Conducting research on human relationships entails special challenges of design and analysis. Many important questions benefit from the study of dyads and families, and studies of relationships in natural settings often involve longitudinal and/or clustered designs. In turn, power analyses for such studies require additional considerations, because multilevel statistical models (or structural equation modeling equivalents) are often used to analyze relationships data. Power calculations in multilevel models involve the difficult task of specifying hypothesized values for a large number of parameters. Planning studies can also involve power trade-offs, including whether to prioritize the number of dyads sampled or the number of repeated measurements per dyad. Unfortunately, the relationships literature provides limited guidance on how to deal with these issues. In this article, we present a data simulation method for estimating power for commonly used relationships research designs. We also illustrate the method using two worked examples from relationships research.
AhnC.HeoM.ZhangS. (2015). Sample size calculations for clustered and longitudinal outcomes in clinical research. Boca Raton, FL: CRC Press.
2.
BarrD. J.LevyR.ScheepersC.TilyH. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68, 255–278.
3.
BarreraM. (1986). Distinctions between social support concepts, measures, and models. American Journal of Community Psychology, 14, 413–445.
4.
BerkhofJ.SnijdersT. A. B. (2001). Variance component testing in multilevel models. Journal of Educational and Behavioral Statistics, 26, 133–152.
5.
BolgerN.LaurenceauJ-P. (2013). Intensive longitudinal methods: An introduction to diary and experience sampling research. New York, NY: Guilford Press.
6.
BolgerN.StadlerG.LaurenceauJ. P. (2012). Power analysis for intensive longitudinal studies. In MehlM. R.ConnerT. S. (Eds.), Handbook of research methods for studying daily life (pp. 285–301). New York, NY: Guilford Press.
7.
BolgerN.ZuckermanA.KesslerR. C. (2000). Invisible support and adjustment to stress. Journal of Personality and Social Psychology, 79, 953–961.
8.
BoskerR. J.SnijdersT. A. B.GuldemondH. (2007). PinT (Power in two-level designs): Estimating standard errors of regression coefficients in hierarchical linear models for power calculations (Version 2.12). Groningen, The Netherlands: Author.
9.
Castro-SchiloL.GrimmK. J. (this issue). Using residualized change versus difference scores for longitudinal research. Journal of Social and Personal Relationships.
10.
CobbS. (1976). Social support as a moderator of life stress. Psychosomatic Medicine, 38, 300–314.
11.
CohenJ. (1962). The statistical power of abnormal-social psychological research: A review. Journal of Abnormal and Social Psychology, 3, 145–153.
12.
CohenJ. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
13.
CoolsW.Van den NoortgateW.OnghenaP. (2008). ML-DEs: A program for designing efficient multilevel studies. Behavior Research Methods, 40, 236–249.
14.
CurranP. J.BauerD. J. (2011). The disaggregation of within-person and between-person effects in longitudinal models of change. Annual Review of Psychology, 62, 583–619.
15.
FinkelE. J.EastwickP. W.ReisH. T. (2015). Best research practices in psychology: Illustrating epistemological and pragmatic considerations with the case of relationship science. Journal of Personality and Social Psychology, 108, 275–297.
16.
FitzmauriceG. M.LairdN. M.WareJ. H. (2012). Applied longitudinal analysis (Vol. 998). Hoboken, NJ: John Wiley & Sons.
17.
GelmanA.HillJ. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge, England: Cambridge University Press.
18.
HaberM. G.CohenJ. L.LucasT.BaltesB. B. (2007). The relationship between self-reported received and perceived social support: A meta-analytic review. American Journal of Community Psychology, 39, 133–144.
19.
HedekerD.GibbonsR. D.WaternauxC. (1999). Sample size estimation for longitudinal designs with attrition: Comparing time-related contrasts between two groups. Journal of Educational and Behavior Statistics, 24, 70–93.
20.
HouseJ. S.LandisK. R.UmbersonD. (1988). Social relationships and health. Science, 241, 540–545.
21.
HoxJ. J. (2010). Multilevel analysis: Techniques and applications (2nd ed.). New York, NY: Routledge.
22.
IBM, Inc. (2013). IBM SPSS Statistics (Version 22). Armonk, NY: Author.
23.
IidaM.SeidmanShroutP. E. (this issue). Models of interdependent individuals and dyadic process in relationship research. Journal of Social and Personal Relationships.
24.
KashyD. A.KennyD. A. (1999). The analysis of data from dyads and groups. In ReisH. T.JuddC. M. (Eds.), Handbook of research methods in social psychology. New York, NY: Cambridge University Press.
25.
KelleyK.RauschJ. R. (2011). Sample size planning for longitudinal models: Accuracy in parameter estimation for polynomial change parameters. Psychological Methods, 16, 391–405.
26.
KennyD. A. (1996). Models of nonindependence in dyadic research. Journal of Social and Personal Relationships, 13, 279–294.
27.
KennyD. A.KashyD. A.CookW. L. (2006). Dyadic data analysis. New York, NY: Guilford Press.
28.
KesslerR. C.McLeodJ. D. (1985). Social support and mental health in community samples. In CohenS.SymeS. L. (Eds.), Social support and health (pp. 219–240). San Diego, CA: Academic Press.
29.
LaRoccoJ. M.HouseJ. S.FrenchJ. R. P.Jr. (1980). Social support, occupational stress, and health. Journal of Health and Social Behavior, 21, 202–218.
30.
LaurenceauJ. P.BolgerN. (2005). Using diary methods to study marital and family processes. Journal of Family Psychology, 19, 86–97.
31.
LeavyR. L. (1983). Social support and psychological disorder: A review. Journal of Community Psychology, 11, 3–21.
32.
LiuG.LiangK.-Y. (1997). Sample size calculations for studies with correlated observations. Biometrics, 53, 937–947.
33.
MaxwellS. E.DelaneyH. D. (2004). Designing experiments and analyzing data: A model comparison perspective (2nd ed.). Mahwah, NJ: Erlbaum.
34.
MoerbeekM.TeerenstraS. (2016). Power analysis of trials with multilevel data. Boca Raton, FL: CRC Press.
35.
MoerbeekM.Van BreukelenG. J.BergerM. P. (2008). Optimal designs for multilevel studies. In de LeeuwJ.MeijerE. (Eds.), Handbook of multilevel analysis (pp. 177–205). New York, NY: Springer.
36.
MuthénL. K.MuthénB. O. (1998–2012). Mplus user’s guide (7th ed). Los Angeles, CA: Author.
37.
PaxtonP.CurranP. J.BollenK. A.KirbyJ.ChenF. (2001). Monte Carlo experiments: Design and implementation. Structural Equation Modeling, 8, 287–312.
38.
R Development Core Team. (2015). R: A language and environment for statistical computing (Version 3.2.3). Vienna, Austria: R Foundation for Statistical Computing.
39.
RaudenbushS. W.BrennanR. T.BarnettR. C. (1995). A multivariate hierarchical model for studying psychological change within married couples. Journal of Family Psychology, 9, 161–174.
40.
RaudenbushS. W.BrykA. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Newbury Park, CA: Sage.
RastP.HoferS. M. (2014). Longitudinal design considerations to optimize power to detect variances and covariances among rates of change: Simulation results based on actual longitudinal studies. Psychological Methods, 19, 133–154.
43.
RogersK.WoodD.FurrR. M. (this issue). Assessment of similarity and self-other agreement in dyadic relationships: A guide to best practices. Journal of Social and Personal Relationships.
44.
SAS Institute, Inc. (2013). SAS 9.4. Cary, NC: Author.
45.
ShroutP.E.BolgerN.IidaM.BurkeC.GleasonM.E.J.LaneS.P. (2010). The mixed effects of daily support transactions during acute stress: Results from a diary study of bar exam preparation. In SullivanK.DavilaJ. (Eds.), Support processes in intimate relationships (pp. 175–199). New York, NY: Oxford University Press.
46.
ShroutP. E.HermanC. M.BolgerN. (2006). The costs and benefits of practical and emotional support on adjustment: A daily diary study of couples experiencing acute stress. Personal Relationships, 13, 115–134.
47.
SnijdersT. A. (2005). Power and sample size in multilevel modeling. In EverittB. S.HowellD. C. (Eds.), Encyclopedia of statistics in behavioral science (Vol. 3, pp. 1570–1573). Chichester, UK: Wiley.
48.
SnijdersT. A. B.BoskerR. J. (1993). Standard errors and sample sizes for two-level research. Journal of Educational Statistics, 18, 237–259.
49.
SnijdersT. A. B.BoskerR. J. (1999). Multilevel analysis: An introduction to basic and advanced multilevel modeling. London, England: Sage.
50.
SternC.WestT. V. (this issue). Assessing accuracy in close relationships research: A truth and bias approach. Journal of Social and Personal Relationships.
51.
TaylorS. E. (2007). Social support. In FriedmanH. S.SilverR. C. (Eds.), Foundations of health psychology (pp. 145–171). New York, NY: Oxford University Press.
52.
ZhangZ.WangL. (2009). Statistical power analysis for growth curve models using SAS. Behavior Research Methods, 41, 1083–1094.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
