Index (RNI), Comparative Fit Index (CFI), Gamma Hat, McDonald's Centrality Index (Mc), or root mean squared error of approximation (RMSEA), various combinations of cutoff values from selected ranges of cutoff criteria for the ML‐based SRMR and a given supplemental fit index were used to calculate rejection rates for various types of true‐population and misspecified models; that is, models with misspecified factor covariance(s) and models with misspecified factor loading(s). The results suggest that, for the ML method, a cutoff value close to .95 for TLI, BL89, CFI, RNI, and G...

Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives

Perceived Usefulness, Perceived Ease of Use, and User

Information technology (IT) acceptance research has yielded many competing models, each with different sets of acceptance determinants. In this paper, we (1) review user acceptance literature and discuss eight prominent models, (2) empirically compare the eight models and their extensions, (3) formulate a unified model that integrates elements across the eight models, and (4) empirically validate the unified model. The eight models reviewed are the theory of reasoned action, the technology acceptance model, the motivational model, the theory of planned behavior, a model combining the technology acceptance model and the theory of planned behavior, the model of PC utilization, the innovation diffusion theory, and the social cognitive theory. Using data from four organizations over a six-month period with three points of measurement, the eight models explained between 17 percent and 53 percent of the variance in user intentions to use information technology. Next, a unified model, called the Unified Theory of Acceptance and Use of Technology (UTAUT), was formulated, with four core determinants of intention and usage, and up to four moderators of key relationships. UTAUT was then tested using the original data and found to outperform the eight individual models (adjusted R2 of 69 percent). UTAUT was then confirmed with data from two new organizations with similar results (adjusted R2 of 70 percent). UTAUT thus provides a useful tool for managers needing to assess the likelihood of success for new technology introductions and helps them understand the drivers of acceptance in order to proactively design interventions (including training, marketing, etc.) targeted at populations of users that may be less inclined to adopt and use new systems. The paper also makes several recommendations for future research including developing a deeper understanding of the dynamic influences studied here, refining measurement of the core constructs used in UTAUT, and understanding the organizational outcomes associated with new technology use.

User acceptance of information technology: toward a unified view

Hypotheses involving mediation are common in the behavioral sciences. Mediation exists when a predictor affects a dependent variable indirectly through at least one intervening variable, or mediator. Methods to assess mediation involving multiple simultaneous mediators have received little attention in the methodological literature despite a clear need. We provide an overview of simple and multiple mediation and explore three approaches that can be used to investigate indirect processes, as well as methods for contrasting two or more mediators within a single model. We present an illustrative example, assessing and contrasting potential mediators of the relationship between the helpfulness of socialization agents and job satisfaction. We also provide SAS and SPSS macros, as well as Mplus and LISREL syntax, to facilitate the use of these methods in applications.

https://link.springer.com/content/pdf/10.3758/BRM.40.3.879.pdf

Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models

Despite the effectiveness of mammography, a large percentage of eligible women do not obtain screening mammograms. Two programs designed to increase the use of mammography were designed, implemented and evaluated. One hundred and fifty-six women were recruited and assigned to an Educational plus Psychological program (EP), an Educational program (E), or to a delayed treatment control group (C). Women completed a pretest, a posttest (EP and E only) and a telephone follow-up. At posttest, EP women had greater intention to obtain a mammogram than C women. EP and E women had higher levels of knowledge and higher levels of perceived benefit of mammography than C women. Actual compliance did not differ among the three conditions. Implications for the design of future programs are discussed.

Increasing the Use of Mammography: A Pilot Program

Announcement

The Jeffrey S. Tanaka Occasional Papers in Quantitative Methods for Personality



With the publication of this issue's lead article, “Weekly Cycles in Daily Report Data: An Overlooked Issue,” the Journal of Personality continues an ongoing series devoted to the memory of Jeffrey S. Tanaka. The goal of this series, exemplified by the Liu and West article, is to continue the spirit of Jeff Tanaka's work by illustrating how innovative methodological and statistical approaches can improve our understanding of personality phenomena. Proposals for the Occasional Papers series are welcomed and should follow the guidelines in the solicitation that appeared in the December, 1993 (Volume 61, Number 4) issue of the Journal.



Howard Tennen, Editor





Daily diaries and other everyday experience methods are increasingly used to study relationships between two time-varying variables X and Y. Although daily data potentially often have weekly cyclical patterns (e.g., stress may be higher on weekdays and lower on weekends), the majority of daily diary studies have ignored this possibility. In this study, we investigated the effect of ignoring existing weekly cycles. We reanalyzed an empirical dataset (stress and alcohol consumption) and performed Monte Carlo simulations to investigate the impact of omitting weekly cycles. In the empirical dataset, ignoring cycles led to the inference of a significant within-person X–Y relation whereas modeling cycles suggested that this relationship did not exist. Simulation results indicated that ignoring cycles that existed in both X and Y led to bias in the estimated within-person X–Y relationship. The amount and direction of bias depended on the magnitude of the cycles, magnitude of the true within-person X–Y relation, and synchronization of the cycles. We encourage researchers conducting daily diary studies to address potential weekly cycles in their data. We provide guidelines for detecting and modeling cycles to remove their influence and discuss challenges of causal inference in daily experience studies.

Weekly Cycles in Daily Report Data: An Overlooked Issue

Multiple regression represents the relationship between a dependent variable and a weighted linear combination of a set of independent variables. Multiple regression can be used to model a wide variety of forms of relationships and types of independent variables. Diagnostic procedures can be used to identify violations of assumptions and outlying data points which adversely affect its performance. Multiple regression can be used to describe relationships, predict future scores on the dependent variable, and test specific hypotheses.


Keywords:

multiple regression;
outliers;
diagnostics

Multiple Linear Regression

Hyperventilation: its relation to symptom experience and to anxiety.

Learning to remember: cognitive training-induced attenuation of age-related memory decline depends on sex and cognitive demand, and can transfer to untrained cognitive domains

Stephen G. West

Papers

Increasing the Use of Mammography: A Pilot Program

Weekly Cycles in Daily Report Data: An Overlooked Issue

Multiple Linear Regression

Hyperventilation: its relation to symptom experience and to anxiety.

Learning to remember: cognitive training-induced attenuation of age-related memory decline depends on sex and cognitive demand, and can transfer to untrained cognitive domains