The aim of this study was to examine the psychometric properties of the job embeddedness instrument (JEI) using a Rasch perspective in a sample of Registered Nurses (RNs). A secondary analysis of data was conducted from a previous study examining the job embeddedness of rural and urban RNs. A Rasch analysis supported the six underlying dimensions: organizational fit, community fit, organizational links, community links, organizational sacrifice, and community sacrifice. The results of this study also demonstrate additional evidence of the validity, reliability, and generalizability of the JEI inferences with a sample of RNs. In total, 38 of 39 items of the original JEI were retained in the model. The psychometric evaluation attained through this multidimensional Rasch analysis provided support for using the JEI to assess the level of job embeddedness for RNs.
AikenL.CimiottiJ.SloaneD.SmithH.FlynnL.NeffD. F. (2011). Effects of nurse staffing and nurse education on patient deaths in hospitals with different nurse work environments. Medical Care, 49, 1047-1053. doi:10.1097/MLR.0b013e3182330b6e
2.
AkaikeH. (1981). Likelihood of models and information criteria. Journal of Econometrics, 16(1), 3-14.
3.
American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (1985). Standards for educational and psychological testing. Washington, DC: American Psychological Association.
4.
AuerbachD. I.BuerhausP. I.StaigerD. O. (2011). Registered nurse supply grows faster than projected amid surge in new entrants ages 23-26. Health Affairs, 30, 2286-2292. doi:10.1377/hlthaff.2011.0588
5.
AuerbachD. I.StaigerD. O.MuenchU.BuerhausP. I. (2013). The nursing workforce in an era of health care reform. New England Journal of Medicine, 368, 1470-1472. doi:10.1056/NEJMp1301694
6.
BondT. G.FoxC. M. (2007). Applying the Rasch model: Fundamental measurement in the human sciences (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.
7.
BooneW. J.TownsendJ. S.StaverJ. (2011). Using Rasch theory to guide the practice of survey development and survey data analysis in science education and to inform science reform efforts: An exemplar utilizing STEBI self-efficacy data. Science Education, 95, 258-280.
8.
BozdoganH. (1987). Model selection and Akaike’s Information Criterion (AIC): The general theory and its analytical extensions. Psychometrika, 52, 345-370. doi:10.1007/BF02294361
9.
BurnhamK. P.AndersonD. R. (2002). Model selection and multimodal inference: A practical information-theoretic approach (2nd ed.). New York, NY: Springer-Verlag.
10.
CrossleyC. D.BennettR. J.JexS. M.BurnfieldJ. L. (2007). Development of a global measure of job embeddedness and integration into a traditional model of voluntary turnover. Journal of Applied Psychology, 92, 1031-1042.
11.
CunninghamG. B.FinkJ. S.SagasM. (2005). Extensions and further examination of the job embeddedness construct. Journal of Sport Management, 19, 319-335.
12.
DineroT. E.HaertelE. (1977). Applicability of the Rasch model with varying item discriminations. Applied Psychological Measurement, 1, 581-592.
13.
DuffieldC. M.RocheM. A.HomerC.BuchanJ.DimitrelisS. (2014). A comparative review of nurse turnover rates and costs across countries. Journal of Advanced Nursing, 70, 2703-2712. doi:10.1111/jan.12483
14.
EwingM. T.SalzbergerT.SinkovicsR. R. (2005). An alternate approach to assessing cross-cultural measurement equivalence in advertising research. Journal of Advertising, 34(1), 17-36.
15.
FelpsW.MitchellT. R.HekmanD. R.LeeT. W.HoltomB. C.HarmanW. S. (2009). Turnover contagion: How coworkers’ job embeddedness and job search behaviors influence quitting. Academy of Management Journal, 52, 545-561. doi:10.5465/AMJ.2009.41331075
16.
FlanneryK.ResnickB.GalikE.LipscombJ.McPhaulK. (2012). Reliability and validity assessment of the Job Attitude Scale. Geriatric Nursing, 33, 465-472. doi:10.1111/j.1365-2702.2012.04137.x
17.
González-de PazL.KostovB.Sisó-AlmirallA.Zabalegui-YárnozA. (2012). A Rasch analysis of nurses’ ethical sensitivity to the norms of the code of conduct. Journal of Clinical Nursing, 21, 2747-2760. doi:10.1111/j.1365-2702.2012.04137.x
18.
HambletonR. K.JonesR. W. (1993). Comparison of classical test theory and item response theory and their applications to test development. Educational Measurement: Issues & Practice, 12, 38-47. doi:10.1111/j.1745-3992.1993.tb00543.x
19.
HeJ.StaggsV. S.Bergquist-BeringerS.DuntonN. (2016). Nurse staffing and patient outcomes: A longitudinal study on trend and seasonality. BMC Nursing, 15, Article 60.
20.
HinshawA. S.SmeltzerC. H.AtwoodJ. R. (1987). Innovative retention strategies for nursing staff. The Journal of Nursing Administration, 17(6), 8-16.
21.
HoltomB. C.O’NeillB. (2004). Job embeddedness: A theoretical foundation for developing a comprehensive nurse retention plan. Journal of Nursing Administration, 34, 216-227.
22.
JonesC. B. (2004). The costs of nurse turnover, part 1: An economic perspective. The Journal of Nursing Administration, 34, 562-570.
23.
JonesC. B. (2005). The costs of nurse turnover, part 2: Application of the nursing turnover cost calculation methodology. The Journal of Nursing Administration, 35, 41-49.
24.
KassR. E.RafteryA. E. (1995). Bayes factors. Journal of the American Statistical Association, 90(430), 773-795.
25.
LeeT. W.BurchT. C.MitchellT. R. (2014). The story of why we stay: A review of job embeddedness. Annual Review of Organizational Psychology and Organizational Behavior, 1, 199-216. doi:10.1146/annurev-orgpsych-031413-091244
26.
LeeT. W.MitchellT. R.SablynskiC. J.BurtonJ. P.HoltomB. C. (2004). The effects of Job Embeddedness on organizational citizenship, job performance, volitional absences, and voluntary turnover. Academy of Management Journal, 47, 711-722. doi:10.2307/20159613
27.
LordF. M. (1983). Small N justifies Rasch model. In WeissD. J. (Ed.), New horizons in testing: Latent trait test theory and computerized adaptive testing (pp. 51-61). New York, NY: Academic Press.
28.
MaertzC. P.Jr.CampionM. A. (2004). Profiles in quitting: Integrating process and content turnover theory. Academy of Management Journal, 47, 566-582. doi:10.2307/20159602
29.
MerbitzC.MorrisJ.GripJ. (1989). Ordinal scales and foundations of misinference. Archives of Physical Medicine & Rehabilitation, 70, 308-312.
30.
MessickS. (1989). Validity. In LinnR. L. (Ed.), Educational measurement (3rd ed., pp. 13-103). New York, NY: MacMillan.
31.
MitchellT. R.HoltomB. C.LeeT. W.SablynskiC. J.ErezM. (2001). Why people stay: Using job embeddedness to predict voluntary turnover. Academy of Management Journal, 44, 1102-1121. doi:10.2307/3069391
32.
National Council State Board of Nursing. (2013). The National Council State Board of Nursing and forum of state nurse workforce centers 2013 National Workforce Survey of RNs. Journal of Nursing Regulation, 4(supplement), S1-S72.
33.
NunnallyJ. C. (1978). Psychometric theory. New York, NY: McGraw-Hill.
34.
Patient Protection and Affordable Care Act, 42 U.S.C. § 18001 (2010).
35.
PriceJ. L.MuellerC. W. (1981). A causal model of turnover for nurses. Academy of Management Journal, 24, 543-565. doi:10.2307/255574
36.
RafteryA. E. (1995). Bayesian model selection in social research. Sociological Methodology, 25, 111-163.
37.
ReitzO. E.AndersonM. A.HillP. D. (2010). Job embeddedness and nurse retention. Nursing Administration Quarterly, 34, 190-200. doi:10.1097/NAQ.0b013e3181e702b7
38.
SalzbergerT.SinkovicsR. R.SchlegelmilchB. B. (1999). Data equivalence in cross-cultural research: A comparison of classical test theory and latent approaches. Australian Marketing Journal, 7(2), 23-38.
39.
SchwarzG. (1978). Estimating the dimensions of a model. The Annals of Statistics, 6, 461-464.
40.
SmithE. V.Jr. (2001). Evidence for the reliability of measures and validity of measure interpretation: A Rasch measurement perspective. Journal of Applied Measurement, 2, 281-311.
41.
SmithE. V.JrConradK.ChangK.PiazzaJ. (2002). An introduction to Rasch measurement for scale development and person assessment. Journal of Nursing Measurement, 10, 189-206.
42.
van de VijverF. J. (1986). The robustness of Rasch estimates. Applied Psychological Measurement, 10(1), 45-57. doi:10.1177/014662168601000104
43.
WolfeE. W.SmithE. V.Jr. (2007a). Instrument development tools and activities for measure validation using Rasch models: Part I—Instrument development tools. Journal of Applied Measurement, 8, 97-123.
44.
WolfeE. W.SmithE. V.Jr. (2007b). Instrument development tools and activities for measure validation using Rasch models: Part II—Validation activities. Journal of Applied Measurement, 8, 204-234.
45.
WrightB. D. (1977). Solving measurement problems with the Rasch model. Journal of Educational Measurement, 14, 97-116.
46.
WrightB. D.LinacreJ. M. (1989). Observations are always ordinal; measurements, however, must be interval. Archives of Physical Medicine and Rehabilitation, 70, 857-860.
