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These patterns will influence how you analyze these variables or drop them from the analysis altogether. Plan to get to know your data by reviewing (1) the frequency of responses and descriptive statistics for each variable (2) the minimum, maximum, and average values for each variable (3) whether any variables have the same response for every record and (4) whether any variables have many or all missing values. A supplement to the data dictionary might include a copy of the questionnaire with the variable names written next to each question.
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Data dictionaries can be organized in different ways, but a tabular format with one row per variable, and columns for name, description, type, legal value, and comment is easy to organize (see example in Table 8.1 from an outbreak investigation of oropharyngeal tularemia ). Typically, a data dictionary lists each variable’s name, a brief description, what type of variable it is (e.g., numeric, text, or date), allowable values, and an optional comment. A data dictionary is a document that provides key information about each variable. Data from a cross-sectional study or survey might need to incorporate weights or design effects in the analysis.The analysis plan should specify which variables are most important-exposures and outcomes of interest, other known risk factors, study design factors (e.g., matching variables), potential confounders, and potential effect modifiers. Data from a case–control study must be analyzed by comparing exposures among case-patients and controls, and the data must account for matching in the analysis if matching was used in the design. For example, data from a simple retrospective cohort study should be analyzed by calculating and comparing attack rates among exposure groups. Therefore, the analysis strategy must be consistent with how the data will be collected. Different types of studies (e.g., cohort, case–control, or cross-sectional) are analyzed with different measures and methods. A more specific question-for example, “Which foods were more likely to have been consumed by case-patients than by controls?”-indicates that key variables will be food items and case–control status and that the analysis method will be a two-by-two table for each food. For example, the question, “What caused the outbreak of gastroenteritis?” might be a suitable objective for a field investigation, but it is not a specific research question. Well-reasoned research questions or hypotheses lead directly to the variables that need to be analyzed and the methods of analysis. The analysis plan usually begins with the research questions or hypotheses you plan to address.
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Some of the listed elements are more likely to appear in an analysis plan for a protocol-based planned study, but even an outbreak investigation should include the key components in a more abbreviated analysis plan, or at least in a series of table shells. It lays out the key components of the analysis in a logical sequence and provides a guide to follow during the actual analysis.Īn analysis plan includes some or most of the content listed in Box 8.1. It describes where you are starting (data sources and data sets), how you will look at and analyze the data, and where you need to finish (final report).
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Therefore, the time to decide what data to collect and how to analyze those data is before you design your questionnaire, not after you have collected the data.Īn analysis plan is a document that guides how you progress from raw data to the final report. Planning is necessary to ensure that the appropriate hypotheses will be considered and that the relevant data will be collected, recorded, managed, analyzed, and interpreted to address those hypotheses. A thoughtfully planned and carefully executed analysis is as crucial for a field investigation as it is for a protocol-based study.