Item Variances and Median Splits: Some Discouraging and Disquieting Findings

ABSTRACT In his response to our article, Baumeister argued that in real data, the confound between interitem variance and trait extremity is small (typically in the range of 0 and –.2), and that the danger of artifact associated with the application of median splits to interitem variance is not as serious as our first simulation study would lead one to believe. When we examined a large body of actual personality data, employing personality scales of average reliability and relatively large samples, we found that the average magnitude of the confound was –.15. However, we also found that even a confound as small as –.03 could be associated with significant differential range restriction of the trait scores within subsamples produced by the median split (MS) technique. We note that several factors, not just the magnitude of the interitem variance/trait extremity confound, must be considered when assessing the danger of artifact associated with the MS technique. We again conclude that researchers should use the moderated multiple regression (MMR) technique in preference to the MS technique when testing for moderating effects in personality research.     

Sample size determination for confidence intervals of interaction effects in moderated multiple regression with continuous predictor and moderator variables

Moderated multiple regression (MMR) has been widely employed to analyze the interaction or moderating effects in behavior and related disciplines of social science. Much of the methodological literature in the context of MMR concerns statistical power and sample size calculations of hypothesis tests for detecting moderator variables. Notably, interval estimation is a distinct and more informative alternative to significance testing for inference purposes. To facilitate the practice of reporting confidence intervals in MMR analyses, the present article presents two approaches to sample size determinations for precise interval estimation of interaction effects between continuous moderator and predictor variables. One approach provides the necessary sample size so that the designated interval for the least squares estimator of moderating effects attains the specified coverage probability. The other gives the sample size required to ensure, with a given tolerance probability, that a confidence interval of moderating effects with a desired confidence coefficient will be within a specified range. Numerical examples and simulation results are presented to illustrate the usefulness and advantages of the proposed methods that account for the embedded randomness and distributional characteristic of the moderator and predictor variables.     

Assessing spurious "moderator effects": Illustrated substantively with the hypothesized ("synergistic") relation between spatial and mathematical ability

The traditional methodology for assessing moderator variables (hierarchical multiple regression analysis) is examined. Possible drawbacks of this technique for corroborating psychological theories (cf. Busemeyer & Jones, 1983), are illustrated empirically on the basis of an analysis of 400,000 subjects. This article tested a well-known (and currently popular) substantive hypothesis: A synergistic relation exists between mathematical ability and spatial visualization in the prediction and development of sophisticated levels of advanced mathematics. Using the traditional methodology, this hypothesis was confirmed; however, on further analysis, using a more systematic approach, it was demonstrated that this finding was spurious. Suggestions are offered for modifying the traditional methodology used for assessing moderator effects (for both applied and theoretical purposes). These amount to ways for minimizing Type I and Type II errors.     

Type A behavior and stress in college males

Substantial inconsistency can be noted among studies that have considered the relationship between Type A status and subsequent cardiac pathology. This erratic prediction pattern could be partially explained by the failure to consider level of stress in Type As, because only Type As that experience excessive and sustained stress should be at special risk. A stress-vulnerability model for young Type A college students was proposed involving selected Type A characteristics and other personality moderator variables serving to elicit, augment, and sustain stress. We considered self-preoccupation as a stress moderator in Type A college males and found evidence that it serves this function. Self-preoccupied Type As reported excessive stress over the previous year, whereas less self-centered Type As did not. Self-preoccupation was of no importance in moderating stress in Type Bs.     

Type A Behavior and Stress in College Males

Substantial inconsistency can be noted among studies that have considered the relationship between Type A status and subsequent cardiac pathology. This erratic prediction pattern could be partially explained by the failure to consider level of stress in Type As, because only Type As that experience excessive and sustained stress should be at special risk. A stress-vulnerability model for young Type A college students was proposed involving selected Type A characteristics and other personality moderator variables serving to elicit, augment, and sustain stress. We considered self-preoccupation as a stress moderator in Type A college males and found evidence that k serves this function. Self-preoccupied Type As reported excessive stress over the previous year, whereas less self-centered Type As did not. Self-preoccupation was of no importance in moderating stress in Type Bs.     

A comparison of mean partialing and dual-hypothesis testing to evaluate stereotype effects when assessing profile similarity

Assessing profile similarity is an important task in research and clinical practice, but conclusions about profile similarity may be confounded by stereotype effects that create artificially similar profiles. In this article, we review the impact of stereotype effects on profile similarity and a conventional approach to addressing this confound (i.e., mean partialing). We argue that a dual-hypothesis testing approach distinguishing the no-effect null hypothesis (i.e., is the observed similarity different from zero?) from the chance-effect null hypothesis (i.e., is the observed similarity different from chance given the distribution of profile elements?) can provide a more nuanced understanding of profile similarity. To compare results from these 2 perspectives, we analyzed data from 2 samples using within-persons correlations as indexes of similar profile shapes. Results indicated that a dual-hypothesis testing approach led to more conservative conclusions about profile similarity (i.e., fewer Type 1 errors) than mean partialing and may be especially valuable when dealing with moderate-sized stereotype effects. Both approaches led to identical conclusions when stereotype effects were largest. Conclusions emphasize the relative merits and limitations of the dual-hypothesis testing approach as well as potential future applications in the personality assessment domain.     

害羞与进谏行为的关系:管理开放性与心理授权的调节作用

包括进谏行为在内的角色外行为研究近来广受关注。基于特质激活理论,本研究探索了害羞特质与进谏行为之间的关系,及管理开放性和心理授权对它们的调节作用。以华东地区369名企业员工为样本,采用配对成组的问卷研究,结果表明:管理开放性和心理授权均对害羞与进谏行为之间的关系存在调节效应,即当处于高管理开放性和高心理授权水平时,害羞特质与进谏行为之间的消极关系减弱,而当处于低管理开放性和低心理授权水平时,害羞特质与进谏行为之间的消极关系增强。     

Moderation Analysis Using a Two-Level Regression Model

Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.     

Detection of interactions between a dichotomous moderator and a continuous predictor in moderated multiple regression with heterogeneous error variance

Moderated multiple regression (MMR) has been widely used to investigate the interaction or moderating effects of a categorical moderator across a variety of subdisciplines in the behavioral and social sciences. In view of the frequent violation of the homogeneity of error variance assumption in MMR applications, the weighted least squares (WLS) approach has been proposed as one of the alternatives to the ordinary least squares method for the detection of the interaction effect between a dichotomous moderator and a continuous predictor. Although the existing result is informative in assuring the statistical accuracy and computational ease of the WLS-based method, no explicit algebraic formulation and underlying distributional details are available. This article aims to delineate the fundamental properties of the WLS test in connection with the well-known Welch procedure for regression slope homogeneity under error variance heterogeneity. With elaborately systematic derivation and analytic assessment, it is shown that the notion of WLS is implicitly embedded in the Welch approach. More importantly, extensive simulation study is conducted to demonstrate the conditions in which the Welch test will substantially outperform the WLS method; they may yield different conclusions. Welch’s solution to the Behrens-Fisher problem is so entrenched that the use of its direct extension within the linear regression framework can arguably be recommended. In order to facilitate the application of Welch’s procedure, the SAS and R computing algorithms are presented. The study contributes to the understanding of methodological variants for detecting the effect of a dichotomous moderator in the context of moderated multiple regression. Supplemental materials for this article may be downloaded from brm.psychonomic-journals.org/content/supplemental.     

Trait-Specific Versus Person-Specific Moderators of Cross-Situational Consistency

ABSTRACT Moderator variables for cross-situational consistency can be tested m two different ways (a) a trait-specific manner which examines differences in cross-situational consistency among traits, or (b) a person-specific manner which examines differences in cross-situational consistency among people The present study examined moderator effects–both trait- and person-specific–of the discrepancy between private self-ratings on trait dimensions (“How do you see yourself?”) and the corresponding public self-ratings (“How do others see you?”) Agreement between self- and peer ratings served as the dependent variable The results showed that public-private discrepancy moderated self-peer agreement when operationalized in a trait-specific manner, i e, for each trait, higher discrepancy was associated with lower self-peer agreement On the other hand, the results showed only minimal moderator effects when public-private discrepancy was operationalized in a person-specific manner, i e, when mean discrepancy across all traits served as the moderator Implications of the distinction between trait- and person-specific approaches to moderator effects are discussed     

Detecting Curvilinear Relationships: A Comparison of Scoring Approaches Based on Different Item Response Models

There is a growing use of noncognitive assessments around the world, and recent research has posited an ideal point response process underlying such measures. A critical issue is whether the typical use of dominance approaches (e.g., average scores, factor analysis, and the Samejima's graded response model) in scoring such measures is adequate. This study examined the performance of an ideal point scoring approach (e.g., the generalized graded unfolding model) as compared to the typical dominance scoring approaches in detecting curvilinear relationships between scored trait and external variable. Simulation results showed that when data followed the ideal point model, the ideal point approach generally exhibited more power and provided more accurate estimates of curvilinear effects than the dominance approaches. No substantial difference was found between ideal point and dominance scoring approaches in terms of Type I error rate and bias across different sample sizes and scale lengths, although skewness in the distribution of trait and external variable can potentially reduce statistical power. For dominance data, the ideal point scoring approach exhibited convergence problems in most conditions and failed to perform as well as the dominance scoring approaches. Practical implications for scoring responses to Likert-type surveys to examine curvilinear effects are discussed.     

On the relationship of hardiness to the Type A behavior pattern: Perception of life events versus coping with life events

A correlational study examined the relationship between perceptions of life events and psychological distress with regard to the moderating effects of personality. Subjects assessed for both hardiness (S. C. Kobasa, 1979, Journal of Personality and Social Psychology, 37, 1–11) and Type A behavior (M. Friedman & R. Rosenman, 1974, Type A Behavior and Your Heart, New York: Knopf) completed the Langner psychiatric symptom inventory, reported life events for the previous year, and rated each event for its desirability, controllability, and foreseeability. Results indicated that an accumulation of events perceived as undesirable was associated with distress for subjects low in hardiness. In contrast, events perceived as moderately controllable or uncontrollable, regardless of their desirability, were associated with increased distress for Type A's. The likelihood of experiencing any given event was not related to any personality type. However, hardy individuals differed from their low hardy counterparts in that, on average, they were more likely to perceive an event as desirable and controllable. No such perceptual biases were observed between Type A and B subjects. The discussion focuses on the combination of personality variables and the moderating effects of perceiving events versus coping with events in the stress-health relationship.     

Further evidence of associations of type a personality scores and driving-related attitudes and behaviors

The present study examined the effects of Type A personality on specific self-reported driving attitudes and behaviors when operating a motor vehicle. 102 undergraduate students completed the student version of the Jenkins Activity Survey (Form T) and several questionnaires asking participants about their driving history, driving attitudes, and driving behaviors. When the full range of Type A scores were examined, Type A personality was significantly related to more traffic accidents, greater frequency of breaking traffic laws, higher impatience when driving, more displays of aggression on the road, and engaging in more risky driving behaviors (rs<.17). When extreme Type A and Type B scores were compared, Type A drivers reported being involved in significantly more motor vehicle accidents and reported displaying more aggression on the road. Further research should examine actual behavioral data using more diverse samples to validate the results.     

Reduced Sense of Coherence Due to Neuroticism: Are Transcendent Beliefs Protective Among Catholic Pastoral Workers?

In this study, we examined a third variable effect on the relationship of personality traits, especially neuroticism and the salutogenetic concept sense of coherence. Specifically, we were interested in the moderating role of religious trust (RT) and transcendence perception operationalized as daily spiritual experiences (DSE) on the aforementioned relationship among religious individuals. We applied a cross-sectional study among a sample of 8594 pastoral workers using standardized questionnaires. Multiple regression and moderator analysis displayed the relationships between big five personality variables and sense of coherence. Neuroticism was identified as a negative predictor to sense of coherence, indicating impairment on this psychological resource. RT and DSE appear to function as moderators that buffer the negative effects of neuroticism on sense of coherence among religious persons. This is an interesting finding because people with expressions of neurotic personality tendencies often struggle to find helpful methods of coping and may find a helpful resource in the concepts studied here.     

基于两水平回归模型的调节效应分析及其效应量

使用多元回归法进行调节效应分析在社科领域已常有应用。简述了目前多元回归法的调节效应分析存在的不足,包括人为变换检验模型、自变量和调节变量区分不足、误差方差齐性的假设难以满足、调节效应量指标ΔR²没有直接测量调节变量对自变量与因变量关系的调节程度。比较好的方法是用两水平回归模型进行调节效应分析并使用相应的效应量指标。在介绍新方法和新效应量后,总结出一套调节效应的分析流程,通过一个例子来演示如何用Mplus软件进行两水平回归模型的调节效应及其效应量分析。最后讨论了两水平回归模型的调节效应分析的发展,包括稳健的调节效应分析、潜变量的调节效应分析、有调节的中介效应分析和有中介的调节效应分析等。     

Modeling Interactions Between Latent Variables in Research on Type D Personality: A Monte Carlo Simulation and Clinical Study of Depression and Anxiety

Several approaches exist to model interactions between latent variables. However, it is unclear how these perform when item scores are skewed and ordinal. Research on Type D personality serves as a good case study for that matter. In Study 1, we fitted a multivariate interaction model to predict depression and anxiety with Type D personality, operationalized as an interaction between its two subcomponents negative affectivity (NA) and social inhibition (SI). We constructed this interaction according to four approaches: (1) sum score product; (2) single product indicator; (3) matched product indicators; and (4) latent moderated structural equations (LMS). In Study 2, we compared these interaction models in a simulation study by assessing for each method the bias and precision of the estimated interaction effect under varying conditions. In Study 1, all methods showed a significant Type D effect on both depression and anxiety, although this effect diminished after including the NA and SI quadratic effects. Study 2 showed that the LMS approach performed best with respect to minimizing bias and maximizing power, even when item scores were ordinal and skewed. However, when latent traits were skewed LMS resulted in more false-positive conclusions, while the Matched PI approach adequately controlled the false-positive rate.     

The Impact of Job Insecurity on Counterproductive Work Behaviors: The Moderating Role of Honesty–Humility Personality Trait

The detrimental effects of job insecurity on individual and organizational well-being are well documented in recent literature. Job insecurity as a stressor is generally associated with a higher presence of negative attitudes toward the organization. In this article, the moderating role of Honesty–Humility personality trait was investigated. It was assumed that Honesty–Humility would function as a psychological moderator of the job insecurity impact on counterproductive work behaviors. Participants were 203 workers who were administered a self–reported questionnaire. Results confirmed that job insecurity was positively related to counterproductive work behaviors whereas Honesty–Humility was negatively associated to them. More importantly, Honesty–Humility moderated this relationship, even after controlling for gender, age, type of contract, and the other HEXACO personality traits. For individuals with low Honesty–Humility, job insecurity was positively related to counterproductive work behaviors, whereas for individuals with high Honesty–Humility, job insecurity turned out to be unrelated to counterproductive work behaviors.     

The impact of ignoring random features of predictor and moderator variables on sample size for precise interval estimation of interaction effects

The influence of the joint distribution of predictor and moderator variables on the identification of interactions has been well described, but the impact on sample size determinations has received rather limited attention within the framework of moderated multiple regression (MMR). This article investigates the deficiency in sample size determinations for precise interval estimation of interaction effects that can result from ignoring the stochastic nature of continuous predictor and moderator variables in MMR. The primary finding of our examinations is that failure to accommodate the distributional properties of regressors can lead to underestimation of the necessary sample size and distortion of the desired interval precision. In order to take account of the randomness of regressor variables, two general and effective procedures for computing sample size estimates are presented. Moreover, corresponding programs are provided to facilitate use of the suggested approaches. This exposition helps to correct drawbacks in the existing techniques and to advance the practice of reporting confidence intervals in MMR analyses.