The model reperesents an equation what value of c makes the equation true? 3 -6 -4 4
Answers
Answer:
sorry I'm can't see the picture
Step-by-step explanation:
Truei→Xi←ErroriXi=Ti+εi.
Truei→Xi←ErroriXi=Ti+εi.On the assumption of random error, the error term is assumed to be uncorrelated with the true score. Both sources make unique contributions to the observed score variance in a population: σX2=σT2+σε2. The ratio of true score to observed score variances is defined as the reliability of measure X:
Truei→Xi←ErroriXi=Ti+εi.On the assumption of random error, the error term is assumed to be uncorrelated with the true score. Both sources make unique contributions to the observed score variance in a population: σX2=σT2+σε2. The ratio of true score to observed score variances is defined as the reliability of measure X:ρX=σT2σX2=1−σε2σX2.
Truei→Xi←ErroriXi=Ti+εi.On the assumption of random error, the error term is assumed to be uncorrelated with the true score. Both sources make unique contributions to the observed score variance in a population: σX2=σT2+σε2. The ratio of true score to observed score variances is defined as the reliability of measure X:ρX=σT2σX2=1−σε2σX2.This formula demonstrates that reliability ranges between 0 and 1: if the entire observed variance is error, ρX=0; but if no random error exists, then ρX=1. Rearranging the reliability formula also reveals that the true score variance equals the observed score variance times the reliability: σT2=ρXσX2. Similarly, for two parallel measures (i.e., items having equal variances), the true score variance can be estimated as the product of their correlation (ρX1X2) and the variance of either measure; that is, σT2=ρX1X2σX2. Hence, reliability equals the correlation of two parallel measures, ρX=ρX1X2, while the correlation between a true score and its indicator equals the square root of the reliability: ρTX1=ρX. The measurement theory principles summarized in this section are encompassed within structural equation models and are used in the next section on the confirmatory factor analytic approach to modeling the relationships between observed indicators and latent constructs.