Chi-square statistic measures how well the observed correlation matrix matches the predicted correlation matrix based on the factor model. The higher the chi-square statistic, the worse the fit. The chi-square statistic follows a chi-square distribution, which allows us to calculate a p-value for testing the hypothesis that a certain number of factors are sufficient to represent the data. A low p-value (usually less than 0.05) indicates that we can reject the hypothesis and conclude that more factors are needed. A high p-value (usually greater than 0.05) indicates that we cannot reject the hypothesis and conclude that the number of factors is adequate. To perform this test in EFA in R, we can use the psych::fa() function, which provides the chi-square statistic and p-value for testing the hypothesis that a certain number of factors are sufficient.