Factor analysis reduces large sets of data, such as survey data, to explain related outcomes in terms of a small number of underlying factors. Making the results of a factor analysis understandable to any audience, regardless of statistical knowledge, poses a challenge as great as the analysis itself. Follow the steps below to prepare a presentation on a hypothetical survey.
Name and describe each underlying factor, using one or more slides in your PowerPoint presentation to do so. You can name each factor based on the pattern of correlations that emerge from your analysis. The factors are those unmeasured, or underlying, issues that help explain the responses to your series of survey questions. Patterns of responses, for example, about views on political and social issues might suggest that religious values may have influenced responses. Religious values would therefore be an underlying factor.
Provide a graphical display of your analysis results in one slide of your PowerPoint presentation, using a diagram known as a common factor model. The diagram, which resembles a flow chart, uses boxes and ovals to illustrate the variables you measured (the survey questions and responses) and the factors that explain such responses, respectively. Lines and arrows clearly illustrate which factors influence which responses.
Explain your factor analysis results in greater detail on another slide, showing a table that displays the correlations between survey responses and the factors that may influence them. This table is known in factor analysis as a factor loading matrix. Factor loadings are measures of correlation. The layout of this table often shows each factor as a column heading and each variable as a row. Each survey question, for example, would represent a single row. The table will display the correlation scores between the survey responses and the factors that influence responses and show your audience the strength of the correlations.
Use data tables to report the results of your analysis. A factor analysis report should display, in a table, the correlations between individual survey items and the factors that explain them. Highlight the important findings in the text reference accompanying the table of correlations, also known as factor loadings.
Name and identify the underlying factors, based on the patterns of correlation among the variables or survey items analyzed. Measures that are highly correlated--either positively or negatively--are likely to be influenced by the same factors.
Explain and discuss the important findings in the results section of your report.
Expand the technical details of your analysis in the methodology section. Keeping the results and technical details sections separate will enable readers that lack extensive statistical knowledge to read and understand the most important findings in your analysis, while allowing the more statistically inclined readers to explore the technical details in a separate section.