Factor analysis is a statistical method that explores correlations and patterns between measurements. There are two types: exploratory and confirmatory, which can be used together. The analysis involves collecting measurements, determining correlations, and extracting possible factors. The factors and final scores can be interpreted, and the analysis can lead to an unlimited amount of important factors. Exploratory factor analysis is used to understand what influences measurements, while confirmatory factor analysis examines specific factors. The number of measurements required is important, with confirmatory testing requiring more than exploratory testing.
Factor analysis is a type of statistical analysis that investigates different correlations and patterns that may occur between measurements. There are two types of factor analysis; exploratory and confirmatory. These two versions can be used individually or combined. There are many different types of statistical calculations that are used within this analysis.
A common first step used in factor analysis includes collecting the measurements in the experiment. Correlation mathematics is used to determine the correlations that exist. The researcher will determine whether all factors calculated from the analysis will be included. Some experiments will require certain factors to be included in the statistics and others to be excluded.
One method used to extract possible factors is maximum likelihood. This calculation is so complicated that statistical computer programs are used, as a researcher typically cannot do the calculation manually. The factors within the analysis can also be combined in various ways. The analysis will require that the order of the factors be rotated or combed in a way that explains the large variance or spread of the data.
Once the factors and final scores have been calculated, the data can be interpreted. The factors that have the highest scores will have the greatest influence on the measurements. These scores can also be used for further statistical analysis. Unlike other types of statistical analysis, this analysis can lead to an unlimited amount of important factors, rather than limiting factors to a small group.
Exploratory factor analysis is used to understand what things in nature can influence certain measurements. How strongly these factors influence the measurements is also of interest in the exploratory version. These are not preset before measurements are taken. With confirmatory factor analysis, there are specific factors that are examined before the calculations.
Both types of factor analysis can be used within an experiment. The exploratory version can be used to create a theory, while the confirmatory version is used to prove that theory. If the confirmatory analysis is not favorable, the researcher may need to adjust the way the exploratory analysis is calculated.
The number of measurements required for these calculations is important. Most calculations require at least ten measurements, if not more. Confirmatory testing will usually require many more measurements than exploratory testing. Sometimes at least 200 measurements are needed for a successful analysis. As a general rule, using more measurements usually results in more reliable data, although the number needed depends on the experiment.
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