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Abstract

The instrument Families’ Importance in Nursing Care–Nurses’ Attitudes (FINC-NA) was developed to measure nurses’ attitudes toward the importance of families in nursing care. The low variations in item responses, which affect the discrimination ability and unstable internal consistency, have been considered as limitations. The aim of this study was to refine and revalidate FINC-NA regarding score distribution, homogeneity, dimensionality, differential item functioning for gender, stability, and internal consistency. There were 246 registered nurses studying at advanced levels who answered the revised FINC-NA. The FINC-NA had five response alternatives. The findings showed that although some subscales still deviated from a normal distribution, the variability of the scores and the homogeneity was improved. In addition, the dimensionality was reproduced and minor problems with differential item functioning for gender were detected. All FINC-NA scales showed good reliability. The results allow the use of the revised FINC-NA in studies where an assessment of nurses’ attitudes toward families’ importance in nursing care is desired.

Keywords

nurses’ attitudes family nursing instrument development

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References

Benzein

Johansson

Årestedt

K. F.

Saveman

B.-I.

(2008). Nurses’ attitudes about the importance of families in nursing care: A survey of Swedish nurses. Journal of Family Nursing, 14, 162-180. doi:10.1177/1074840708317058.

Benzein

Johansson

Årestedt

K. F.

Berg

Saveman

B.-I.

(2008). Families’ importance in nursing care: Nurses’ attitudes—An instrument development. Journal of Family Nursing, 14(1), 97-117. doi:10.1177/1074840707312716.

Cook

Heath

Thompson

R. L.

(2000). A meta-analysis of response rates in web- or Internet-based surveys. Educational and Psychological Measurement, 60, 821-836. doi:10.1177/00131640021970934.

Couper

M. P.

Blair

Triplett

. (1999). A comparison of mail and e-mail for a survey of employees in federal statistical agencies. Journal of Official Statistics, 15, 39-56.

Cronbach

L. J.

(1951). Coefficient alpha and the internal structures of tests. Psychometrika, 16, 297-334.

D’Agostino

R. B.

Belanger

D’Agostiono

R. B.

Jr. (1990). A suggestion for using powerful and informative tests of normality. The American Statistician, 44, 316-321.

Fabrigar

L. R.

Wegener

D. T.

MacCallum

R. C.

Strahan

E. J.

(1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4, 272-299.

Fayers

Machin

. (2007). Quality of life (2nd ed.). Chichester, UK: John Wiley.

Groenvold

Petersen

M. A.

(2005). The role and use of differential item functioning (DIF) analysis of quality of life data from clinical trials. In Fayers

P. M.

Hays

R. D.

(Eds.), Assessing quality of life in clinical trials (2nd ed, pp. 195-208). New York, NY: Oxford University Press.

10.

Hair

J. F.

Black

W. C.

Babin

B. J.

Anderson

R. E.

(2008). Multivariate data analysis: A global perspective (7th ed.). Upper Saddle River, NJ: Pearson Education.

11.

Hurley

A. E.

Scandura

T. A.

Schriesheim

C. A.

Brannick

M. T.

Seers

Vandenberg

R. J.

Williams

L. J.

(1997). Exploratory and confirmatory factor analysis: Guidelines, issues, and alternatives. Journal of Organizational Behavior, 18, 667-683.

12.

Kwak

Radler

. (2007). A comparison between mail and web surveys: Response pattern, respondent profile, and data quality. Journal of Official Statistics, 18, 257-273.

13.

McHorney

C. A.

Tarlov

A. R.

(1995). Individual-patient monitoring in clinical practice: Are available health status surveys adequate? Quality of Life Research, 4, 293-307.

14.

Nunnally

J. C.

Bernstein

I. H.

(1994). Psychometric theory (3rd ed.). New York, NY: McGraw-Hill.

15.

Pett

M. A.

Lackey

N. R.

Sullivan

J. J.

(2003). Making sense of factor analysis: The use of factor analysis for instrument development in health care research. Thousand Oaks, CA: SAGE.

16.

Schmidt

W. C.

(1997). World-wide web survey research: Benefits, potential problems and solutions. Behavior Research Methods, Instruments & Computers, 29, 274-279.

17.

Scott

N. W.

Fayers

P. M.

Aaronson

N. K.

Bottomley

de Graeff

Groenvold

. . .Sprangers

M. A.

(2010). Differential item functioning (DIF) analyses of health-related quality of life instruments using logistic regression. Health and Quality of Life Outcomes, 8, 81. doi:10.1186/1477-7525-8-81.

18.

Streiner

D. L.

Norman

G. R.

(2003). Health measurement scales: A practical guide to their development and use (3rd ed.). Oxford, UK: Oxford University Press.

19.

Teresi

J. A.

Fleishman

J. A.

(2007). Differential item functioning and health assessment. Quality of Life Research, 16(Suppl. 1), 33-42.

20.

Terwee

C. B.

Bot

S. D.

deBoer

M. R.

van der Windt

D. A.

Knol

D. L.

Dekker

. . .De Vet

. (2007). Quality criteria were proposed for measurement properties of health status questionnaires. Journal of Clinical Epidemiology, 60(1), 34-42. doi:10.1016/j.jclinepi.2006.03.012.

21.

Yen

. (2002). Examining test-retest reliability: An intra-class correlation approach. Nursing Research, 51(1), 59-62.

22.

Zumbo

B. D.

(1999). A handbook on the theory and methods of differential item functioning (DIF): Logistic regression modeling as a unitary framework for binary and Likert-type (ordinal) item scores. Ottawa, Ontario, Canada: Directorate of Human Resources Research and Evaluation National Defense Headquarters.

23.

Zumbo

B. D.

(2007). Three generations of DIF: Considering where it has been, where it is now and where it is going. Language Assessment Quarterly, 4, 223-233. doi:10.1080/15434300701375832.

Refinement and Psychometric Reevaluation of the Instrument

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