What sample size do you need for confirmatory factor analysis?
What sample size do you need for confirmatory factor analysis?
Minimum Sample Size Recommendations for Conducting Factor Analyses. There is no shortage of recommendations regarding the appropriate sample size to use when conducting a factor analysis. Suggested minimums for sample size include from 3 to 20 times the number of variables and absolute ranges from 100 to over 1,000.
How do you do a confirmatory factor analysis?
Steps in a Confirmatory Factor Analysis. The first step is to calculate the factor loadings of the indicators (observed variables) that make up the latent construct. The standardized factor loading squared is the estimate of the amount of the variance of the indicator that is accounted for by the latent construct.
What does confirmatory factor analysis measure?
Confirmatory factor analysis (CFA) is a statistical technique used to verify the factor structure of a set of observed variables. CFA allows the researcher to test the hypothesis that a relationship between observed variables and their underlying latent constructs exists.
What is the minimum sample size for CFA?
Simulation studies show that with normally distributed indicator variables and no missing data, a reasonable sample size for a simple CFA model is about N = 150 (Muthén and Muthén, 2002). For multi-group modeling, the rule of thumb is 100 cases/observations per group (Kline, 2005).
How many participants do you need for a CFA?
At least 10 respondents per parameter or question in questionnaire. Again a rule of thumb, which is globally accepted.
What is confirmatory data?
a statistical analysis designed to address one or more specific research questions, generally with the aim of confirming preconceived hypotheses.
Can CFA be done in SPSS?
When conducting a CFA, it is always good practice to examine each variable before performing further analyses. This can be done in SPSS.
How does CFA calculate sample size?
How does exploratory factor analysis differ from confirmatory factor analysis?
In exploratory factor analysis, all measured variables are related to every latent variable. But in confirmatory factor analysis (CFA), researchers can specify the number of factors required in the data and which measured variable is related to which latent variable.
How does exploratory factor analysis work?
Exploratory factor analysis is a statistical technique that is used to reduce data to a smaller set of summary variables and to explore the underlying theoretical structure of the phenomena. It is used to identify the structure of the relationship between the variable and the respondent.
What is 10 times rule sample size?
A widely used minimum sample size estimation method in PLS-SEM is the ’10-times rule’ method (Hair et al., 2011), which builds on the assumption that the sample size should be greater than 10 times the maximum number of inner or outer model links pointing at any latent variable in the model.