Influence of social and work exchange relationships on organizational citizenship behavior, The

Previous studies explain situational antecedents of OCB using social exchange theory. However, the effects of factors such as perceptions of job characteristics on OCB seem to require a different explanatory mechanism. We propose that these effects can be explained through a new exchange relationship that we call work exchange. We develop a theory for the situational antecedents of OCB that includes economic, work, and social exchange relationships. The theory is tested using structural equations.exchange relationship; organizational citizenship behavior; organizational commitment; perceived organizational support; job characteristics;

A scaled difference chi-square test statistic for moment structure analysis

A family of scaling corrections aimed to improve the chi-square approximation of goodness-of-fit test statistics in small samples, large models, and nonnormal data was proposed in Satorra and Bentler (1994). For structural equations models, Satorra-Bentler's (SB) scaling corrections are available in standard computer software. Often, however, the interest is not on the overall fit of a model, but on a test of the restrictions that a null model say ${\cal M}_0$ implies on a less restricted one ${\cal M}_1$. If $T_0$ and $T_1$ denote the goodness-of-fit test statistics associated to ${\cal M}_0$ and ${\cal M}_1$, respectively, then typically the difference $T_d = T_0 - T_1$ is used as a chi-square test statistic with degrees of freedom equal to the difference on the number of independent parameters estimated under the models ${\cal M}_0$ and ${\cal M}_1$. As in the case of the goodness-of-fit test, it is of interest to scale the statistic $T_d$ in order to improve its chi-square approximation in realistic, i.e., nonasymptotic and nonnormal, applications. In a recent paper, Satorra (1999) shows that the difference between two Satorra- Bentler scaled test statistics for overall model fit does not yield the correct SB scaled difference test statistic. Satorra developed an expression that permits scaling the difference test statistic, but his formula has some practical limitations, since it requires heavy computations that are not available in standard computer software. The purpose of the present paper is to provide an easy way to compute the scaled difference chi-square statistic from the scaled goodness-of-fit test statistics of models ${\cal M}_0$ and ${\cal M}_1$. A Monte Carlo study is provided to illustrate the performance of the competing statistics.Moment-structures, goodness-of-fit test, chi-square difference test statisitc, chi-square distribution, non-normality

Cognitive Motivations and Sensation Seeking as Long-Term Predictors of Drinking Problems

The development of comprehensive theories regarding the determinants ofvulnerability toward drinking problems depends in part on longitudinal evidencelinking psychosocial precursors to clinically-relevant problem consequences. Inan investigation of some of the more promising psychosocial precursors ofproblem vulnerability, we evaluated the long-term predictive effects of adolescentcognitive motivations for alcohol use and sensation seeking on a wide variety ofadult drinking-problem consequences including driving while intoxicated (DWI).Results indicated that the Cognitive Motivation factor was a significant, independent, nine-year predictor of a factor of Drinking-Problem Consequences. Over this same period, certain cognitive motivation and sensation seeking indicators independently predicted DWI, and the Sensation Seeking factor independently predicted Cognitive Motivation and Alcohol Use factors. The significant, independent effects on problem-drinking variables demonstrated that psychosocial vulnerability appeared across a range of consumption levels. These findings have important implications for counseling practices and the identification of teenagers of high-risk for drinking problems and DWI in later adulthood

Interactive and Higher-Order Effects of Social Influences on Drug Use

The study of moderators and higher-order effects of social influences on drug use has many implications for theories of health behavior. In the present study, we investigated the longitudinal predictive effects of some of the prominent moderator variables that represent forms of susceptibility toward social influence in teenage drug use. We also studied the possibility that social influence may predict drug use in nonlinear (quadratic) forms, consistent with theories proposing that threshold or decelerating effects may occur in social influences on normatively sanctioned behaviors. Results showed that several of the interactive and quadratic predictive effects were significant. The findings supported the views that certain moderator variables act as buffers, which either protect the individual from social pressures to use drugs, or make the individual more susceptible to such pressures. In addition, two of the obtained quadratic effects of social influence lent support to the application of social impact theory to drug use. Overall, our findings suggest that interactive and nonlinear approaches to social influences on drug use provide a unique and viable theoretical perspective from which to construe this problem health behavior

Structural Equations Modeling

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/144267/1/jcpy83.pd

Structural equation and log-linear modeling: a comparison of methods in the analysis of a study on caregivers' health

BACKGROUND: In this paper we compare the results in an analysis of determinants of caregivers' health derived from two approaches, a structural equation model and a log-linear model, using the same data set. METHODS: The data were collected from a cross-sectional population-based sample of 468 families in Ontario, Canada who had a child with cerebral palsy (CP). The self-completed questionnaires and the home-based interviews used in this study included scales reflecting socio-economic status, child and caregiver characteristics, and the physical and psychological well-being of the caregivers. Both analytic models were used to evaluate the relationships between child behaviour, caregiving demands, coping factors, and the well-being of primary caregivers of children with CP. RESULTS: The results were compared, together with an assessment of the positive and negative aspects of each approach, including their practical and conceptual implications. CONCLUSION: No important differences were found in the substantive conclusions of the two analyses. The broad confirmation of the Structural Equation Modeling (SEM) results by the Log-linear Modeling (LLM) provided some reassurance that the SEM had been adequately specified, and that it broadly fitted the data

Redesign and initial validation of an instrument to assess the motivational qualities of music in exercise: The Brunel Music Rating Inventory-2

In the present study, a measure to assess the motivational qualities of music in exercise was redesigned, extending previous research efforts (Karageorghis et al., 1999). The original measure, the Brunel Music Rating Inventory (BMRI), had shown limitations in its factor structure and its applicability to non-experts in music selection. Redesign of the BMRI used in-depth interviews with eight participants (mean age 31.9 years, s¼8.9 years) to establish the initial item pool, which was examined using a series of confirmatory factor analyses. A single-factor model provided a good fit across three musical selections with different motivational qualities (comparative fit index, CFI: 0.95 – 0.98; standardized root mean residual, SRMR: 0.03 – 0.05). The single-factor model also demonstrated acceptable fit across two independent samples and both sexes using one piece of music (CFI: 0.86 – 1.00; SRMR: 0.04 – 0.07). The BMRI was designed for experts in selecting music for exercise (e.g. dance aerobic instructors), whereas the BMRI-2 can be used both by exercise instructors and participants. The psychometric properties of the BMRI-2 are stronger than those of the BMRI and it is easier to use. The BMRI-2 provides a valid and internally consistent tool by which music can be selected to accompany a bout of exercise or a training session. Furthermore, the BMRI-2 enables researchers to standardize music in experimental protocols involving exercise-related tasks

Hierarchical regression analysis in structural Equation Modeling

In a hierarchical or fixed-order regression analysis, the independent variables are entered into the regression equation in a prespecified order. Such an analysis is often performed when the extra amount of variance accounted for in a dependent variable by a specific independent variable is the main focus of interest (e.g., Cohen & Cohen, 1983). For example, in the area of reading achievement, there is a general interest in the specific abilities that predict reading development. Because these specific abilities are often correlated with more general abilities, such as verbal intelligence, the latter abilities are controlled for first (e.g., Wagner, Torgesen, & Rashotte, 1994). An additional reason for performing a hierarchical regression analysis is that, in these research applications, as well as in many others, the independent variables are often highly correlated. When correlated independent variables are included simultaneously in the regression model, multicollinearity arises (Cohen & Cohen, 1983). Though regularly used with observed variables, hierarchical regression analysis has not been performed with latent variables. In most applications of structural equation modeling (SEM), the latent predictors have been entered simultaneously into the regression model, although in several cases hierarchical regression analysis would have been the more appropriate approach (e.g., Guthrie et al., 1998; Normandeau & Guay, 1998; Wagner et al., 1994; Wagner et al., 1997). In this article we describe how a hierarchical regression analysis may be conducted in SEM. The main procedure proposed is to perform a Cholesky or triangular decomposition of the intercorrelations among the latent predictors (Harman, 1976; Loehlin, 1996). First the procedure is described and then an example of a hierarchical regression analysis with latent variables is given. Copyright © 1999, Lawrence Erlbaum Associates, Inc