Proper data analysis is a crucial step in understanding the results of your project. It is important that data analysis be done correctly so results can be used to accurately inform decisions.
Analyzing your Data
There are a number of software programs designed to analyze research data. Before starting your analysis, the data should be reviewed to identify and correct errors which may have occurred as the information was gathered. The Office of Measurement Services (OMS) and CLA-OIT Research Support Services offer professional data analysis services.
Below is a description of the most popular data analysis packages used at the University of Minnesota:
|Microsoft Excel||Can be used for basic data analysis. With Excel, you can create a database, code, enter, clean, and analyze your data.|
|SAS||Is a full-featured package that enables the programmer to perform powerful statistical analyses.|
|SPSS||Integrates and analyzes marketing, client, and operational data in key vertical markets.|
|AMOS||Provides structural modeling and analysis of covariance structures or casual modeling.|
|HLM||Analyzes data in a clustered, or nested, structure.|
|Conducts structural equation modeling and similar analyses.|
|Mplus||Offers exploratory factor analysis, confirmatory factor analysis, and structural equation modeling.|
Sometimes the language used to analyze survey data can be confusing. Here is an explanation of common terms:
- Descriptive Statistics: statistics that describe the sample data without drawing inferences about the larger population.
- Inferential Statistics: statistics that describe the sample data by drawing inferences about parameters of the population(s) you have sampled.
- Cumulative Frequency: the sum of the number of occurrences within a given answer, including all responses up to the present; increases with each successive addition.
- Frequency: the number of occurrences within a given answer.
- Mean: the average, or sum of responses divided by the number of responses.
- Median: the mid-point; a numeric value separating the lower half of the results from the upper half.
- Response Rate: the number of people who answered the survey request divided by the number of people in the sample; usually expressed as a percentage
- Standard Deviation: a measure of the variability from the average response; the average distance of scores from the mean score.
The task of data analysis becomes more complex when the number of items or questions is large. A frequency table can be constructed for each item on the survey, but this can result in upwards of 50 or 100 tables. As the volume of statistics increases, the problem becomes one of organizing the results into a coherent and meaningful set of findings.
Reporting your Findings
The final step of a survey project is reporting the findings. Reports should be informative, relevant to the target audience, and customized to the individual or organization’s needs. Reports can be presented as visual presentations, written, or electronic reports.
A comprehensive, formal report generally includes the following elements:
- Title Page
- Table of Contents
- Executive Summary
- Contextual Background of the Research and Purpose
- Survey/Research Population
- Research Methodology
- Conclusion and Recommendations
- Contact Information
Not all reports require each of the above elements. In fact, the method, scope, and type of project often determine which style of reporting is used. Other popular styles for reporting survey findings include:
|Transcription||Convert the verbal of all participants into text.||To present the information gathered through qualitative methods (Interviews, Focus groups).|
|Summary||Include the results of the survey and tables, charts, and graphs representing descriptive statistics. Typically shorter than formal reports.||
|Executive||Briefly addresses methods and findings, including only salient information and quotes. While typically written in less than three pages, the length depends on the scope of the project.||
Some factors to consider when writing a successful report include:
- Formatting: Keep the report organized to make it easier for the reader to comprehend the findings by using color and headings.
- Language: The language used in the report should be appropriate for the audience. Make the information understandable, define any terms that may be unfamiliar, and summarize the findings. The report writer can also use images or visual representations sparingly.
- Voice: All findings, interpretations, conclusions, and recommendations should be written objectively. Similarly, if the data is off-putting, present it in an informative manner rather than a judgmental manner.