Common Problems/Proper Solutions: Avoiding Error in Quantitative ResearchJ. Scott Long Statistical and methodological errors are fairly universal in all the social sciences. This unique volume investigates the following questions: what are the most common errors, and how can they be avoided? Common Problems/Proper Solutions identifies and corrects these errors and provides clear statements concerning methodological issues. Long groups the problems into two broad types: omission where researchers fail to apply methods ideal to a topic; and commission where a technique is inappropriately applied. Each article addresses a specific aspect of these problems. This volume encourages further communication between methodological specialists and quantitative researchers, and highlights the important relationship be |
Contents
Acknowledgments | 8 |
Measurement and the Interpretation | 15 |
Direct and Indirect Effects | 46 |
Copyright | |
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ancestry type assignment assume assumption Bentler and Bonett calculated categorical variables causal cell Clogg coefficient of determination coefficients cohort college track considered constraints covariance matrix crosstable defined degrees of freedom denote dependent differ by group discussion distribution disturbances endogenous variables enter position equal estimating equation evaluate example expected F-test factor frequencies hypothesis independent variables indicators indirect effects interaction effects interpretation Jöreskog latent variable latent-class models length of sentence linear LISREL log-linear analysis log-linear models logit equation logit model measures methods mother tongue NILF noncollege track Note null model observed obtained odds of exposure odds ratios omitted category outcome overfit parameter estimates population problem procedure programs regression relative restricted model rotation group satisfaction social specification standard errors standardized coefficients statistical structural equation models structural modeling substantive Table theory tion unobserved variance weighted X₁ Y₁