My understanding of the Chi-square in EFA is that this statistic measures the discrepancy between the observed and expected covariance matrices. A lower value indicates a smaller discrepancy, suggesting a better fit of the model to the data. Thus, the p-value associated with the Chi-square statistic indicates the probability that the observed data could have occurred under the null hypothesis. A high p-value (typically greater than 0.05) suggests that the model fits the data well, while a low p-value (typically less than 0.05) suggests a poor fit. Though keeping in mind that the Chi-square test is sensitive to sample size. With large samples, even small discrepancies can lead to significant Chi-square values, potentially suggesting a poor fit even when the model is reasonable.
Could you explain further about the F value and degrees of freedom and why/how these are significant if P value is < 0.05? And also what do the Durbin-Watson numbers signify?
Thank you for the tutorial. I understand why you rejected the null with the P value from the anova table. But could you explain what the P values indicate in the coefficient table?
Wow I love how JASP will update the information if you keep the spreadsheet open, edit and save -- I used to start all over again when I wanted to make a change! Very cool tip
My understanding of the Chi-square in EFA is that this statistic measures the discrepancy between the observed and expected covariance matrices. A lower value indicates a smaller discrepancy, suggesting a better fit of the model to the data. Thus, the p-value associated with the Chi-square statistic indicates the probability that the observed data could have occurred under the null hypothesis. A high p-value (typically greater than 0.05) suggests that the model fits the data well, while a low p-value (typically less than 0.05) suggests a poor fit. Though keeping in mind that the Chi-square test is sensitive to sample size. With large samples, even small discrepancies can lead to significant Chi-square values, potentially suggesting a poor fit even when the model is reasonable.
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what if i have a degrees of freedom of 6, how would i interpret the association between the variables?
Could you explain further about the F value and degrees of freedom and why/how these are significant if P value is < 0.05? And also what do the Durbin-Watson numbers signify?
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Thank you for the tutorial. I understand why you rejected the null with the P value from the anova table. But could you explain what the P values indicate in the coefficient table?
Thank you.
Thank you
I think you need to use a correct effect size calculated.
Wow I love how JASP will update the information if you keep the spreadsheet open, edit and save -- I used to start all over again when I wanted to make a change! Very cool tip