SP3 analytical SP3 analytical chapterchapter

Overview of resultsOverview of results

Ellen BoerenEllen Boeren

LLL2010 Sofia meeting 13-06-2010LLL2010 Sofia meeting 13-06-2010

1. Overview of variables1. Overview of variablesARCS model (John Keller) attitude – relevance – confidence – satisfaction can be influenced by environment implementation of … * management strategies * teaching strategies

role of classroom environment role of barriers

DEPENDENT VARIABLES

1. Overview of variables1. Overview of variablesARCS model (John Keller) control for socio-economic status gender, age, education, occupation

control for organisation of ed. institution exemptions, entrance, scheduling, didactics, services

+ control for classroom environment and barriers

INDEPENDENT VARIABLES

2. Overview of analyses2. Overview of analysesA) Do countries differ on dependent variables?

Analysis of Variance (ANOVA)

B) How are dependent variables shaped / related to control variables?

Lineair regressions Start with bivariate correlations

3. ANOVA3. ANOVA- Do country scores differ on dependent factors? F-value, df, p-score, intraclass correlation

- Which countries have higher scores on these dependent factors? Do they differ (p*<=.050)?

Bonferroni post-hoc tests

ICC COUNTRY DIFFERENCES

ICED 1-2 ISCED 3 ISCED 4 ISCED 5 TOTAL

extrinsic motivation .61 .23 .28 .26 .29

total barriers .48 .22 .12 .27 .23

attitude .22 .09 .05 .09 .12

classroom environment .13 .11 .11 .12 .09

intrinsic motivation .09 .06 .13 .06 .06

satisfaction .08 .05 .04 .07 .05

confidence .03 .03 .03 .04 .02

3. ANOVA3. ANOVA

3. ANOVA3. ANOVAEXTRINSIC MOTIVATION

COUNTRIES

TOTAL (F=336,980; df=11) BG – LT / HU – RU / CZ – EE / SI – SC – IE / EN / AU – BE

ISCED 1-2 (F=188,182; df=10) BG – HU / RU / LT / SI / AU / SC / EE – IE / EN – BE

ISCED 3 (F=71,466; df=11) BG / HU / LT / CZ – SI / EE / RU / SC / IE / EN – AU – BE

ISCED 4 (F=98,729; df=10) BG – LT / HU / RU – EE / SI / IE / AU / EN / SC – BE

ISCED 5 (F=87,657: df=11) BG – LT / HU / RU / CZ / EE / SC / SI / EN / IE – AU / BE

3. ANOVA3. ANOVABARRIERS

COUNTRIES

TOTAL (F=94,045; df=12) NO / RU / EE / SI / HU – LT / CZ / AU – BE – BG / IE – SC – EN

ISCED 1-2 (F=54,330; df=11) RU / SI / EE / AU – NO – LT / HU – SC / IE / BG / BE / EN

ISCED 3 (F=26,794; df=12) NO / SI / HU / EE / RU / LT / BG / CZ / AU / IE / BE / SC / EN

ISCED 4 (F=13,965; df=11) NO / EE / RU / SI / IE / AU / HU / BG / LT / SC / EN / BE

ISCED 5 (F=28,004; df=12) EE / LT / NO / RU / AU / CZ / HU / SI / BE / IE / BG – SC / EN

3. ANOVA3. ANOVAATTITUDE

COUNTRIES

TOTAL (F=133,712; df=12) NO – AU / IE / EE / HU / EN / SC / BE / LT – SI / BG – RU / CZ

ISCED 1-2 (F=65,387; df=11) NO / AU – IE / BE / HU / EE / LT / EN / SI / SC / BG – RU

ISCED 3 (F=40,313; df=12) NO – AU / IE / HU / SC / EN / EE / BE / RU / LT / SI / BG – CZ

ISCED 4 (F=23,942; df=11) NO – EE / IE / SC / EN / AU / HU / LT / BG / BE / SI / RU

ISCED 5 (F=47,810; df=12) NO – AU / EE / IE / EN / LT / SC / HU / RU / SI / BE / BG / CZ

3. ANOVA3. ANOVACLASSROOM ENVIRONMENT

COUNTRIES

TOTAL (F=86,349; df=11) EN / IE / SC / AU / RU / BE / EE / BG / LT – HU – SI / CZ

ISCED 1-2 (F=30,412; df=10) AU / IE / BE / EN / SC / BG / HU / EE / RU / LT / SI

ISCED 3 (F=28,557; df=11) IE / AU / SC / RU / EN / HU / BG / BE / LT / EE / CZ / SI

ISCED 4 (F=27,698; df=10) EN / IE / SC / EE / RU / LT / BG / BE / HU / AU – SI

ISCED 5 (F=38,766; df=11) EN – SC / EE / AU / LT / IE / RU – BE / BG / SI / CZ / HU

3. ANOVA3. ANOVAINTRINSIC MOTIVATION

COUNTRIES

TOTAL (F=58,981; df=11) IE / SC / LT / BG / EE / RU / EN / BE / HU / SI / AU / CZ

ISCED 1-2 (F=20,851; df=10) IE / BG / SC / LT / BE / HU / EN / EE / AU / RU / SI

ISCED 3 (F=15,176; df=11) IE / SC / LT / EE / RU / BG / EN / BE / HU / SI / AU / CZ

ISCED 4 (F=34,651; df=10) EE / LT / SC / IE / EN / RU / BG / BE / SI / HU / AU

ISCED 5 (F=18,016; df=11) RU / EE / BG / IE / LT / EN / SC / HU / SI / AU / BE – CZ

3. ANOVA3. ANOVASATISFACTION

COUNTRIES

TOTAL (F=50,148; df=11) IE / EN / SC / AU / RU / BE / LT / BG / EE / SI / HU / CZ

ISCED 1-2 (F=19,149; df=10) IE / BE / AU / EN / SC / BG / RU / EE / LT / SI / HU

ISCED 3 (F=15,541; df=11) IE / AU / EN / RU / SC / BG / BE / SI / EE / LT / HU / CZ

ISCED 4 (F=11,062; df=10) IE / EN / SC / RU / LT / BG / BE / AU / EE / HU / SI

ISCED 5 (F=20,575; df=11) EN / IE / SC / RU / AU / EE / LT / BE / SI / BG / CZ – HU

3. ANOVA3. ANOVA

COUNTRIES

TOTAL (F=50,148; df=11) IE / EN / SC / AU / RU / BE / LT / BG / EE / SI / HU / CZ

ISCED 1-2 (F=19,149; df=10) IE / BE / AU / EN / SC / BG / RU / EE / LT / SI / HU

ISCED 3 (F=15,541; df=11) IE / AU / EN / RU / SC / BG / BE / SI / EE / LT / HU / CZ

ISCED 4 (F=11,062; df=10) IE / EN / SC / RU / LT / BG / BE / AU / EE / HU / SI

ISCED 5 (F=20,575; df=11) EN / IE / SC / RU / AU / EE / LT / BE / SI / BG / CZ – HU

CONFIDENCE

3. ANOVA3. ANOVACONCLUSIONS- Extrinsic motivation differs most- Barrier group differs second most

(I refer to 2 models that can be of help in understanding these differences)

influence of motives (Smith & Surling, 2001) bounded agency (Rubenson & Desjardins, 2009)

Targeted policy measures

Structural barriers•Family, job & institutionally related barriers

Broad structural conditions

Type of welfare state regime

Dispositional barriers

Conditioning of values and perspective on opportunity structure

Disposition•Capabilities •Consciousness

Bounded agency•Choices and action enablíng participation•Decision to participate

IndividualStructure

Dynamic feedback into defining structure

Dynamic feedback into defining self

Subjective rationality and perspective on opportunity structure

4. Regressions4. RegressionsFirst step before regressions- Bivariate correlation between macro level

variables and ARCS

correlation between macro level indicators and extrinsic motivation

negative correlation e.g. countries with low GDP = higher score on

extrinsic motivation

- Correlation with other variables: classroom environment

4. Regressions4. Regressions- How are the dependent variables shaped in

various countries?

separate lineair regressions by country

- Full regressions (B – S.E. – p – adjusted R²) in appendix + comparison with multilevel

- Indication of significant coefficients and direction reported in a table within text

large dataset, lot of significant effects table next slide p*** <= .010 only

SATISFACTION

AUBE BG CZ ENG EE HU IE LT RU SC SL

MICROMale (ref=female)

25 – 40 years old (ref=-25)41 – … years old (ref=-25)Medium educ level (ref=l)High educ level (ref=low)

Job-seeker (ref=employed)Inactive (ref=employed)

BARRIERSTransport problemsChildcare problemsFinancial problems

Time schedule problemsTime studying problems

Lack of preparation problemAge differences problem

Family problemsMESO

Classroom environmentEntrance test

APELAPL

DaytimeWeekends

EveningsWhole class teaching

Individual teachingDistance teaching

Group workingModular based

Financial supportLessons > 3 times/week

Childcare serviceOrganised transport

CafetariaInternet access

LibraryService for study advice

Career serviceSocial serviceLegal service

Medical serviceDormitory

Sports accommodationISCED 3 (ref=ISCED 1-2)ISCED 4 (ref=ISCED 1-2)ISCED 5 (ref=ISCED 1-2)

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4. Regressions4. RegressionsDo we recognize some patterns ? Based on

p*<=.100, p**<=.050 and p***<=.010

SATISFACTIONTime scheduling problems (- in 8 countries)Lack of preparation (- in 6 countries)Classroom environment (+ in all 12 countries)

CONFIDENCEAge difference problems (- in 6 Eastern European countries)Classroom environment (+ in 11 countries)

4. Regressions4. Regressions

INTRINSIC MOTIVATIONMale (- in 7 countries)Inactive (+ in 5 countries)Classroom environment (+ in all 12 countries)Group working (+ in 5 countries)

EXTRINSIC MOTIVATIONAge difference problems (+ in 6 countries)Classroom environment (- in 5 countries)Financial support (+ in 5 countries)ISCED 5 course (- in 5 countries)

4. Regressions4. Regressions

ATTITUDEMale (- in 9 countries)Age 25-40 (+ in 6 Eastern European countries)Age 41 and older (+ in 9 countries)Classroom environment (+ in all 12 countries)

Relation classroom environment and AR(i)CS always at p<=.001 level

Classroom environment as a dominant variable! BUT: which variables are related with classroom

environment ???

4. Regressions4. Regressions

CLASSROOM ENVIRONMENTMale (- in 7 countries)Age 41 and older (+ in 8 countries)Time scheduling problems (- in 7 countries) ***Time studying problems (- in 6 countries)Lack of preparation (- in 5 countries)Age difference problems (- in 5 countries)Whole class teaching (+ in 5 countries)Individual teaching (+ in 8 countries)Group working (+ in 7 countries)Service for study advice (+ in 7 countries)ISCED 4 courses (- in 5 countries)ISCED 5 courses (- in 7 countries) ***

4. Regressions4. Regressions

BARRIERSMale (- in 5 countries)Classroom environment (- in 11 countries)

Relation between classroom environment and barriers Males experience less barriers But … they are less positive about the classroom

environment (cfr. Hayes: different needs / gender) Time problems as a major barrier in enjoying the course

5. Summary5. Summary- There are country differences in dependent

variable scores …- But, within the different countries, these

dependent variables are - the more or less -shaped in the same way

At this moment, we have difficulties in recognizing clear regression patterns like e.g. East – West or Anglo-Celtic versus Continental …

yes in ANOVA

6. Clusters versus Countries6. Clusters versus Countries