Psychometric properties of the Barkin Index of Maternal Functioning with Ethiopian mothers
Tiruneh, S. G.; Yeshitela, Y. G.; chisha, Y. C.; Tekabe, B.; Trevethan, R.
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BackgroundThe functional status of mothers after childbirth has implications for maternal, child, and family health. There is a lack of adequate reliable and valid instruments in Ethiopia for assessing womens postpartum functional status. ObjectiveThis study was intended to reveal the psychometric properties of the Barkin Index of Maternal Functioning (BIMF) for assessing Ethiopian mothers functional status. MethodStructured interviews were used to obtain BIMF data from 202 women who had a child less than 1 year of age. Descriptive statistics were calculated for the BIMF items; internal consistency was assessed with interitem correlations and coefficient alphas; construct validity was examined through exploratory factor analyses (EFAs) after face and content validities had been confirmed; and test-retest reliability was assessed with intraclass correlation coefficients (ICCs). ResultsNarrow standard deviations and significant skewness and kurtosis characterized most of the individual BIMF items. Most interitem correlations were < |.15| and 13 of the 20 BIMF items did not load satisfactorily on any factor in exploratory factor analyses. Two factors emerged from the remaining items, one with three items and the other with four. Coefficient alphas were .54 for the first of these factors, .48 for the second, and .58 for all 20 items. The ICCs for test- retest reliability were < .40. ConclusionsBIMF data from the sample of Ethiopian women in this study exhibited unusually low levels of variability and high levels of skewness and kurtosis. Furthermore, in the context of this study, the BIMF could not be regarded as reliable either in terms of internal (interitem) consistency or temporal (test-retest) consistency. Researchers using the BIMF in Ethiopian contexts are advised to examine the nature of their data carefully, identify the factor structure of their samples data, and consider ways in which the index might be improved, or replaced.
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