Nonprofit leaders increasingly recognize the value of data, but how can nonprofits move beyond this to teach employees at all levels to make data-driven decisions rather than anecdotal, emotional or knee jerk decisions? In this series, the 7 Steps to Data Driven Decision Making, we are sharing the simple process we use to teach our clients how to do just that.

Once you have framed the issue, developed hypotheses and collected data, it is time for Step 4: Data Analysis.

Data Analysis

Many people cringe at the idea of data analysis. However, you don’t need to be a statistician to successfully analyze and interpret data. Basic statistical skills will usually suffice. For example, you should be able to:

• Digest and assess statistical data,

• Calculate summary statistics (average, median, standard deviation and distributions), and

• Understand statistics well enough to explain the findings to key stakeholders.

Below, we give some examples of how organizations analyze their data, providing links for some statistical terms with which you may want to become more familiar.

Examples of Data Analysis Methods

Case Study 1: Foundation

Sarah is a Program Manager for a capacity-building foundation who wants to increase volunteer retention. She prioritized three hypotheses to test and collected relevant data from an existing internal database.

Sarah began to analyze her data by calculating summary statistics (average, median, standard deviation) to see how her data was distributed. To test her hypotheses and understand which factors would help her understand volunteer retention, she then looked for correlations between variables. She examined correlation coefficients to determine which variables had the strongest positive or negative relationships.

Case study 2: Private school

John, the principle of an independent school wants to investigate the perceptions and performance of his middle school math program. He gathered his data from parent surveys, test scores, staff evaluation forms and training documents.

John’s data showed that parents in 7th and 8th grade rated his school’s math program lower than they rated other subjects (Exhibit 1). However, John knew that he couldn’t rely on the averages alone for his analysis.

As he examined the average scores, he also to care to look at the sample size for each group (Exhibit 2) so that he could properly interpret the results. He noticed that, while the music and art programs received high scores, these programs were electives that only a small subset of students used. As a result, the sample size of parents rating these programs was much smaller and not comparable to the averages from larger sample sizes.

Exhibit 1

Exhibit 2

John began to notice trends in his descriptive statistics and decided to use t-tests to determine whether his observations were statistically significant.

Your Data Anaylsis

Your data analysis will vary depending on the type of data you have collected and the hypotheses that you are trying to test, but the process does not have to be complicated. Start with basic summary statistics like Sarah and John did. Be sure not to examine simple averages alone, as these can be misleading when isolated from important information like sample size and standard deviation.

You can use excel to calculate correlations, run t-tests and perform other statistical analyses, depending on your skill level. If you are familiar with statistics and statistical software packages like Stata and SPSS, these will provide the best advanced analysis options.

Want to beef up your statistical skills? You can find nearly unlimited resources online, including videos, free google books, articles and blogs. If you would prefer some expert guidance, consider signing up your organization for one of Measuring Success’ Building Data Competency trainings. We can easily customize one of these workshops to meet your unique needs. Email us if you would like more information.

Once you have analyzed your data, it is time to re-examine your hypotheses in light of what you have found. Check out next month’s newsletter for SHARETHIS.addEntry({ title:'How Do You Create a Data Driven Culture? Step 4: Data Analysis', url: 'http://measuring-success.com/archive/7StepsDataAnalysis/', summary:'Many people cringe at the idea of data analysis. However, you don’t need to be a statistician to successfully analyze and interpret data. Basic statistical skills will usually suffice. In this fourth installment of our <a href="http://measuring-success.com/archive/Make%20culture%20data-driven/"> 7 Steps to Data-Driven Decision Making</a> series, we list some of the basic skills you will need to analyze your data and how other organizations have used these skills to make data driven decisions.', }, {button:true} ); Newer | Archive | Older