By Toon W Taris
This available advent to the idea and perform of longitudinal study takes the reader in the course of the strengths and weaknesses of this sort of study, making transparent: how you can layout a longitudinal learn; the best way to gather info such a lot successfully; how one can make the easiest use of statistical recommendations; and the way to interpret effects. even though the e-book offers a vast assessment of the sphere, the point of interest is often at the sensible concerns bobbing up out of longitudinal learn. This e-book provides the coed with all that they should start and acts as a handbook for facing possibilities and pitfalls. it's the perfect primer for this transforming into zone of social study.
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Additional info for A Primer in Longitudinal Data Analysis
1995) describe the strategies used for the National Comorbidity Study (NCS). The NCS was a large-scale national survey carried out in 1990±92 to examine the prevalence, causes, and consequences of psychiatric morbidity and comorbidity in the United States. Measures taken to increase contact rates included use of a very long ®eld period and an extended callback schedule in an effort to minimize the number of potential respondents who could not be contacted. Further, at the last wave of the study, hard-to-reach households were undersampled by half, and twice as much ®eld effort was devoted in each case to making contacts with the remaining half-sample during the last month of the ®eld period.
Many largescale surveys use advance letters in which potential respondents are noti®ed that they will be contacted to participate in the study. Such letters usually contain information about the organization conducting the study, the rationale and purpose of the study, along with information about how the respondent was selected. Eaker et al. (1998) estimated that in their study preliminary noti®cation led to a 30 per cent higher retrieval rate. Further, reminders are often used to improve response rates.
Note that more than one predictor variable may be included. This method is especially acceptable if most of the variance of X2 is accounted for by X1 (as often occurs in longitudinal research; many variables are rather stable across time; compare Chapters 3 and 4). Both types of imputation are good for estimating means, but not for estimating variances and covariances. Imputing the mean of a variable for all missing cases on this variable (what Little and Rubin, 1990, call `naive imputation' ± a term aptly re¯ecting how they feel about this procedure) leads to a situation in which more cases obtain the mean score than would normally be the case: the mean value will be overrepresented in the postimputation sample, as it is unlikely that all missing values were actually equal to the sample mean.