The research purpose was to examine the psychometric properties of the Kinship Center Attachment Questionnaire (KCAQ) of Latency-Aged for Children. The research method was descriptive-validation through development and validation of an instrument. The sample consisted of 400 children were selected by multi-stage cluster random sampling method. Measuring instruments were the Kinship Center Attachment Questionnaire (KCAQ), Maternal Emotional Styles Questionnaire (MESQ), and Strength and Difficult Questionnaire (SDQ). Data analysis was done using SPSS and Amos software’s version 16. The scale was assessed using factor analysis and principle component method (KMO = 0.73). In total, analysis approved the results of exploratory factor analysis based on the varimax rotation. In addition, this model was found fit with the data. The KCAQ demonstrated high internal consistency. A re-administration of the measure 1 month later yielded high correlation (P<0.001). Pearson’s correlation between KCAQ, MESQ, SDQ and were respectively this four factors accounted for 48.72% of the total variance. Confirmative factor analysis confirming their convergent, divergent and concurrent validity. Results of this study showed Persian version of KCAQ has good psychometrics particularity in measuring for child’s attachment in latency-aged.
S, H., B, L., & L, M. (2014). Psychometric Properties of the Kinship Center Attachment Questionnaire (KCAQ) of Latency-Aged for Children. Psychological Models and Methods, 4(16), 41-63.
MLA
H. S; L. B; M. L. "Psychometric Properties of the Kinship Center Attachment Questionnaire (KCAQ) of Latency-Aged for Children". Psychological Models and Methods, 4, 16, 2014, 41-63.
HARVARD
S, H., B, L., L, M. (2014). 'Psychometric Properties of the Kinship Center Attachment Questionnaire (KCAQ) of Latency-Aged for Children', Psychological Models and Methods, 4(16), pp. 41-63.
VANCOUVER
S, H., B, L., L, M. Psychometric Properties of the Kinship Center Attachment Questionnaire (KCAQ) of Latency-Aged for Children. Psychological Models and Methods, 2014; 4(16): 41-63.
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