Quantitative Methods in Social Sciences (E774)

1. Quantitative Methods in Social Sciences (E774) December 4, 2009 GenderDifferencesAmongIHEID 1st YearMdev Students Catherine DoeAdodoadji NatháliaEstevam Fraga Aleksandra Žaronina Maurice Tschopp 4 December 2009

2. IntroductionandHypothesis • Gender differences - one of the most discussed issues in frames of the Development discourse • Context: rapid women emancipation process during the past 20 years, spreading not only in the “developed”, but also in “developing” • Focus: IHEID Mdev 1st Year Students • Why HEID Mdev Students? • Different cultural backgrounds • Representing over 40 countries • Different education backgrounds • Involved in Development Studies Hypothesis: There is no significant difference between male and female MDev students’ personal profiles and opinions on global issues QM_MDEV_E774(2009)

3. Datasets • Data used: Results of the survey held in frame of the course Quantitative Methods for Social sciences by Pr. Basu • Data used for the series of policy papers - mostly qualitative • Dataset by biggest categories: • Educational background • 1st or 2nd degree • Previous degree by categories( SociSci, Arts, BA, Nat Sc) • Statistical background • Computer proficiency • Most important global issue • The most important global issue • Approve /disapprove of the way world leaders are dealing with the current global issues • Rating the role of UN solving some of the global issues • Global Financial Crisis • Being concerned about the global financial crisis • Possible negative impact of the financial crisis on students homecountries • Student personal profile: habits and skills • Time spent of phone • Time spent on internet QM_MDEV_E774(2009)

4. ValidityandReliability Validity Internal- the degree to which conclusions about causes of relations are likely to be true, in view of the measures used, the research setting, and the whole research design • Objectives accurately described in each of the policy papers, consistent among policy • Due to time limits survey held only once- no re/test held • In some cases; lack of continuous data to be used for more advanced statistical techniques External - to what extent one may safely generalize the (internally valid) causal inference (a) from the sample studied to the defined target population • Time relevance • Recently collected data • Big sample in relation to population size • Multidimensional analysis (personal profile, habits, opinions on global issues) Indicators: Mostly qualitative and discrete Encompassing both opinions and numeric data Standartisedwhere appropriate Reliability: Data collected is consistently Units of measurement standardized Augmenting level of complexity of techniques Errors tests performed QM_MDEV_E774(2009)

5. Statistical Techniques 1 Describing centre • We described center by finding the percentages of male and female students since our data was qualitative in nature Variability • To describe variability we made use of standard deviation and coefficient of variation Sampling • Random sampling • Population = all 1st year MDev students • Sample = the Mdev student, who provided their answers • In some of the stages of the analysis divided the sample into two sub-samples male and female sub-samples and compared them between themselves QM_MDEV_E774(2009)

6. Statistical Techniques 2 Estimation and significance • In our policy paper we used the confidence interval for a proportion to test significance ( Z-test) Correlation • Calculated correlation coefficient( r) between the pairs of variables (time spent on phone, time spent on internet, computer proficiency, previous background in statistics) for male and female sub-samples • Compared the results of the two subsamples • Run the significance test QM_MDEV_E774(2009)

7. Statistical Techniques 3 Regression • Run two regressions for each of the sub-samples( male and female) in order to find out if there could be any linear relationship between pairs of the variables (time spent on phone, time spent on internet, computer proficiency, previous background in statistics), putting the particular stress on significance of the model and variables F and P values • Compare the results of the relevant regressions between the genders • Running outlier test to identify outliers • Excluding the outliers from the data, make the comparison of the two models with and without outliers for both subsamples QM_MDEV_E774(2009)

8. Results Policy Paper 1: In this PP we mainly used descriptive statistics instruments to discuss differences in opinions among female and male Mdev Students. Method: Comparing proportions and distribution of variables with the help of tables and figures. Results and conclusion: comparable patterns in the answer of male and female students: Example: 74 % of female students believe that Global poverty is the most important global issue. 62 % of male students have the same opinion. QM_MDEV_E774(2009)

9. Similarities in opinions of Mdev Male and Female Students

10. Results: Policy Paper 2 Method: using Z calculations and test of hypothesis to see if the differences in percentages were significant enough. Example: 87.5 % of male students believe that their country has been negatively affected by world crisis. (77% have the same opinion). Test for male students H0: π = 0.77 H1: π > 0.77 Z = π - π 0 = 0.875 – 0.77 = 1.35 Z (table) =1.65 (one tailat 95%) SE 0.0826 Z< Z (table) We cannot reject H0. Therefore we cannot say with a confidence interval of 95 % that there is a statistical differences on opinions of both male and female students on this issue. QM_MDEV_E774(2009)

11. Results: Policy Paper 3 Additional hypothesis: Students who spend a lot of time and internet also spend more time on phone and have better computer proficiency. We also looked at our “old” hypothesis, that states that there is no significant difference among men and women on the variables tested. Method: Using correlations and regressions to find similar patterns in male and female subsample and to test our second hypothesis. Results: (PP3): • Correlations, similar coefficients in male and female subsamples for relations tested: • Ex relation between time spent on internet and time spent on phone • But, a many of these coefficients were not significant to confirm our hypothesis. • Next step: regression calculations QM_MDEV_E774(2009)

12. Results: Policy Paper 3 Regression time internet //time on phone for femalestudents R Squared = 0.4916 Beta = 0.41 p-value= 0.000

13. Results: Policy Paper 3 Regression time internet //time on phone for male students Beta= 0.069; n = 12 R-square= 0.1451 QM_MDEV_E774(2009)

14. Conclusions What’s new about our approach: • Analysis made on the basis of the unique sample, encompassing representatives of over 40 nationalities, very diverse academic and professional backgrounds. ( Normally the analysis made either on the basis of more homogenous or bigger number/size of samples) What we learnt: • Fundamental importance of survey structure • The crucial need to determine the most appropriate technique to the particular data analysis • How to balance between narrative and technically analytical part of PP Policy Implications: • Males should be encouraged to apply to the MDev • There should be more courses on poverty in the MDev structure (currently, there is only one offered by the institute) QM_MDEV_E774(2009)

15. Future work Shortcomings of the dataset and missing elements of research: • No information about the respondents country or region of origin • Responses to open-ended questions had great variability • Survey 2 focused on global issues but most questions were related to the financial crisis • In some cases there was a need to use the more advanced techniques of analysis(non-linear regressions) Suggestions: 2nd round of surveys at the end of the semester • After the course in Quantitative Methods in Social Sciences, how would students evaluate their knowledge of the subject? • Would there be differences in the time spent on the phone and internet? • What was the overall time spent on course work? • What were the grades obtained? QM_MDEV_E774(2009)