The significance of a skip pattern depends on the question leading to the skip pattern, the questions within that skip pattern, and the variables you intend to analyze.
If you fail to address skip patterns, your sample may end up including only a proportion of the population, instead of the entire study population.
The Questionnaire files contain some items that have skip patterns, and these items may be relevant to your analysis. For example, in question OSQ.060, participants are asked, “Has a doctor ever told you that you had osteoporosis, sometimes called thin or brittle bones?” Those who answered “Yes,” were asked the follow-up question OSQ.070: “Were you treated for osteoporosis?” Those who answered “No” were not asked the follow-up question.
If, for example, you would like to estimate the prevalence of treated osteoporosis, you must recode OSQ.070 to include those who answered “No” in OSQ.060. Thus, until you recode OSQ.070 (or define a new variable based on it) to include those who answered “No” to OSQ.060, these people will be left out of the denominator value. If you fail to do this step, you will obtain the proportion of treated osteoporosis only among a subpopulation of people who have ever been told by a doctor that they had osteoporosis, instead of the entire study population.
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