Cross sectional study design involves observation of variable\/s at a particular point in time. It can be descriptive or analytical. Descriptive cross-sectional study design measures prevalence of disease\/traits. Analytical cross-sectional study design evaluates associations between variables. However, it could not establish causality. Doing a cross-sectional study starts with identification of the purpose of the study. This is followed by development of the objectives that should follow the SMART criteria. A dummy table should also be constructed that is based on the objectives of the study so that needed data would not be missed. The next step is defining the population of the study followed by sample size computation which could be done using the Epi-info\u2122 program. Next, selection of sample population, ideally, using random sampling should be done. This is followed by the development of data collection methods. For cross-sectional studies, questionnaires are frequently used to collect data. 5A\u2019s of questionnaire development should be kept in mind when formulating the questionnaire. In addition, the use of the following should be avoided: double-barreled item; negatively worded item; statements as questions; agreement response anchors; and too few or too many response anchors. Data collection and data analysis will be done next. Analysis of data could also be done using the Epi-info\u2122 program. Descriptive statistics which includes frequency distributions, measures of central tendency and measures of variability provide a description and summary of participants data. Specific type of statistics is determined by the type of variable. For analytical type of cross sectional studies, measure of association could either be Prevalence Ratio (PR) or Odds Ratio (OR). Data independence and type of outcome data measured determine what statistical test to utilize in order to test the hypothesis. The STROBE statement should guide the writing of the final paper.<\/p>\n
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