Person-oriented research proceeds from the assumption that individuals differ to the extent that statements that are based on the aggregation of raw scores rarely capture all individuals, if ever. To justify the effort needed for person-oriented research, it needs to be shown where and how individuals differ. Conversely, when aggregate-level statements are intended, it needs to be shown that groups are homogeneous. In this article, it is suggested that ‘to be different’ is specific to the method of analysis used. Within the context of person-oriented research, intra- and interindividual differences can be searched for in the domains of magnitude of scores, structure of scores, causal relations and direction dependence, frequencies of occurrences of patterns of behavior, as well as distances between individuals and groups of individuals. All this can be performed in both, cross-sectional and longitudinal research. The requirements are discussed that data sets must meet for person-oriented research. It is also shown that differences in one domain neither imply nor preclude differences in other domains. Real-world data examples are given that illustrate the nature of differences with respect to each tenet of person-oriented research.
BergmanL. R. (2001). A person approach to adolescence: Some methodological challenges. Journal of Adolescent Research, 16(1), 28–53. https://doi.org/10.1177/0743558401161004
5.
BergmanL. R.MagnussonD. (1997). A person-oriented approach in research on developmental psychopathology. Development and Psychopathology, 9(2), 291–319. https://doi.org/10.1017/s095457949700206x
6.
BergmanL. R.MagnussonD.El-KhouriB. M. (2003). Studying individual development in interindividual context. Lawrence Erlbaum.
7.
BorsboomD.HaslbeckJ. (2024). Integrating intra- and interindividual phenomena in psychological theories. Multivariate Behavioral Research, 59(6), 1–20. https://doi.org/10.1080/00273171.2024.2336178
8.
BreimanL.FriedmanJ. H.OlshenR. A.StoneC. J. (1984). Classification and regression trees. Chapman & Hall/CRC.
9.
CainM. K.ZhangZ.YuanK.-H. (2017). Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation. Behavior Research Methods, 49(5), 1716–1735. https://doi.org/10.3758/s13428-016-0814-1
CostelloA.OsborneJ. (2005). Exploratory Factor Analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research and Evaluation, 10(7), 1–9.
12.
De BinR.JanitzaS.SauerbreiW.BoulesteixA. L. (2016). Subsampling versus bootstrapping in resampling-based model selection for multivariable regression. Biometrics, 72(1), 272–280. https://doi.org/10.1111/biom.12381
13.
DerogatisL. R.LipmanR. S.RieklesI. C.UhlenhuthE. H.CoviL. (1974). The Hopkins Symptom Checklist (HSCL). In PichotP. (Ed.), Psychological measurements in psychopharmacology: Modem problems in Pharmacopsychiatry (Vol. 7, pp. 79–110). Karger.
14.
EstesW. K. (1956). The problem of inference from curves based on group data. Psychological Bulletin, 53(2), 134–140. https://doi.org/10.1037/h0045156
15.
EstesW. K.MaddoxW. T. (2005). Risks of drawing inferences about cognitive processes from model fits to individual versus average performance. Psychonomic Bulletin & Review, 12(3), 403–408. https://doi.org/10.3758/BF03193784
16.
FinkelsteinJ. W.von EyeA.PreeceM. A. (1994). The relationship between aggressive behavior and puberty in normal adolescents: A longitudinal study. Journal of Adolescent Health, 15(4), 319–326. https://doi.org/10.1016/1054-139x(94)90605-x
17.
GatesK.MolenaarP. C. M.ChowS.-M. (2023). Analysis of intra-individual variation: Systems approaches to human process analysis. CRC Press/Taylor & Francis.
18.
GeigerP.ZhangK.GongM.JanzingD.SchölkopfB. (2015). Causal inference by identification of vector autoregressive processes with hidden components. Proceedings of 32th International Conference on Machine Learning, 37(1), 1917–1925.
19.
GendollaG. H. (2000). On the impact of mood on behavior: An integrative theory and a review. Review of General Psychology, 4(4), 378–408. https://doi.org/10.1037/1089-2680.4.4.378
20.
GrangerC. W. J. (1981). Some properties of time series data and their use in econometric model specification. Journal of Econometrics, 33(1), 121–130. https://doi.org/10.1016/0304-4076(81)90079-8
21.
GreenacreM.Öztaş AyhanH. (2015). Identifying inliers. Barcelona School of Economics. Working paper 763.
22.
GrettonA.FukumizuK.TeoC. H.SongL.SchölkopfB.SmolaA. J. (2008). A kernel statistical test of independence. In Advances in Neural Information Processing Systems (Vol. 20, pp. 585–592). MIT Press.
23.
HamakerE. L.NesselroadeJ. R.MolenaarP. C. M. (2007). The integrated trait-state model. Journal of Research in Personality, 41(2), 295–315. https://doi.org/10.1016/j.jrp.2006.04.003
24.
HarrisK. M.UdryJ. R. (2022). National longitudinal study of adolescent to adult health (Add Health), 1994-2018. Carolina Population Center. University of North Carolina-Chapel Hill, Inter-university Consortium for Political and Social Research. https://doi.org/10.3886/ICPSR21600.v25
25.
HettmanspergerT. P.SheatherS. J. (1992). A cautionary note on the method of least median squares. The American Statistician, 46(2), 79–83. https://doi.org/10.1080/00031305.1992.10475855
26.
HothornT.HornikK.ZeileisA. (2006). Unbiased recursive partitioning: A conditional inference framework. Journal of Computational & Graphical Statistics, 15(3), 651–674. https://doi.org/10.1198/106186006x133933
27.
HyvärinenA.ZhangK.ShimizuS.HoyerP. O. (2010). Estimation of a structural vector autoregression model using non-gaussianity. Journal of Machine Learning Research, 11(5), 1709–1731.
28.
JennrichR. I. (1979). Admissible values of !g in direct oblimin rotation. Psychometrika, 44(2), 173–177. https://doi.org/10.1007/BF02293969
29.
KaufmanL.RousseeuwP. J. (2005). Finding groups in data: An introduction to cluster analysis. Wiley & Sons.
30.
KidwellK. M.AlmirallD. (2023). Sequential, multiple assignment, randomized trial designs. Journal of the American Medical Association, 329(4), 336–337. https://doi.org/10.1001/jama.2022.24324
31.
LeboM. A.NesselroadeJ. R. (1978). Intraindividual differences dimensions of mood change during pregnancy identified in five P-technique factor analyses. Journal of Research in Personality, 12(2), 205–224. https://doi.org/10.1016/0092-6566(78)90098-3
32.
LeiH.Nahum-ShaniI.LynchK.OslinD.MurphyS. A. (2012). A “SMART” design for building individualized treatment sequences. Annual Review of Clinical Psychology, 8(1), 21–48. https://doi.org/10.1146/annurev-clinpsy-032511-143152
33.
LienertG. A. (1968). Die “Konfigurationsfrequenzanalyse” als Klassifikationsmethode in der klinischen Psychologie. Vortrag auf dem 26. Kongress der Deutschen Gesellschaft für Psychologie in Tübingen.
34.
LienertG. A.KrauthJ. (1973). Die Konfigurationsfrequenzanalyse als Prädiktionsmodell in der angewandten Psychologie. In EckensbergerH. (Ed.), Bericht über den 28. Kongreß der deutschen Gesellschaft für Psychologie in Saarbrücken 1972 (pp. 219–228). Hogrefe.
35.
LienertG. A.KrauthJ. (1975). Configural Frequency Analysis as a statistical tool for defining types. Educational and Psychological Measurement, 35(2), 231–238. https://doi.org/10.1177/001316447503500201
36.
LienertG. A.RaatzU. (1994). Testaufbau und Testanalyse (5th ed.). Beltz.
37.
LobatoO. C.MartínezJ. M.MirandaV. S.RiveraM. H.SerratoH. (2007). Diseño de la evaluación del Índice Mexicano de Satisfacción del Usario del programa de abasto social de leche y del programa de estancias y garderías infantiles de la SEDESOL: Universidad Iberoamericana. unpublished project report.
38.
LobatoO. C.RiveraM. H.GonzálezM. H. S.GómezA. M. E.LeónC.CervantesP. (2011). Índice Mexicano de Satisfacción del Usuario (IMSU). Universidad Iberoamericana. unpublished project report.
39.
LohW.-Y. (2009). Improving the precision of classification trees. Annals of Applied Statistics, 3(4), 1710–1737. https://doi.org/10.1214/09-aoas260
40.
LohW.-Y. (2014). Fifty years of classification and regression trees. International Statistical Review, 82(3), 329–348. https://doi.org/10.1111/insr.12016
41.
LordF. M.NovickM. R. (1968). Statistical theories of mental test scores. Addison-Wesley.
42.
MessengerR.MandellL. (1972). A modal search technique for predictive nominal scale multivariate analysis. Journal of the American Statistical Association, 67(340), 768–772. https://doi.org/10.1080/01621459.1972.10481290
MolenaarP. C.NesselroadeJ. R. (2009). The recoverability of P-technique factor analysis. Multivariate Behavioral Research, 44(1), 130–141. https://doi.org/10.1080/00273170802620204
45.
MolenaarP. C. M. (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology, this time forever. Measurement: Interdisciplinary Research & Perspective, 2(4), 201–218. https://doi.org/10.1207/s15366359mea0204_1
46.
MolenaarP. C. M. (2010). Testing all six person-oriented principles in dynamic factor analysis. Development and Psychopathology, 22(2), 255–259. https://doi.org/10.1017/S0954579410000027
47.
MolenbroekJ. (2022). The average man does not exist. International Journal of Environmental Research and Public Health, 19(4), 2094. https://doi.org/10.3390/ijerph19042094
48.
MorganJ. N.SonquistJ. A. (1963). Problems in the analysis of survey data, and a proposal. Journal of the American Statistical Association, 58(302), 415–434. https://doi.org/10.2307/2283276
49.
MunE.-Y.BatesM. E.VaschilloE. (2010). Closing the gap between person-oriented theory and methods. Development and Psychopathology, 22(2), 261–271. https://doi.org/10.1017/S0954579410000039
50.
NesselroadeJ. R. (1991). Interindividual differences in intraindividual change. In CollinsL. M.HornJ. L. (Eds.), Best methods for the analysis of change: Recent advances, unanswered questions, future directions (pp. 92–105). American Psychological Association. https://doi.org/10.1037/10099-006
51.
NesselroadeJ. R.SalthouseT. A. (2004). Methodological and theoretical implications of intraindividual variability in perceptual-motor performance. The Journals of Gerontology: Serie Bibliographique, 59(2), 49–55. https://doi.org/10.1093/geronb/59.2.P49
52.
PerrineM. W.MundtJ. C.SearlesJ. S.LesterL. S. (1995). Validation of daily selfreport consumption using interactive voice response (IVR) technology. Journal of Studies on Alcohol and Drugs, 56(5), 487–490. https://doi.org/10.15288/jsa.1995.56.487
53.
PhilippM.RuschT.HornikK.StroblC. (2018). Measuring the stability of results from supervised statistical learning. Journal of Computational & Graphical Statistics, 27(4), 685–700. https://doi.org/10.1080/10618600.2018.1473779
54.
PrimackB. A.SwanierB.GeorgiopoulosA. M.LandS. R.FineM. J. (2009). Association between media use in adolescence and depression in young adulthood: A longitudinal study. Archives of General Psychiatry, 66(2), 181–188. https://doi.org/10.1001/archgenpsychiatry.2008.532
55.
RadloffL. S. (1977). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385–401. https://doi.org/10.1177/014662167700100306
56.
RamN.GerstorfD. (2009). Time-structured and net intraindividual variability: Tools for examining the development of dynamic characteristics and processes. Psychology and Aging, 24(4), 778–791. https://doi.org/10.1037/a0017915
57.
RogersC. (1951). Client-centered therapy: Its current practice, implications and theory. Constable.
SpearmanC. (1907). Demonstration of formulae for true measurement of correlation. American Journal of Psychology, 18(2), 160–169. https://doi.org/10.2307/1412408
60.
SterbaS. K.BauerD. J. (2010). Matching method with theory in person-oriented developmental psychopathology research. Development and Psychopathology, 22(2), 239–254. https://doi.org/10.1017/S0954579410000015
61.
StroblC.MalleyJ.TutzG. (2009). An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychological Methods, 14(4), 323–348. https://doi.org/10.1037/a0016973
62.
SubramanianS. V.KimR.ChristakisN. A. (2018). The “average” treatment effect: A construct ripe for retirement. A commentary on Deaton and Cartwright. Social Science & Medicine, 210(1), 77–82. https://doi.org/10.1016/j.socscimed.2018.04.027
63.
TamhaneA. C.DunlopD. (2000). Statistics and data analysis from elementary to intermediate. Prentice Hall.
64.
VaughanA. C.BirneyD. P. (2023). Within-individual variation in cognitive performance is not noise: Why and how cognitive assessments should examine within-person performance. Journal of Intelligence, 11(6), 110. https://doi.org/10.3390/jintelligence11060110
65.
VinokurA. D.PriceR. H.SchulY. (1995). Impact of the JOBS intervention on unemployed workers varying in risk for depression. American Journal of Community Psychology, 23(1), 39–74. https://doi.org/10.1007/BF02506922
66.
VinokurA. D.SchulY. (1997). Mastery and inoculation against setbacks as active ingredients in the JOBS intervention for the unemployed. Journal of Consulting and Clinical Psychology, 65(5), 867–877. https://doi.org/10.1037//0022-006x.65.5.867
67.
von EyeA. (2010). The many parameters that can change. ISSBD Bulletin, 1/57, 4–7.
68.
von EyeA.BergmanL. R. (2003). Research strategies in developmental psychopathology: Dimensional identity and the person-oriented approach. Development and Psychopathology, 15(3), 553–580. https://doi.org/10.1017/s0954579403000294
69.
von EyeA.BergmanL. R.HsiehC.-A. (2015). Person-oriented methodological approaches. In OvertonW. F.MolenaarP. C. M. (Eds.), Handbook of child psychology and developmental science - theory and methods (pp. 789–841). Wiley.
70.
von EyeA.Gutiérrez PeñaE. (2004). Configural Frequency Analysis - the search for extreme cells. Journal of Applied Statistics, 31(8), 981–997. https://doi.org/10.1080/0266476042000270545
71.
von EyeA.Gutiérrez-PeñaE. (2005). Configural frequency analysis of large sparse cross-classifications. Psychology Science, 47(1), 356–376.
72.
von EyeA.MairP. (2008a). A functional approach to configural frequency analysis. Austrian Journal of Statistics, 37(2), 161–173. https://doi.org/10.17713/ajs.v37i2.297
73.
von EyeA.MairP. (2008b). Functional configural frequency analysis: Explaining types and antitypes. Bulletin de la Société des Sciences Médicales, Luxembourg, 144(1), 35–52. https://doi.org/10.17713/ajs.v37i2.297
74.
von EyeA.MunE.-Y. (2012). Interindividual differences in intraindividual change in categorical variables. Psychological Test and Assessment Modeling, 54(2), 151–167. https://doi.org/10.1002/wics.203.
75.
von EyeA.MunE.-Y. (2013). Differential developmental pathways - Configural moderator models. European Journal of Developmental Psychology, 10(1), 60–80. https://doi.org/10.1080/17405629.2012.732720
76.
von EyeA.WiedermannW. (2015). Manifest variable Granger causality models for developmental research: A taxonomy. Applied Developmental Science, 19(4), 183–195. https://doi.org/10.1080/10888691.2014.1001512
77.
von EyeA.WiedermannW. (2021). Configural frequency analysis. Foundations, models, and applications. Springer.
78.
von EyeA.WiedermannW. (2023). The general linear model: A primer. Cambridge University Press.
79.
von EyeA.WiedermannW. (2024a). The individual as moderator of variable relations – A configural approach. Journal for Person-Oriented Research, 10(1), 16–25. https://doi.org/10.17505/jpor.2024.xxxxx
80.
von EyeA.WiedermannW. (2024b). Moving from statistical to hypothesis outliers. Integrative Psychological and Behavioral Science. (in press).
81.
von EyeA.WiedermannW. (2025). Person-oriented research. (in preparation).
82.
WiedermannW.HermanK. C.ReinkeW.von EyeA. (2022). Configural frequency trees. Development and Psychopathology, 34(4), 1585–1603. https://doi.org/10.1017/S0954579421000018
83.
WiedermannW.LiX.von EyeA. (2019). Testing the causal direction of mediation effects in randomized intervention studies. Prevention Science: The Official Journal of the Society for Prevention Research, 20(3), 419–430. https://doi.org/10.1007/s11121-018-0900-y
84.
WiedermannW.ReinkeW. M.HermanK. C. (2025). Distributional moderation analysis: Unpacking moderation effects in intervention research. Journal of School Psychology, 108(1), Article 101399. https://doi.org/10.1016/j.jsp.2024.101399
85.
WiedermannW.von EyeA. (2016). Directional dependence in the analysis of single subjects. Journal for Person-Oriented Research, 2(1-2), 20–33. https://doi.org/10.17505/jpor.2016.04
86.
WiedermannW.von EyeA. (Eds.), (2016). Statistics and causality: Methods for applied empirical research. Wiley.
87.
WiedermannW.von EyeA. (2025). Direction dependence analysis. Foundations and statistical methods. Cambridge University Press. (in press).
88.
WiedermannW.ZhangB.ReinkeW.HermanK. C.von EyeA. (2022). Distributional causal effects: Beyond an “averagarian” view of intervention effects. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000533
89.
WiedermannW.ZhangB.ShiD. (2024). Detecting heterogeneity in the causal direction of dependence: A model-based recursive partitioning approach. Behavior Research Methods, 56(4), 2711–2730. https://doi.org/10.3758/s13428-023-02253-8
90.
WolfE.HarringtonK.ClarkS.MillerM. (2013). Sample size requirements for structural equation models: An evaluation of power, bias, and solution propriety. Educational and Psychological Measurement, 76(6), 913–934. https://doi.org/10.1177/0013164413495237
91.
YoungR. M.OeiT. P.KnightR. G. (1990). The tension reduction hypothesis revisited: An alcohol expectancy perspective. British Journal of Addiction, 85(1), 31–40. https://doi.org/10.1111/j.1360-0443.1990.tb00621.x
92.
ZeileisA.HothornT.HornikK. (2008). Model-based recursive partitioning. Journal of Computational and Graphical Statistics, 17(2), 492–514. https://doi.org/10.1198/106186008X319331
93.
ZhangG.BrowneM. W.OngA. D.ChowS. M. (2014). Analytic standard errors for exploratory process factor analysis. Psychometrika, 79(1), 444–469. https://doi.org/10.1007/s11336-013-9365-x
