Cant Count Wont Count Some Results From A

Can't Count, Won't Count? Some Results From A National Survey Of Student Attitudes To Quantitative Methods Malcolm Williams, Liz Hodgkinson, Geoff Payne, Donna Poade, University of Plymouth Contact: malcolm. williams@plymouth. ac. uk

Stages of the Research • Series of small scale projects: i) Content analysis of quantitative output of leading British sociology journals ii) Baseline study of taught quantitative methods in UK HEIs & consultation with methods teachers (CSAP/ BSA funded) iii)Study of student attitudes to quantitative methods (undergraduates in English & Welsh HEIs (ESRC Funded)

Results - Journals • 14. 3% of content quantitative & 40. 6% qualitative • 5. 3% used bivariate analysis • 6. 1% multivariate • ‘Junior’ staff over twice as likely to employ qualitative than quantitative methods. • Conclusion: quantitative methods underepresented in UK academic sociological output

Results – Sociology Units • 68% of units said ‘quants’ comprised between 5 & 15% of sociology curriculum. Only 5% taught no ‘quants’ • Survey methods compulsory in 19% of units • Data analysis compulsory in 15% • Conclusion: Quants central to virtually all programmes, but consultations revealed problems in ‘quantitative culture’, teaching and learning.

HEI Study • Survey of sociology students across HEIs in England Wales (n= 738). Focus groups in 4 institutions. • Conducted November – March 2005/6 • Aimed to description of student perceptions of and attitudes towards quantitative methods in political science and sociology.

Views of Sociology • Nearly two-thirds thought sociology had less status than the natural sciences. • ten point semantic differential scale: 71% scored toward the arts humanities end of the scale, 14. 5 % the science end of the scale chose 15. 5% the middle category • ‘two sociologies’ in the focus groups

Table 1 Topics Studied during degree (multiple response)

• All students in the sample had studied some quantitative methods by Stage 3 • Just under 80% had studied statistics in some form. • ‘Quantitative secondary analysis’ and ‘qualitative analysis’ probably being interpreted quite broadly.

Table 2 Student Experience of Research Methods

Student Experience of Research Methods. • Some indication that students mostly regard quantitative methods as a necessary evil. • Less than half of students enjoyed learning about surveys. 65% would rather write an essay than analyse data. • A sizeable proportion have concern about their numeric ability and nearly half claim to have had a bad experience of maths at school. • 76. 6% of all those who had a bad experience of maths at school were anxious about statistics (not shown here).

Table 3 Student Attitude by Performance

• With the exception of the final attitude statement about ‘trusting statistics’ there is a clear association between a positive attitude toward quantitative methods and achievement in methods modules. • Students who viewed number and quantitative methods more positively more likely to obtain 2. 1 / 1 st and less likely to fail or obtain a 3 rd. • Opposite true for those who expressed a negative attitude, or fear of number (though maybe issue of causality).

Table 4. Performance in Research Methods marks by Course entry requirements

• Relationship between achievement and UCAS entry points. Courses requiring 340 -260 more likely to have students achieving 1 st / 2. 1 and less likely to have students failing methods modules than those HEIs with a lower entry tariff for sociology. • Lowest band had the highest percentage of Fails, but those in the lower band were more likely to achieve Firsts or Upper Seconds than those in the middle band. By-product of recruitment policies geared to mature students? • Cross-tab (not shown here) of tariff points by attitude statements showed a significant positive relationship between the highest point band positive view of number.

Table 5 Difficulty of Statistical Technique

• Yellow Group: intuitively understandable topics requiring little arithmetic skill and to some extent largely visual (charts, means, frequencies, histograms). • Green Group: topics that require greater conceptualisation/logic and perhaps more confidence with number (correlation, hypothesis testing, standard deviation): • Blue Group: topics that form a more conventional core of basic statistics techniques requiring more grasp of number and the internal logic of statistical reasoning (Chi-sq, Pearson's, V, t test, z test, Spearmans rho, regression)

Provisional Conclusions • All students study some quantitative methods • Less than 50% enjoy learning about quantitative methods and majority would rather write an essay • Evidence of concern about number and bad experiences of number at school • These latter associated with poorer marks as is a ‘negative’ attitude to quantitative methods. • Some evidence of an underlying ‘anti-science’/ ‘prohumanities’ attitudes.