The philosophy and the idea of the person-oriented analysis

Jari-Erik NurmiDepartment of Psychology

University of Jyväskylä

Background: a variable-orientedresearch

• Since early 20th century (1905 or so), quantitavebehavioral sciences (psychology, sociology, etc.) have used ”variable-oriented methods” in analyzing data:– Measurement of phenomena by operationalizing

them as (usually) continuous variables– Examining research questions as statistical

associations between variables (correlations, betacoefficients, exploratory factor analyses, etc.)

– More recently using more sophisticated analysis likeSEM and LGM

– Analysis have focused on inter-individual variance and covariance between variables

Background: a variable-orientedresearch

• More recently the analysis of inter-individualvariance have been complemented by– Research on intra-individual variance– And research on nested environments

But something has been missing: the person-oriented approach

A holistic theory of personality byDavid Magnusson

• Magnusson started by using a variable-orientedpsychometrics

• At certain point he begin to rethink the researchon personality and human development

• ”a holistic theory of personality”– Personality does not consist of (orthogonal) traits but

rather a unique constellation/ combination of traits– In order to study such unique constellations one

needs a new methodological framework a person-oriented approach

Lars Bergman, David Magnusson and Bassam El’Khouri: A person-oriented

analysis• The aim is to identify unique combinations of

individual characteristics• This can be done by using many statistical

methods: creating categorical variables, clusteranalysis, mixture modeling, etc.

• You can do this either by using– Cross-sectional data or– Longitudinal data or– Even diary data

Lars Bergman, David Magnusson and El’Khouri: A person-oriented analysis

• Key idea: – Identify groups of individuals who are similar in the

constellation of variable values– But who differ from other groups of individuals in

these constellations• Benefits of using the approach:

– Identify typical groups with different pattern of chracteristics

– Identify the percentage of people showing thesepatterns

– Identify developmental trajectories of changingpatterns over time

Many ways to use the person-orientedapproach and many statistical

methods available

1. Cross-sectional research that can beinteresting

• Just when you are interested in different patterns/ constellations at certain time-point

X1X2X3X4X5

Cluster 1

Cluster 2

Cluster 3

2a. Clustering in longitudinal data using different clustering criteria over

time• How do patterns of some individual characteristics

change over time• How do people change from one pattern to another

Cluster 1

Cluster 2

Cluster 3

Cluster 1

Cluster 2

Cluster 3Cluster 4

time1 time2

Odd ratios

2b. Clustering longitudinal data usingidentical criteria in different time

points: ISOA procedure• The benefit that clusters are formed on the

basis of identical criteria– The concept of stability (of cluster membership)

becomes clear– The concept of change becomes clear– The role of other predictors become clear

• Should be preceded by 2a type of analysis

The trick is simple but clevert1 t2

S1 y11 y21…S2 y11 y21…S3 y11 y21…S4 y11 y21…S5 y11 y21…

S1 y12 y22…S2 y12 y22…S3 y12 y22…S4 y12 y22…S5 y12 y22…

S1 y1 y2…S2 y1 y2…S3 y1 y2…S4 y1 y2…S5 y1 y2…

Conduct clusteringand save cluster membership

Rearrangedata

S1 y11 y21CL2…S2 y11 y21CL1…S3 y11 y21CL2…S4 y11 y21CL3…S5 y11 y21CL2…

S1 y12 y22CL2…S2 y12 y22CL1…S3 y12 y22CL3…S4 y12 y22CL3…S5 y12 y22CL2…

RearrangeData again includingcluster membership

Now you can study …

• Stability: Frequency table and observed vs. expected frequencies

• Predictors: Multinomial regression

Study example

JEPS-study

• Data 1lk-4lk (kevät)• Measures

- Motivation- Achievement beliefs: e.g. failure expectations

- Task-avoidance

ISOA clustering by cases

-2,5

-2,0

-1,5

-1,0

-0,5

0,0

0,5

1,0

1,5

2,0

2,5

failure-expectation low-motivation autonomic adaptive-social

task_avoidanceefficacymotivationfailure_expectationsocial_supportreading_motivationmath_motivation

n = 31(17.4 %)n = 26

(13.3 %)n = 22

(11.2 %)

n = 35(19.7 %)

n = 24(12.2 %)n = 19

(9.7 %)

n = 64(36.0 %)

n = 66(33.7 %)

n = 32(16.3 %)

n = 48(27.0 %)

n = 80(40.8 %)

n = 123(62.8 %)

0,0 %

20,0 %

40,0 %

60,0 %

80,0 %

100,0 %

1st 2nd 4th

failure-expectation low-motivation autonomic adaptive-social

65***

17*

8***

7**

4 ***

3 *

9 **

10 **

30***

13***

15***

0 **

8 *

5 *

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2c. Person-oriented analysis on developmental changes over time

• Clustering is conducted by using variablesmeasured over several time-points

• The benefit of the approach is that the changepatterns over time becomes very clear

Development of Reading Skills among Preschool and Primary

School Pupils

Ulla Leppänen, Pekka Niemi,

Kaisa Aunola & Jari-Erik Nurmi

Reading Research Quarterly.

Mean LUKUT5Mean lukut4: lukutai

Mean lukut3: lukutaiMean lukut2: lukutai

Mean lukut1: lukutai

70

60

50

40

30

20

10

0

197.00

198.00

199.00

200.00

201.00

202.00

203.00

204.00

205.00

206.00

207.00

Level Trend 1 Trend 2

1 1 11

01 1 1

00 0 1

Reading skillTime 1

Reading skillTime 2

Reading skillTime 3

Reading skillTime 4

.26** -.71***

-.82***

.17 .09 .09 .43

Time 4Time 3Time 2Time 1

Rea

ding

Ski

ll S

core

s

40

30

20

10

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Cluster Groups

1 N=71

2 N=113

3 N=11

But … whatever procedure you usethere is always many statistical tools to

conduct the clustering:– Clustering by cases (e.g., SPSS)– Mixture modelling (M-plus, AMOS)