Market research consultants, internal research departments, statisticians and ‘advanced quant’ teams No advanced analysisRequires the user to do ‘programming’ No Advanced analysisAssumes you have access to a DP department that uses SPSS data collection productsĮfficient and flexible creation of crosstabs Well-integrated with SPSS data collection products Weighting Automation (e.g., exporting to office, updating trackers)Ĭreation of complex crosstabs is less flexible than in WinCross, Survey Reporter and MarketSight Market research consultants and the internal research departments of large companiesĮasy to learn/useQuickly create large numbers of crosstabsĮasier to learn and use than SPSSAdvanced analysis Little or no experience in analyzing surveysĮase of use and sharing resultsAdvanced analysisĬreation of complex crosstabs is less flexible than in WinCross, Survey Reporter and MarketSightDoes not support Tracking studies This link takes you to tutorials which you can use to evaluate the different programs.
The best way to evaluate whether the software is likely to be useful is via having a trial. The next table provides an overview of more specialized types of software used in survey analysis.
Some of the main programs for doing this are described in the table below. Most people who analyze surveys do so using general-purpose survey analysis software. General-purpose software for analyzing surveys However, if there is a need to share reporting with others, to fix problems with the data or to conduct more sophisticated analyses, it is generally necessary to use software specifically designed for the analysis of survey data. Where only rudimentary analysis is required, such as working out the proportion of people to select each option, this is usually the best approach.
The easiest option for analyzing survey data is usually to use the analysis tools that come for free in the Data Collection Software that has been used to collect the data. Basic process Choosing Survey Analysis Software However, in practice it is much more efficient to simultaneously clean and tidy the data and then weight the data. It is possible to first clean the data, then tidy the data and, then, if necessary, weight the data. These steps are also sometimes referred to as data processing Weighting is a technique which adjusts the results of a survey to bring them in line with what is known about the population.For example, changing birth dates into age categories, or removing ‘don’t know’ categories. Data tidying involves manipulating the way that data is set up to make it easier to interpret.The unglamorous world of data cleaning can be a key determinant of the quality of data analysis, particularly when the data is from a messy source (e.g., customer records, collected using a cheap data collection program). The goal is to identify data that is, in some way, clearly incorrect. Data cleaning refers to checking and correcting anomalies in a data file.Cleaning, tidying, and weighting are activities that are performed before trying to work out what the data in a survey means.