Browsing by Subject "Analysis of covariance (ANCOVA)"
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Item Differentiation and healthy family functioning(2012-08) Kim, Hyejin; Prouty, Anne M.; Smith, Douglas B.; Ko, Mei-Ju; Wetchler, Joseph L.Inconsistent results have been found in prior research on differentiation of self in Bowen Family Systems Theory and its application to individuals, couples, and families of different cultural backgrounds. In this regard, the present study examined the impact of differentiation of self on healthy family functioning with 183 participants including Koreans in South Korea, South Koreans in the United States, and White American in the United States. Multigroup Confirmatory Factor Analysis identified good construct validity of a measurement (DSI-R) used for the three groups of this study. An analysis of covariance (ANCOVA) found significant differences among the three groups with regard to the level of differentiation. Results of a multivariate analysis of covariance (MANCOVA) showed significant effects of differentiation levels on family functioning, family satisfaction, and family communication. This study also examined the relationships between differentiation and family functioning within a collectivistic Korean culture. Koreans residing in South Korea (n=235) participated in this study, and ranged in age from 20 to 70 years. Confirmatory Factor Analysis showed that the Differentiation of Self Inventory-Revised had adequate construct validity for use with South Koreans. An analysis of variance (ANOVA) revealed that older South Koreans had higher differentiation levels than younger South Koreans. Regression results showed that balanced and healthy family functioning was significantly related to greater family satisfaction and more positive family communication. Results of a multivariate analysis of covariance (MANCOVA) revealed there were significant differences between the high differentiation group and the low differentiation group across family functioning, family satisfaction, and family communication. The author discusses implications for clinical practice, interventions, and future research.Item Multiple comparison procedures in analysis of covariance model(2011-08) Jia, Shihui; Mansouri, Hossein; Westfall, Peter H.In analysis of covariance, multiple comparison techniques are used to make inference about the treatment effects. An important feature of a good MC technique is controlling the familywise error rate for the family of tests considered. Except for the balanced designs, critical values of some simultaneous tests are not available. Simulation techniques are used effectively for the computation of the critical values for multiple comparisons. This investigation involves comparing the performance of the parametric and rank tests for multiple comparisons in the analysis of covariance models. The MC techniques considered are: Bonferroni, Scheffe’, Fisher’s stepwise least significant difference technique, and the techniques based on simulation. Robustness of these testing procedures is studied based on the violations of the underlying distributional assumptions. Under each study condition, performance of each test will be evaluated. SAS IML programming language is used for the simulation study.