![]() The DF is calculated separately for one sample and two sample t-test. ![]() If there were multiple groups in the model (as in Example 12 in the AMOS 4 User's Guide), then you would multiply the number of moments per group (variances, covariances and means (if means are requested in model)) by the number of groups. Two Sample T-Test Formula: df (n 1 + n 2) - 2 Where, df Degree of Freedom n 1 Total Number in Sequence 1 n 2 Total Number in Sequence 2. Add the 14 sample means and you have 105+14=119 sample moments. (There are 14*14=196 total elements in the covariance matrix, but the matrix is symmetric about the diagonal, so only 105 values are unique). For 14 observed variables, this equals 14 variances and 14*13/2 = 91 covariances for a total of 14+91=105 unique values in the sample covariance matrix. The number of degrees of freedom is the number of values in the final calculation of a statistic that are free to vary. For K observed variables, the number of unique elements in the sample covariance matrix is K*(K+1)/2, comprised of K variances and K*(K-1)/2 covariances. The equation you need to use depends on what type of test or procedure you’re performing. The degrees of freedom (df) of a statistic are calculated from the sample size (n). For a chi-square test, the degree of freedom assists in calculating the number of categorical variable data cells before calculating the values of other cells. In general the number of degrees of freedom equals:ĭF = Number of sample moments - Number of free parameters in the model.įrom your question, I understand that you have 14 observed variables and that you have requested a model with means and intercepts. Step 2: Calculate the degrees of freedom.
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