This study examined the factor structure and psychometric properties of the Urban Hassles Index (UHI).
Method:
Exploratory factor analyses (EFAs) were conducted via principal axis factoring extraction method. Confirmatory factor analyses were conducted to evaluate the fit of the EFA-derived model using the weighted least squares estimator with mean and variance adjustments. Composite/scale scores were created for the extracted factors, with a total score derived by summing the scale scores. Criterion-related validity was examined using hierarchical regressions.
Results:
The UHI had four first-order factors (environmental nuisance, safety concerns, drug, and coercion) accounting for 43% of the total variance. Covariance among the first-order factors was explained by a higher second-order factor. Moderate factor correlations and evidence for the criterion-related validity of the subscales and total score indicated multidimensionality of the UHI.
Conclusions:
The shortened 16-item UHI is a brief assessment tool evaluating stressors unique to urban adolescents, providing specific intervention targets.
AchenbachM.DumenciL.RescorlaL. A. (2001). Ratings of relations between DSM-IV diagnostic categories and items. Burlington, VT: University of Vermont Research Center for Children (Youth and Children).
2.
AllisonK. W.CrawfordI.LeoneP. E.TrickettE.Perez-FeblesA.BurtonL. M.Le BlancR. (1999). Adolescent substance use: Preliminary examinations of school and neighborhood context. American Journal of Community Psychology, 27, 111–141.
3.
BarbaranelliC.LeeC. S.VelloneE.RiegelB. (2014). Dimensionality and reliability of the self-care of heart failure index scales: Further evidence from confirmatory factor analysis. Research in Nursing & Health, 37, 524–537. doi:10.1002/nur.21623
4.
BennettM. D.MillerD. B. (2006). An exploratory study of the Urban Hassles Index: A contextually relevant measure of chronic multidimensional urban stressors. Research on Social Work Practice, 16, 305–314. doi:10.1177/1049731505283886
5.
BronfenbrennerU. (1979). The ecology of human development. Cambridge, MA: Harvard University Press.
6.
ChandraA.BatadaA. (2006). Exploring stress and coping among urban African American adolescents: The Shifting the Lens study. Preventing Chronic Disease, 3. Retrieved fromhttp://www.cdc.gov/pcd/issues/2006/apr/05_0174.htm
7.
CheungG. W.RensvoldR. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233–255. doi:10.1207/S15328007SEM0902_5
CostelloA. B.OsborneJ. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research, & Evaluation, 10, 1–9.
10.
CurranP. J.WestS. G.FinchJ. F. (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1, 16–29. doi:10.1037/1082-989X.1.1.16
11.
DeardorffJ.GonzalesN. A.SandlerI. N. (2003). Control beliefs as a mediator of the relation between stress and depressive symptoms among inner-city adolescents. Journal of Abnormal Child Psychology, 31, 205–217. doi:10.1023/a:1022582410183
12.
FabrigarL. R.WegenerD. T.MacCallumR. C.StrahanE. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4, 272–299. doi:10.1037/1082-989X.4.3.272
13.
FloydF. J.WidamanK. F. (1995). Factor analysis in the development and refinement of clinical assessment instruments. Psychological Assessment, 7, 286–299. doi:10.1037/1040-3590.7.3.286
14.
HaytonJ. C.AllenD. G.ScarpelloV. (2004). Factor retention decisions in exploratory factor analysis: A tutorial on parallel analysis. Organizational Research Methods, 7, 191–205. doi:10.1177/1094428104263675
15.
HuL.BentlerP. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55. doi:10.1080/10705519909540118
16.
KannerA. D.CoyneJ. C.SchaeferC.LazarusR. S. (1981). Comparison of two modes of stress measurement: Daily hassles and uplifts versus major life events. Journal of Behavioral Medicine, 4, 1–39.
17.
KoufterosX.BabbarS.KaighobadiM. (2009). A paradigm for examining second-order factor models employing structural equation modeling. International Journal of Production Economics, 120, 633–652. Retrieved fromhttp://dx.doi.org/10.1016/j.ijpe.2009.04.010
18.
LazarusR. S. (1984). Puzzles in the study of daily hassles. Journal of Behavioral Medicine, 7, 375–389.
19.
LittleT. D.LindenbergerU.NesselroadeJ. R. (1999). On selecting indicators for multivariate measurement and modeling with latent variables: When “good” indicators are bad and “bad” indicators are good. Psychological Methods, 4, 192–211. doi:10.1037/1082-989X.4.2.192
20.
LuL. (1991). Daily hassles and mental health: A longitudinal study. British Journal of Psychology, 82, 441–447.
21.
MarcoM.GraciaE.TomásJ. M.López-QuílezA. (2015). Assessing neighborhood disorder: Validation of a three-factor observational scale. The European Journal of Psychology Applied to Legal Context, 7, 81–89. Retrieved fromhttp://dx.doi.org/10.1016/j.ejpal.2015.05.001
22.
MathesonF. I.MoineddinR.DunnJ. R.CreatoreM. I.GozdyraP.GlazierR. H. (2006). Urban neighborhoods, chronic stress, gender and depression. Social Science & Medicine, 63, 2604–2616.
23.
McBride MurryV.BerkelC.Gaylord-HardenN. K.Copeland-LinderN.NationM. (2011). Neighborhood poverty and adolescent development. Journal of Research on Adolescence, 21, 114–128.
24.
MillerD. B.BennettM. D. (2015). Continued development of the Urban Hassles Index: Evolving examination of urban adolescent stressors. Child and Adolescent Social Work Journal, 33, 327–335. doi:10.1007/s10560-015-0430-4
25.
MillerD. B.TownsendA. (2005). Urban hassles as chronic stressors and adolescent mental health: The Urban Hassles Index. Brief Treatment and Crisis Intervention, 5, 85–94. doi:10.1093/brief-treatment/mhi004
26.
MillerD. B.WebsterS. E.MacIntoshR. (2002). What’s there and what’s not: Measuring daily hassles in urban African American adolescents. Research on Social Work Practice, 12, 375–388. doi:10.1177/1049731502012003003
27.
MinM. O.MinnesS.LangA.WeishampelP.ShortE. J.YoonS.SingerL. T. (2014). Externalizing behavior and substance use related problems at 15 years in prenatally cocaine exposed adolescents. Journal of Adolescence, 37, 269–279. doi:10.1016/j.adolescence.2014.01.004
28.
MinnesS.SingerL. T.KirchnerH. L.ShortE.LewisB.SatayathumS.QuehD. (2010). The effects of prenatal cocaine exposure on problem behavior in children 4-10 years. Neurotoxicol Teratol, 32, 443–451. doi:10.1016/j.ntt.2010.03.005
NunnallyJ. C.BernsteinI. H. (1994). Psychometric theory (3rd ed.). New York, NY: McGraw-Hill.
31.
O’connorB. P. (2000). SPSS and SAS programs for determining the number of components using parallel analysis and Velicer’s MAP test. Behavior Research Methods, Instruments, & Computers, 32, 396–402. doi:10.3758/bf03200807
32.
PettM. A.LackeyN. R.SullivanJ. J. (2003). Making sense of factor analysis: The use of factor analysis for instrument development in health care research. Thousand Oaks, CA: Sage.
33.
RahdertE. (1991). The adolescent assessment and referral system manual (DHHS Publication No. ADM 91-1735). Rockville, MD: National Institute on Drug Abuse.
34.
RhemtullaM.Brosseau-LiardP. E.SavaleiV. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological Methods, 17, 354–373. doi:10.1037/a0029315
35.
RowlisonR. T.FelnerR. D. (1988). Major life events, hassles, and adaptation in adolescence: Confounding in the conceptualization and measurement of life stress and adjustment revisited. Journal of Personality and Social Psychology, 55, 432–444.
36.
ScalesP. C.LeffertN. (2004). Developmental assets: A synthesis of the scientific research on adolescent development. Minneapolis, MN: Search Institute.
37.
Schaefer-McDanielN. (2007). “They be doing illegal things”: Early adolescents talk about their inner-city neighborhoods. Journal of Adolescent Research, 22, 413–436. doi:10.1177/0743558407303034
38.
SchneidermanN.IronsonG.SiegelS. D. (2005). Stress and health: Psychological, behavioral, and biological determinants. Annual Review of Clinical Psychology, 1, 607–628. doi:10.1146/annurev.clinpsy.1.102803.144141
39.
SingerL. T.MinnesS.ShortE.ArendtR.FarkasK.LewisB.…KirchnerH. L. (2004). Cognitive outcomes of preschool children with prenatal cocaine exposure. The Journal of the American Medical Association, 291, 2448–2456. doi:10.1001/jama.291.20.2448
40.
SingerL. T.NelsonS.ShortE.MinM. O.LewisB.RussS.MinnesS. (2008). Prenatal cocaine exposure: Drug and environmental effects at 9 years. Journal of Pediatrics, 153, 105–111. doi:10.1016/j.jpeds.2008.01.001
41.
YstgaardM.TambsK.DalgardO. S. (1999). Life stress social support and psychological distress in late adolescence: A longitudinal study. Social Psychiatry and Psychiatric Epidemiology, 34, 12–19.
42.
YuC. Y.MuthénB. (2002). Evaluation of model fit indices for latent variable models with categorical and continuous outcomes. Paper presented at the Annual Meeting of the American Educational Research Association, New Orleans, LA.
