Description
(1) SEM; Find a journal article that uses Structural Equation Modeling. Evaluate both the diagram and the fit statistics. Comment on: (a) appropriate use of SEM based on the author’s hypothesis, and (b) the goodness of fit of the model.
(2) CFA; Consider the RMSEA’S in TABLE 11.16 & 11.17 (Picture Attached). Do they offer us a clear choice as to which model is to be preferred?
(3) Comparing HCA, PCA & EFA; From the attached CHEN article, answer the following: (A) What is the difference between the total patient sample at baseline and the non-zero subgroup at baseline? Why is this important to whether or not clusters or factors are properly identified? (B) Examine the comparative results of the three different methods of analyzing the non-zero subgroup and creating symptom clusters as outlined in Tables 1, 2, 3, and 4. Review the results section analysis of them. Which method (cluster analysis, principal components analysis, or exploratory factor analysis) makes the most sense in terms of the way symptoms in cancer patients might be grouped? (C) These methods are data driven. What theory driven method might produce more useful results? Which method would you be most confident in using as a practitioner?
(4) Sports & Suicide ARIMA Model; From the attached ENCRENEZ article, answer the following: (A) The authors say they have a Seasonal ARIMA model of [(0,1,1) x (0, 1, 1)12 for their monthly data. Explain in terms of the variables in the article what this means? (B) The authors say they have an ARIMA model of (1,1,1) for their daily data. Explain in terms of the variables in the article what this means? (C) The authors conclude that sports teams winning important games over a series has the effect of decreasing suicides in men between the ages of 30 and 4. Evaluate their evidence and explain why you would accept or reject their argument.
(5) Regression Analysis; From the attached AMOCO sample data set, conduct a regression analysis and any other analysis’ in SPSS. Interpret the results & findings.
