Like large scale demographic surveys, marketing surveys require at least two stages of rigorous treatment. Careful data collection, cleaning and finally data mining is required to make the dataset ready for the analysis. In both data management and the analysis part SPSS can be regarded as the most useful software tool. SPSS has a spreadsheet interface for data management, which can be manipulated by state-of-the-art syntax coding. Together with the spreadsheet SPSS has advanced statistical tools and graphics engine that can be used to analyze the survey data. Key utilities that can be in used in SPSS in dealing with marketing surveys are as follows:
- Cross tabulation – to make custom build summary statistics of different subgroups.
- Missing data analysis – to fill up missing data or cleaning the missing data from the original dataset
- Forecasting and trend analysis – to predict future trend of products
- Regression analysis – to understand the effect of factors on something in question
- Discriminant analysis – to separate one seemingly related factors with another
One of great SPSS utilities is that it has user friendly database management tools in it. Within a very short time, without writing huge amount of codes, we can summarize and process the survey results with its help. Let’s have an example using a SPSS sample dataset. Let’s assume our thesis writing service made a survey with different questions including demographic information. Now we want to see how the race of the respondent differs in family planning issues. We can do a cross-tabulation having race in the rows and number of children in columns. The results will be the following:
Table: Cross tabulation of race and family size
To interpret the survey descriptive statistics more rigorously, SPSS offers a number of sophisticated statistical measures.
Figure: 1 Different statistical measures related to cross tabulation.
Our thesis writing service can facilitate the client on several stages of marketing surveys, ranging from the very early data cleaning stage to advanced statistical processing like regression analysis, logistic regression analysis etc.