To illustrate an analysis of a survey, consider the hypothetical case of a company undertaking a market survey to understand the spending pattern of households, as a precursor to launch a financial planning service.
The population or the universe of such a study is all families with at least one income-earning member. The survey obviously cannot cover all income earners in the United States, and as such, administers the questionnaire to a sample group. The challenge before the researcher is to ensure the selected sample represents the universe, and for this, the researcher applies weights, margin of error of the sample size, and other techniques. The researcher may also undertake a test survey with one identified primary sampling unit to test the effectiveness of the sample, and the validity and soundness of the questionnaire and analysis methodology.
Before analyzing the collected data, the researcher needs to sort the data based on respondents, or stratify the sample. The common basis of categorization is according to demographics or income levels, age, geographical location, education, and more. Separating the sample into such categories allows the researcher to identify trends. For instance, people in a particular age group may tend to spend more on entertainment, people with children may tend to save a greater proportion of their income, people in a particular geographical area may tend to demonstrate some unique spending patters, and more.
The analysis of data depends on the survey design. Here, the analysis may include:
- T-test to validate hypothesis based on assumptions such as “people with two children below five years of age have a children’s education plan."
- Correlation analysis to link savings and age.
- ANOVA to analyze the variance of spending on certain categories such as food, entertainment, and lifestyle by age and geography, and more.