# Analyzing and interpreting data Evaluation resources from Wilder Research

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4 The overall goal of inferential analysis is to determine whether results are meaningful. For example, did participants change in important ways over time? Were participants really different from people who did not receive services? In statistical terms, the meaningfulness of findings is typically described in terms of significance. There are two common forms of significance: Statistical significance: Using probability theory, statistical significance indicates whether a result is stronger than what might have occurred due to chance or random error. To be considered significant, there has to be a high probability that the results were not due to chance or random variation. When this occurs, we can infer that a relationship between two variables is strong and reliable. Several factors influence the likelihood of significance, including the strength of the relationship, the amount of variability in the results, and the number of people in the sample. Clinical significance: Statistical significance can be difficult to obtain, especially when data are available for a relatively small number of people. As a result, some evaluations focus instead on clinical significance. Clinical significance is sometimes seen as having more practical value, but only when there is a clear and justified rationale for establishing the underlying standards. Clinical significance does not necessitate statistical significance, but rather is based on what appears to be practically significant based on existing knowledge, expertise, and its meaning. Tips for analysis Review and clean data to make sure everything is accurate, complete, and any inconsistencies have been resolved before beginning your analysis. Leave enough time and money for analysis it is easy to focus so much on data collection that you do not leave enough resources to analyze the results. Identify the appropriate statistics for each key question get consultation if needed. Do not use the word significant when describing your findings unless it has been tested and found to be true either statistically or clinically. Keep the analysis simple. Analyzing and interpreting data 4 Wilder Research, August 2009

6 Do the results make sense? Are there any findings that are surprising? If so, how do you explain these results? Are the results significant from a clinical or statistical standpoint? Are they meaningful in a practical way? Do any interesting stories emerge from the responses? Do the results suggest any recommendations for improving the program? Do the results lead to additional questions about the program? Do they suggest that additional data may need to be collected? Involve stakeholders. While evaluation findings must be reported objectively, interpreting those findings and reaching conclusions can be a challenging process. Consider including key stakeholders in this process by reviewing findings and preliminary conclusions with them prior to writing a formal report. Consider practical value, not just statistical significance. Do not be discouraged if you do not obtain statistically significant results. While a lack of significant findings may suggest that a program was not effective in promoting change, other factors should also be considered. You may have chosen to measure an outcome that was too ambitious, such as a behavioral change that takes longer to emerge. In interpreting your results, consider whether there are alternate explanations for the lack of significance. It is also important to consider the practical significance of the findings. Some statistically significant results do not yield important information to guide program enhancements, while some findings that are not significant are still useful. Watch for, and resolve, inconsistencies. In some cases, you may obtain contradictory information. For example, you may find that stakeholders describe important benefits of the services, but these improvements do not appear in pre-posttest comparisons. Various stakeholders may also disagree. For instance, staff may report improvements in participants that are not reported by the participants themselves. It can be challenging to determine which information is accurate, especially when comparing different peoples viewpoints or perspectives. It is important to consider the validity of each information source, and to remember that various stakeholders can have valid viewpoints that vary based on their unique perspectives and experiences. Try to resolve these discrepancies and reflect them in your findings to the extent possible. Analyzing and interpreting data 6 Wilder Research, August 2009

7 Summary Organizing and analyzing your data, while sometimes daunting, is how you learn the meaning and results of an evaluation. Plan ahead for this phase of the project, taking into consideration the time, budget and expertise necessary to complete this essential phase of any evaluation. Tips for analyzing quantitative and qualitative data are identified throughout this section. Quantitative analysis can be descriptive, such as using frequency distributions, mean, median, mode, and variability. Quantitative analysis can also be inferential, a process that requires greater technical skills. Qualitative analysis consists of analyzing non-numerical data, such as information gathered from interviews, focus groups, or open-ended survey questions. The key to analyzing qualitative information is to make it manageable. Take care to interpret results accurately and to report sounds conclusions. Get input from stakeholders to ensure that everyone is drawing similar conclusions from the information available. Wilder Research Information. Insight. Impact. For more information To learn more about evaluation AUGUST Lexington Parkway North Saint Paul, Minnesota Analyzing and interpreting data 7 Wilder Research, August 2009

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