Trajectories of Perceived Workplace Age Discrimination and Long-Term Associations With Mental, Self-Rated, and Occupational Health.

OBJECTIVE: This study addresses older employees' trajectories of perceived workplace age discrimination, and the long-term associations among perceived age discrimination and older workers' mental and self-rated health, job satisfaction, and likelihood of working past retirement age. We evaluate the strength and vulnerability integration (SAVI) model. METHOD: Three waves of data from employed participants were drawn from the Health and Retirement Study (N = 3,957). Latent growth modeling was used to assess relationships between the slopes and the intercepts of the variables, thereby assessing longitudinal and cross-sectional associations. RESULTS: Perceived workplace age discrimination tends to increase with age, although notable variance exists. The initial status of perceived age discrimination relates to the baseline statuses of depression, self-rated health, job satisfaction, and likelihood of working past retirement age in the expected directions. Over time, perceived age discrimination predicts lower job satisfaction and self-rated health, as well as elevated depressive symptoms, but not likelihood of working past retirement age. DISCUSSION: This study provides empirical support for the SAVI model and uncovers the "wear and tear" effects of perceived workplace age discrimination on older workers' mental and overall health. We deliberate on social policies that may reduce age discrimination, thereby promoting older employees' health and ability to work longer.

Ideal, nonideal, and no-marker variables: The confirmatory factor analysis (CFA) marker technique works when it matters.

A persistent concern in the management and applied psychology literature is the effect of common method variance on observed relations among variables. Recent work (i.e., Richardson, Simmering, & Sturman, 2009) evaluated 3 analytical approaches to controlling for common method variance, including the confirmatory factor analysis (CFA) marker technique. Their findings indicated significant problems with this technique, especially with nonideal marker variables (those with theoretical relations with substantive variables). Based on their simulation results, Richardson et al. concluded that not correcting for method variance provides more accurate estimates than using the CFA marker technique. We reexamined the effects of using marker variables in a simulation study and found the degree of error in estimates of a substantive factor correlation was relatively small in most cases, and much smaller than error associated with making no correction. Further, in instances in which the error was large, the correlations between the marker and substantive scales were higher than that found in organizational research with marker variables. We conclude that in most practical settings, the CFA marker technique yields parameter estimates close to their true values, and the criticisms made by Richardson et al. are overstated.

Decomposing model fit: measurement vs. theory in organizational research using latent variables.

Goodness-of-fit indices have an important role in structural equation model evaluation. However, some studies (e.g., McDonald & Ho, 2002; Mulaik et al., 1989) have raised concerns that overall fit values primarily reflect the fit of the measurement model, and this allows significant misspecification among the latent variables to be masked. Using an approach analogous to Anderson and Gerbing's (1988) 2-step approach that isolates the measurement component of a composite model, we present the rationale and evidence for the root mean square error of approximation of the path component (RMSEA-P), a relatively new fit index that isolates the path component. We reviewed 5 of the top organizational behavior/human resources journals from 2001 to 2008 and identified 43 studies using structural equation modeling in which the overall composite model could be decomposed into its measurement and path components. The RMSEA-P for these studies generally showed unfavorable results, with many values failing to meet commonly accepted standards. Incorporating the RMSEA-P and its confidence interval into James, Mulaik, and Brett's (1982) framework for model testing, we provide evidence that many of the conclusions based upon the goodness of fit of the overall model may be inaccurate. We conclude with recommendations for how researchers can focus more attention on path models and latent variable relations and improve their model evaluation process.