Diferenciando la violencia familiar de la zona rural de la urbana

Two main typologies have been put forward to define the purposes of mixed- methods evaluation research.

First, Greene et al (1989) described five purposes of mixed-methods evaluation: triangulation, complementarity, development, initiation and expansion (Box 3.1). This early typology continues to be influential. In their review of 57 early mixed- methods studies, Greene et al (1989) coded each study into these five purposes, both in terms of their ‘stated purpose’ and the ‘actual use’ when the results were presented. The stated purpose for these studies showed that complementarity (29%) and expansion (25%) were cited the most, whereas development (10%), triangulation (8%) and initiation (0.4%) were less common (the rationale was not stated for 27% of studies). Once the 57 reports were examined for the ‘actual use’ to which mixed-methods were put, complementarity (45%) and expansion (31.5%) both increased in frequency. The most frequent purpose for studies focusing on ‘evaluation research’ was

Limitations of Greene’s typology are that not all mixed-methods research can be categorised into these five purposes, and there appears to be overlap between these broad purposes. With these concerns in mind, Bryman (2006) devised a more detailed scheme involving 16 uses of mixed-methods research (Box 3.2). Although there is clearly overlap between the five purposes of Greene et al (1989) and the 16 purposes of Bryman (2006), this later scheme offers greater explanations of the reasons for carrying out mixed-methods research and is also more comprehensive.

Box 3.1 – Greene’s Five Purposes of Mixed-Methods evaluation 1. Triangulation(Denzin 1978): process of examining an issue using

different methods and then focusing on degree with which findings converge (aimed at enhancing validity of results).

2. Complementarity: different methods address different but

complementary research questions, and then seeks clarification of the results from one method with the results of the other method (aimed at seeking deeper understanding).

3. Development:results from one method inform the development or use of another method, using the two data types iteratively.

4. Initiation:seeks to discover inconsistencies in the results by contrasting the results from one method with another.

5. Expansion: to extend the breadth of the research using different methods for different components of the inquiry i.e. quantitative methods for measures of effectiveness and qualitative methods to assess process measures around the delivery of an intervention. Also aimed at facilitating the interpretation of the study results, e.g. statistical analyses from a quantitative study may be enhanced by a qualitative account, or vice versa.

Bryman (2006) used his scheme to categorise 232 mixed-methods research articles published from 1994 to 2003. The purpose or rationale of using mixed- methods was stated for 170 (73.3%) studies. ‘Enhancement’ was the stated rationale for a third of the studies (31.5%) which rose to over a half (52.2%) when studies were assessed against how mixed-methods were actually used. ‘Enhancement’ is equivalent to ‘expansion’ in the typology from Greene et al (1989), thereby confirming the findings of the earlier study in terms of the

Box 3.2 - Brymans’ Scheme to Categorise the Uses of Mixed-Methods Research

1. Triangulation – degree to which findings converge, enhancing validity 2. Offset – offset the weaknesses of qualitative and quantitative

research & draw on the strengths of both 3. Completeness – more comprehensive account

4. Process – quantitative research provides account of structures in social life but qualitative research provides sense of process 5. Different research questions – can answer different questions 6. Explanation – help explain findings generated by the other 7. Unexpected results – generates surprising results

8. Instrument development – qualitative research used to develop questionnaires

9. Sampling – one approach used to decide on sampling of respondents 10.Credibility – employing both approaches enhances integrity of results 11.Context – qualitative research provides the context for the results

from larger survey

12.Illustration – qualitative data illustrates quantitative findings

13.Utility– mixed-methods will be more useful to practitioners, in articles with an applied focus

14.Confirm and discover – qualitative data generates hypotheses, and quantitative data tests them in a single project

15.Diversity of views – (i) combining researchers & participants perspectives via quantitative and qualitative data, respectively; (ii) uncovering relationships (quantitative) and meaning (qualitative) 16.Enhancement – expand the findings from one data type with the other 17. Other/unclear

18. Not stated

‘triangulation’ as a rationale (12.5%, 29 studies) and the proportion actually using triangulation (34.5%, 80 studies). Of the 29 studies citing triangulation as the rationale only 19 actually used it in this way. The mis-match between rationale and actual use is therefore striking, with only 19 of the 80 studies which used triangulation having cited this as their rationale for doing mixed- methods research.

This mis-match may be due to two reasons (Bryman 2006). First, the rationale for mixed-methods research are not thought through in sufficient detail at the protocol stage, resulting in a mis-alignment when the results are analysed and presented. Second, mixed-methods research provides vast quantities of data and it is only when collected that the true potential emerges. Researchers then pursue different analytical strategies to that which was originally planned. For example, researchers may decide to use triangulation only when inconsistency between quantitative and qualitative data have emerged: ‘when faced with the two sets of data, some researchers find it hard to resist making allusions to the symmetry or otherwise between these findings’ (Bryman 2006, p266). This has the potential to bring greater understanding, although an argument against this laissez faire approach is that there should be a clear rationale for the use of mixed-methods at the outset to ensure that the right data collection methods are used.