Régimen fiscal del contrato

Quantitative 9.0

Qualitative 57.7

Mixed Methods 32.0

I do not undertake empirical research 1.3

N 450

The majority (57.7%) of survey participants saw themselves as ‘qualitative researchers’. It seems that the dominance of qualitative methods in British sociology is so great that, even when combining ‘mixed methods researchers’ with those who saw themselves as purely

‘quantitative researchers’, the percentage of ‘qualitative researchers’ was still greater (41.0%

compared to 57.7%). Participants’ resistance toward quantitative methods was also detectable in the comments that they gave in the survey:

“[…] quantification is the root of all evil in sociology” (Qualitative Researcher, Female, Lecturer or Equivalent, Aged 35-44)

“[…] quantification is very worrying, other disciplines can do that” (Qualitative Researcher, Female, Postgraduate, Aged 35-44)

Only a small number (1.3%) of participants reported not conducting empirical research.

Conversely, MacInnes et al. (forthcoming) found that approximately 30% of papers published in the British Journal of Sociology, Sociological Review and Sociology between 2008 and 2010 were non-empirical. Likewise, in their analysis of mainstream British sociology journals, Payne et al. (2004) found that almost 40% of articles published were non-empirical. Similarly, they reported that over 35% of the papers presented at the British Sociological Association (BSA) conference in 2000 were non-empirical. While, these studies of the output of British sociology are now at least seven years old, the evidence may indicate that there is a possible disjuncture between the methodological identity of sociologists and their research output.

2.1 Model Building

Multinominal regression models were built to predict the odds of respondents using quantitative methods in the last year; the odds of respondents using qualitative research methods in the last year, and the odds of respondents classifying themselves as either a

‘quantitative’, ‘qualitative’ or ‘mixed methods’ researcher. Multinominal regression is an extension of binary logistic regression (Sheskin, 2007: 1619; Field, 2013: Chapter Nineteen).

It enables researchers to predict group membership when more than two groups or

outcomes exist. Table 4.3 shows all the predictor variables considered for inclusion in these models, the parameters of the variables and frequency of responses.

An assumption of multinominal logistic regression is that all predictor variables are strongly related to the dependent variable but, at the same time, predictors cannot be strongly related to each other (Pallant, 2010: Part 4). To ensure that variables meet these conditions, two steps need to be taken. Firstly, bivariate analysis between each predictor and the dependent variable needs to be conducted. Only variables which share a statistically significant relationship can be retained for the model building. Secondly, collinearity diagnostics need to be analysed to ensure that the predictor variables are not strongly related to each other.

Crosstabulation and chi-square statistics were produced to investigate the relationships between the predictor variables and the dependent variables (see Table 4.4). Statistically significant associations are discussed below.

Table 4.3: Variables included in regression models

Variable Description Parameters %

Gender Whether a respondent is male, female or other 1=Male

2=Female

51.3 48.7

Age Whether a respondent is aged 18-34, 35-44, 45-54 or 55+ 1=18-34

2=35-44 Organisation Type Whether a respondent worked/studied in a college or university or worked outside

academia

1=University/College 2=Other

97.1 2.9 Russell Group Whether a respondent worked/studied in a Russell Group institution or not 1=Yes

2=No

56.0 44.0 Employment Contract Whether a respondent has a teaching contract or not. Variables recoded to exclude

those on neither teaching or research contracts due to the low cell count

1=Research Only 2=Teaching/Teaching &

Research

18.9 81.1 Seniority Whether a respondent is a student (undergraduate or postgraduate), lecturer (or

equivalent), senior lecturer (or equivalent) or professor/reader (or equivalent)

1=Postgraduate Qualification outside UK Whether a respondent has obtained a qualification outside of the UK 1=Yes

2=No

30.3 69.7

BSA Member Whether a respondent is a member of the British Sociological Association 1=Yes

2=No

66.4 33.6 BSA Membership Length The length of time a respondent has been a member of the British Sociological

Association

Table 4.4: Predictor variables and dependent variables (row percentages)

Last Year Quantitative Last Year Qualitative Researcher Identity A lot Percentages in bold indicate statistically significant associations

2.1.1. Model 1: Last Year Quantitative

Table 4.4 shows that almost a quarter (23.7%) of those aged 18-34 reported using ‘a lot’ of quantitative methods in the last year compared to 15.2% of those aged 35-44; 14.8% of those aged 45-54, and only 10.1% of those aged 55 and over. However, while being the group with the smallest frequency of participants reportedly using ‘a lot’ of quantitative research methods in the last year, those aged 55 and over, were also the least likely group to report using ‘no’ quantitative methods in the last year. This could suggest that this group are more likely to engage with a variety of methods or approaches in their work compared to their younger peers. Those aged 55 and over were also more likely than younger respondents to report using ‘some’ or ‘a little’ quantitative research in their work in the past twelve months.

Moreover, while the modal response for each other age group was using ‘no’ quantitative methods at all, the modal response for those aged 55 and over was using ‘some’ quantitative methods in the last year.

Almost one quarter (23.7%) of those who had qualified from other countries reported using

‘a lot’ of quantitative research methods in the last twelve months, in comparison with 13.4%

of those who had not obtained a qualification outside of the UK. However, the modal response for both groups was ‘not’ using quantitative approaches at all in the last year.

2.1.2. Model 2: Last Year Qualitative

Female participants were more likely to report using ‘a lot’ of qualitative research methods in the last twelve months compared to male respondents. Over 66% of the females who responded, stated that they had used ‘a lot’ of qualitative methods in the last year compared to 53.7% of the male participants. Further, female respondents were less likely to report that they had ‘not’ used qualitative methods at all in the last year in comparison to the male survey participants. Just over 7% of males stated that they had ‘not’ used qualitative research methods in the last year, while under 5% of females reported that they had ‘not’ used qualitative research methods in the last twelve months. The modal response for both males and females was ‘a lot’ of qualitative research methods.

The association between the level of engagement with qualitative methods in the last year and obtaining a qualification outside of the UK was less obvious. 67.9% of those who had obtained a qualification abroad stated that they had used ‘a lot’ of qualitative methods in the last year in comparison to 58.2% of those who had not obtained a qualification outside of the UK. However, those who had obtained a qualification outside of the UK, were also more likely to report ‘not’ using qualitative research methods at all in the last year. 6.9% of those that had obtained a qualification overseas had ‘not’ used qualitative methods at all in

the last year compared to only 4.7% of those in the sample who had not studied abroad. The modal response for both groups was using ‘a lot’ of qualitative methods in the last year.

2.1.3. Model 3: Researcher Identity

Table 4.4 also shows that the female respondents were more likely to state that they were

‘qualitative researchers’ compared to the male participants in the sample. 64.7% of females classified themselves as ‘qualitative researchers’ in comparison to 52.1% of the male participants. Conversely, male respondents were much more likely to identify as

‘quantitative researchers’ than their female counterparts. Just over 10% of the males in the survey sample stated that they were ‘quantitative researchers’ in contrast to 8.3% of the females. Of the male participants, 37.4% reported being ‘mixed methods researchers’

compared to 27.1% of female respondents. The modal response for both males and females was ‘qualitative researcher’.

The eldest respondents in the sample were more likely to classify themselves as ‘mixed methods researchers’. Half of those aged 55 and over stated that they were ‘mixed methods researchers’ compared to less than 30% of participants in each of the younger age cohorts.

The younger the participants, the more likely they were to identify as ‘quantitative researchers’. Of those aged 18-34, 12.2% stated that they were ‘quantitative researchers’

while less than 5% of those aged 55 and over stated that they were ‘quantitative researchers’.

Meanwhile, there was no clear direction to the statistically significant association between seniority and researcher identity, with professors and readers and postgraduates having similar proportions of ‘qualitative’, ‘quantitative’ and ‘mixed methods’ researchers.

Finally, those in the sample who had obtained a qualification abroad were more likely to classify themselves as a ‘quantitative researcher’. Of those who had obtained a qualification from overseas, 15% stated that they were a ‘quantitative researcher’ in comparison to 6.8%

of those who had not received a qualification abroad. The modal response for both groups was ‘qualitative researcher’.

Collinearity diagnostics between the predictor variables for each of the models (Model 1:

Last Year Quantitative; Model 2: Last Year Qualitative, and Model Three: Researcher Identity) were investigated in turn. A Variance Inflation Factor (VIF) score greater than 2.5, a tolerance level below 0.4 or a condition index greater than 15 can suggest that variables are correlated with one another and are too similar to include in the model (Sheskin, 2007: 1477; Tarling,

Collinearity was not an issue for the first two models (Model 1: Last Year Quantitative; Model 2: Last Year Qualitative). However, for Model 3 predicting researcher identity, the collinearity diagnostics revealed a slightly high condition index for the qualification obtained outside of the UK variable (18.22). As this is only slightly larger than the recommendation of 15 and the VIF and tolerance levels were both sound, the variable was retained in the analysis as it was believed to be important in understanding the methodological preferences of sociologists working in the UK. Additionally, it is suggested that collinearity may be an issue if two variables with high condition indexes have a variance proportion above 0.5 (Sheskin, 2007:

1477). This was not the case for the variables included in this analysis.

Table 4.5 shows the variables included in each of the models and the reference categories for each of the variables.

Table 4.5: Reference categories and other categories for each variable in multinominal regression models

Variable Reference

Category

Other Categories

Last Year Quantitative None A Little, Some, A Lot

Last Year Qualitative None A Little, Some, A Lot

Researcher Identity Quantitative Qualitative, Mixed Methods

Gender Female Male

Age 55+ 18-34, 35-44, 45-54

Seniority Postgraduate Lecturer, Senior Lecturer, Professor/Reader

Qualification Outside UK No Yes