The article "Job Satisfaction and Job Performance: A Meta-Analysis" by Michelle T. Iaffaldano and Paul M. Muchinsky examines the relationship between job satisfaction and job performance through a meta-analytic approach. The study aims to integrate existing research and provide a more precise estimate of the true population correlation between these two variables. Key findings include: 1. **Low Correlation**: The best estimate of the true population correlation between job satisfaction and job performance is relatively low at .17. 2. **Variability in Results**: Much of the variability in previous research results is attributed to small sample sizes rather than unreliable measurement of job satisfaction and performance. 3. **Research Design Characteristics**: Nine research design characteristics are only modestly related to the magnitude of the satisfaction-performance correlation. 4. **Methodological Considerations**: The study discusses the impact of various methodological factors, such as the nature of the sample, measurement techniques, and the use of self-reports versus other sources of data. 5. **Statistical Artifacts**: The study corrects for statistical artifacts such as sampling error and measurement unreliability, reducing the variance in the distribution of observed correlations. 6. **Time Trends**: There were no significant differences in the magnitude of satisfaction-performance correlations over four time periods, suggesting that recent advancements in measurement and design have not significantly improved the relationship. 7. **Moderating Factors**: The nine study characteristics only account for a small portion of the variance in satisfaction-performance correlations, indicating that other factors may play a more significant role in the observed relationships. The article concludes by discussing the implications of these findings for both substantive and research practices, emphasizing the need for further exploration of other potential moderators and the importance of larger sample sizes and more rigorous measurement techniques.The article "Job Satisfaction and Job Performance: A Meta-Analysis" by Michelle T. Iaffaldano and Paul M. Muchinsky examines the relationship between job satisfaction and job performance through a meta-analytic approach. The study aims to integrate existing research and provide a more precise estimate of the true population correlation between these two variables. Key findings include: 1. **Low Correlation**: The best estimate of the true population correlation between job satisfaction and job performance is relatively low at .17. 2. **Variability in Results**: Much of the variability in previous research results is attributed to small sample sizes rather than unreliable measurement of job satisfaction and performance. 3. **Research Design Characteristics**: Nine research design characteristics are only modestly related to the magnitude of the satisfaction-performance correlation. 4. **Methodological Considerations**: The study discusses the impact of various methodological factors, such as the nature of the sample, measurement techniques, and the use of self-reports versus other sources of data. 5. **Statistical Artifacts**: The study corrects for statistical artifacts such as sampling error and measurement unreliability, reducing the variance in the distribution of observed correlations. 6. **Time Trends**: There were no significant differences in the magnitude of satisfaction-performance correlations over four time periods, suggesting that recent advancements in measurement and design have not significantly improved the relationship. 7. **Moderating Factors**: The nine study characteristics only account for a small portion of the variance in satisfaction-performance correlations, indicating that other factors may play a more significant role in the observed relationships. The article concludes by discussing the implications of these findings for both substantive and research practices, emphasizing the need for further exploration of other potential moderators and the importance of larger sample sizes and more rigorous measurement techniques.