By Toon W Taris
This obtainable advent to the speculation and perform of longitudinal examine takes the reader during the strengths and weaknesses of this type of learn, making transparent: the right way to layout a longitudinal examine; find out how to gather information such a lot successfully; find out how to make the easiest use of statistical strategies; and the way to interpret effects. even supposing the publication presents a wide assessment of the sector, the point of interest is often at the useful concerns coming up out of longitudinal learn. This ebook offers the coed with all that they should start and acts as a guide for facing possibilities and pitfalls. it's the excellent primer for this becoming region of social learn.
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Extra resources for A Primer in Longitudinal Data Analysis
In the case of income, information about the participants' job level may be useful in determining whether missing values on income are MAR. Job level is usually positively correlated with income, and explains about a quarter of the variance in income. If participants with a high-level job are more likely to have a missing value on income, it seems likely that these missings are not MAR. If the missing values are neither MCAR nor MAR, 32 A PRIMER IN LONGITUDINAL DATA ANALYSIS the nonresponse mechanism cannot be neglected, and measures must be taken in the data analysis phase (see below).
Again, this type of approach cannot yield conclusive evidence as to whether the sample is representative. Inspection of nonresponse patterns Nonresponse rates need not be the same for all waves of a longitudinal study. Indeed, nonresponse rates frequently decrease with every successive wave. Consider the nonresponse rates in the three-wave study among Dutch youth reported by Taris (1996, 1997). The nonresponse rates for the three waves were 37, 20, and 11 per cent for each successive wave. One reason might have been that the participants took more pleasure in completing the questionnaires with every successive wave, or that their commitment to the study increased across time ± nothing to worry about.
Such knowledge may also be of use when adjusting for the sample bias that may result when nonresponse is selective. Unfortunately, all methods designed to correct for selective nonresponse ultimately rest on untestable assumptions. Thus, the best way of handling missing data and drop-out is to make sure that there is none. While acknowledging that this cannot be achieved, researchers should take every conceivable measure to optimize their response rates. Only then can they be reasonably certain that the possible bias due to selective nonresponse is as small as possible.
A Primer in Longitudinal Data Analysis by Toon W Taris