Psycho-oncology · 2018
Examining the dimensionality of the Fear of Cancer Recurrence Inventory.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Galica Jacqueline, Brennenstuhl Sarah, Maheu Christine, Townsley Carol, Metcalfe Kelly
Аннотация
OBJECTIVE: Fear of cancer recurrence (FCR) is a common concern among cancer survivors, and the Fear of Cancer Recurrence Inventory (FCRI) is a frequently used measure to assess FCR. Given that the dimensionality of FCR has received recent debate, the overall goal of this secondary analysis was to re-examine the dimensionality of the FCRI using confirmatory factor analyses (CFA) to compare models of FCR, using data from a large sample of cancer survivors.
METHODS: Three models of FCR (including unidimensional and multidimensional models of the FCRI) were informed by the literature and proposed a priori. Separate CFAs were conducted to test the fit of each model to the data, and models with acceptable fits were compared.
RESULTS: Of all the tested FCR models, a multidimensional first-order model aligned with the originally developed 7-subscale FCRI revealed the best fit to the data (χ2 = 3359.135, P < .0001, df = 795, RMSEA = 0.057 [0.055, 0.059], CFI = 0.897, TLI = 0.888). When this 7-factor structure was loaded onto a single, second-order factor of overall FCR, the model fit statistics were slightly poorer (χ2 = 3459.632, P < .0001, df = 807, RMSEA = 0.058 [0.056, 0.060], CFI = 0.893, TLI = 0.886). However, the difference between the models was significant (chi-square difference = 103.142, P < .0001, df = 12) indicating that the first-order model was a better fit to the data.
CONCLUSIONS: These results align with empirical and theoretical literature that supports the use of the FCRI as a multidimensional scale. Implications of results are discussed in light of FCR conceptualization and measurement.
METHODS: Three models of FCR (including unidimensional and multidimensional models of the FCRI) were informed by the literature and proposed a priori. Separate CFAs were conducted to test the fit of each model to the data, and models with acceptable fits were compared.
RESULTS: Of all the tested FCR models, a multidimensional first-order model aligned with the originally developed 7-subscale FCRI revealed the best fit to the data (χ2 = 3359.135, P < .0001, df = 795, RMSEA = 0.057 [0.055, 0.059], CFI = 0.897, TLI = 0.888). When this 7-factor structure was loaded onto a single, second-order factor of overall FCR, the model fit statistics were slightly poorer (χ2 = 3459.632, P < .0001, df = 807, RMSEA = 0.058 [0.056, 0.060], CFI = 0.893, TLI = 0.886). However, the difference between the models was significant (chi-square difference = 103.142, P < .0001, df = 12) indicating that the first-order model was a better fit to the data.
CONCLUSIONS: These results align with empirical and theoretical literature that supports the use of the FCRI as a multidimensional scale. Implications of results are discussed in light of FCR conceptualization and measurement.