using the idb analyzer iv - european commission · 1 using the idb analyzer iv andrés...

1

Using the IDB Analyzer IV

Andrés Sandoval-Hernández – IEA DPC

Workshop on using PISA, PIAAC, TIMSS & PIRLS, TALIS datasets

Ispra, Italy- June 24-27, 2014

Note: These slides were prepared as part of the IEA training portfolio with the collaboration of IEA staff and resource persons.

2

Table of contents

• Correlations with TIMSS and PIRLS combined

• Regression with PIAAC

3

Correlations

How strong is the correlation between

achievement in reading and achievement in

mathematics?

ASMMAT1-5 ASRREA1-5

Variables of interest

4

What Do we Need to Do?

• Merge the data set for TIMSS and PIRLS 2011 by choosing the student background questionnaire and the selected countries

• Countries in the merged data file:

– Austria

– Italy

– Poland

– Slovak Republic

– Spain

• Open Analysis Module

5

Analysis: Correlations with TIMSS and PIRLS combined

• Analysis File: C:\EU_Workshop\Work\TIMSS_PIRLS_Merged.sav

• Analysis Type:

TIMSS/PIRLS (Using Student Weights)

• Statistic Type: Correlations

• Plausible Value Option: Use PVs

• Grouping Variables: IDCNTRY

• Plausible Values: ASMMAT1-5, ASRREA1-5

• Weighting variable: TOTWGT

6

Correlations Analysis

C:\EU_Workshop\Work\TIMSS_PIRLS_Merged.sav

C:\EU_Workshop\Work\ASMMAT_ASRREA.*

Analysis Type:

TIMSS/PIRLS (Using Student Weights)

Statistic Type:

Correlation

Plausible Value Option:

Use PVs

IDCNTRY

ASMMAT01-05

ASRREA01-05

7

Correlations Analysis

Start SPSS and run syntax

8

Country = Austria

.78 (.01)

There is a significant positive correlation between the achievement in math and achievement in reading for the students that participated in TIMSS and PIRLS 2011, grade 4.

Correlations Outcome

9

Correlations Outcome: Correlations

Correlation between ASMMAT1-5 and ASRREA1-5 per country

Standard error of correlations

10

Correlation Outcome: Descriptive

Mean and standard deviation for the variables

Standard errors of the mean and standard deviation

11

Table of contents

• Correlations with TIMSS and PIRLS combined

• Regression with PIAAC

12

Regression with continuous variable

To what extent does the level of education

explain differences in problem solving skills ?

ISCED_HF_C PVPSL1-10

Variables of interest

13

What Do we Need to Do?

• Merge the data set for PIAAC for the selected countries

• Countries in the merged data file:

– Austria

– Denmark

– Netherlands

– Slovak Republic

– Spain

• Open Analysis Module

14

Analysis: Regression with continuous variable

• Analysis File: C:\EU_Workshop\Work\PIAAC_Merged.sav

• Analysis Type:

PIACC (Using Full Final Sample Weight)

• Statistic Type: Regression

• Plausible Value Option: Use PVs

• Grouping Variables: CNTRYID

• Independent Variable: ISCED_HF_C

• Dependent Variable: PVPSL1-10

• Weighting variable: SPFWT0

15

SPSS: Run the analysis

16

Differences btw students belonging to schools of ‘Very high’ (ACDGEASH=1, the reference group) and ‘Medium’ (ACDGEASM=1) school emphasis on academic success in math achievement

Analysis : Results

17

Regression: Results

In AUSTRIA each additional education level attained is associated to 4.09 score points in the scale of problem solving. This association is statistically significant (α=0.05) as t-value > 1.96

* Check descriptive statistics!

18

Any Questions?

Thank you for your attention!