Where \(\mathrm{X}_{\mathrm{d}}\) is the difference score, \(\mathrm{X}_{\mathrm{T} 1}\) is the score on the variable at time 1, and \(\mathrm{X}_{\mathrm{T} 2}\) is the score on the variable at time ...Where \(\mathrm{X}_{\mathrm{d}}\) is the difference score, \(\mathrm{X}_{\mathrm{T} 1}\) is the score on the variable at time 1, and \(\mathrm{X}_{\mathrm{T} 2}\) is the score on the variable at time 2. What all of these names have in common is that they describe the analysis of two scores that are related in a systematic way within people or within pairs, which is what each of the datasets usable in this analysis have in common.
Where \(\mathrm{X}_{\mathrm{d}}\) is the difference score, \(\mathrm{X}_{\mathrm{T} 1}\) is the score on the variable at time 1, and \(\mathrm{X}_{\mathrm{T} 2}\) is the score on the variable at time ...Where \(\mathrm{X}_{\mathrm{d}}\) is the difference score, \(\mathrm{X}_{\mathrm{T} 1}\) is the score on the variable at time 1, and \(\mathrm{X}_{\mathrm{T} 2}\) is the score on the variable at time 2. What all of these names have in common is that they describe the analysis of two scores that are related in a systematic way within people or within pairs, which is what each of the datasets usable in this analysis have in common.
