ARAŞTIRMA YÖNTEMLERİ

Greene, Caracelli, and Graham (1989) suggest that five major objectives or motivations exist for conducting MMR. The first one is ‘triangulation’, which provides validation and concurrence of results gained from various methods and designs investigating the same circumstance. ‘Complementarity’, as the second motivation, seeks for further detailing, improvement, interpretation and description of the results obtained from one method with results from the other method. The third one is ‘initiation’, which means unearthing the bias, and discrepancies that cause re-designing of the research question. ‘Development’, the fourth motivation, includes utilizing the findings from one method to deliver clues to other method. The last motivation, ‘expansion’, seeks to expand the size of research with different methods for different investigation factors.

The weaknesses of the MMR are as follows:

The other options reflect the strengths of mixed methods research.

MMR can be defined as the combination of quantitative and qualitative methods for a clear understanding of research objectives than either approach alone. It is because MMR balances the weaknesses of quantitative and qualitative research and transforms them into strength. By using all types of data collection tools, researchers can get more comprehensive evidence for a research problem. On the other hand, MMR enables researchers to answer questions that cannot be answered by qualitative or quantitative approaches alone. MMR can be so functional that the researchers feel free to make use of different methodologies to reach the research objectives. It is possible to use words, pictures and narrative to add meaning to numbers in MMR. Requiring more than one research is considered as a weakness of mixed methods research.

Johnson and Onwuegbuzie (2004) propose a process of MMR including eight specific steps, respectively designating the research question, specifying whether a mixed design is appropriate, choosing the mixed method or mixed model research design, gathering the data, clarifying the data, validating the data, coming to a conclusion and writing the final report.

After deciding that MMR is suitable for the study, the researchers determine their rationale for using a mixed design. Participant enrichment involves blending quantitative and qualitative research to better the sample using approaches that encompass recruiting participants, dealing with activities such as institutional review board interviews assuring that each participant chosen is eligible for involvement. Triangulation seeks convergence, correspondence, and corroboration of results from different methods studying the same phenomenon. Instrument fidelity pertains to evaluating the convenience and/or effectuality of current instruments, by generating new instruments and observing performance of human instruments. Significance enhancement refers to interpretation and utility of findings.

MMR sampling process consists of seven steps:

The goal of the study determines the used research design. The researchers can use the convergent design in order to make people understand the topic more completely or validate the quantitative scales. While the explanatory design is being used to explain quantitative results, the exploratory design is selected to test qualitative exploratory findings. The embedded design is preferred in experimental study for preliminary exploration or follow-up explanation or more complete understanding of the experimental trial. On the other hand, in case of the social injustice, the transformative mixed methods research is conducted to describe the phenomonolgy. Lastly, multiple phase design is implemented to develop, adapt or evaluate program, multilevel statewide studies

In research implementation stage, firstly quantitative and/or qualitative data is collected through different methods followed by data analysis. Thirdly, mixed research findings should be validated or legitimized. Fourth stage is interpreting mixed research findings and the last step is writing the mixed research report.

Onwuegbuzie and Teddlie (2003) present a seven-stage conception of the mixed methods data analysis process within the MMR model. These stages are respectively data reduction, data display, data transformation, data correlation, data consolidation, data comparison, and data integration.

It is necessary to represent the importance of all findings in the MMR article (Onwuegbuzie and Leech, 2004). Regarding the significance of that, an interpretive framework which includes ten aspects of quality is drafted by Teddlie and Tashakkori (2009, pp. 301-302):

Mixed methods research can be defined as research designs using qualitative and quantitative data collection and analysis techniques in either parallel or sequential phases. The MMR can be stated as a process of research when researchers integrate quantitative methods of data collection and analysis and qualitative methods of data collection and analysis to understand a research problem. It corresponds to the class of research where the researcher mixes or combines quantitative and qualitative research techniques, methods, approaches, concepts or language into a single study.

Mixed methods research can be conducted in the following steps:

Greene, Caracelli, and Graham (1989) suggest that five major objectives or motivations exist for conducting MMR. The first one is ‘triangulation’, which provides validation and concurrence of results gained from various methods and designs investigating the same circumstance. ‘Complementarity’, as the second motivation, seeks for further detailing, improvement, interpretation and description of the results obtained from one method with results from the other method. The third one is ‘initiation’, which means unearthing the bias, and discrepancies that cause re-designing of the research question. ‘Development’, the fourth motivation, includes utilizing the findings from one method to deliver clues to other method. The last motivation, ‘expansion’, seeks to expand the size of research with different methods for different investigation factors.

In respect of limitations, a single researcher may have some difficulties on conducting both qualitative and quantitative research when s/he intends to use two or more ways all at once. Thus, a research team may be needed to get the best results. Furthermore, learning about multiple methods and approaches and understanding how to mix them aptly should be regarded as a must. Conducting MMR can also be more expensive, time consuming and may require formulating how to mix paradigm, analysing quantitative data or interpreting the paradoxical results by research methodologist

Five major standard research objectives are pertinent for the quantitative and qualitative phases of the study: a) exploration, b) description, c) explanation, d) prediction, and/or e) influence. The first type of research objective, exploration, comprises inductive methods to figure out an opinion, argument and the like. In this way, it brings about intuition, hypotheses, deductions or generalization. Description, the second type, means clarifying and defining the previous experiences, relations, and the nature of the incident. One should develop or broaden a theory to achieve ‘explanation’ to find out the phenomena. Another type of research, prediction, enables the researcher to predict future events by means of previous knowledge. The fifth type of objective, influence, includes ‘the manipulation of a variable or construct’ so as to achieve an outcome.

Participant enrichment means making sampling better by blending quantitative and qualitative research approaches in order to select eligible participants for the study. It involves blending quantitative and qualitative research to better the sample using approaches that encompass recruiting participants, dealing with activities such as institutional review board interviews assuring that each participant chosen is eligible for involvement.

The researchers can use the convergent design in order to make people understand the topic more completely or validate the quantitative scales. While the explanatory design is being used to explain quantitative results, the exploratory design is selected to test qualitative exploratory findings. The embedded design is preferred in experimental study for preliminary exploration or follow-up explanation or more complete understanding of the experimental trial. On the other hand, in case of the social injustice, the transformative mixed methods research is conducted to describe the phenomonolgy. Lastly, multiple phase design is implemented to develop, adapt or evaluate program, multilevel statewide studies.

Convergent design can be explained as:

Identical sampling: Exactly the same participants are involved in both the and qualitative phases of the study.

Parallel sampling: Sample for the quantitative and qualitative components of the study are different but are drawn from the same population of interest.

Nested sampling: Sample members selected for one phase of the study represent a subset of those sample members selected for the previous phase of the research.

Multilevel sampling: Use of two or more sets of samples that are extracted from different levels of the population of interest.

Purposeful sampling is not specific to mixed methods research.

an interpretive framework which includes ten aspects of quality is drafted by Teddlie and Tashakkori (2009, pp. 301-302):

Appropriateness of design (i.e. ‘Are the methods of study appropriate for answering the research questions?’; ‘Does the design match the research questions?’; ‘Does the mixed methods design match the stated purpose for conducting an integrated study?’; ‘Do the strands of the mixed methods study address the same research questions [or closely related aspects of the research question]?’).

Reliability of design (i.e. ‘Are the qualitative, quantitative, and mixed methods procedures or design components... capturing the meanings, effects, or relationships?’).

Coherence inside the design (i.e. ‘Do the components of the design t together in a seamless manner?’; ‘Do the strands of the mixed methods study follow each other [or are they linked] in a logical and seamless manner?’).

Analytical competence (i.e., ‘Are the data analysis procedures/strategies appropriate and adequate to provide possible answers to research questions?’; ‘Are the mixed methods strategies implemented effectively?’).

Analytical cohesion (i.e., ‘Do the inferences closely follow the relevant findings in terms of type, scope, and intensity?’; ‘Are multiple inferences made on the basis of the same findings consistent with each other?’).

Conceptual coherence (i.e., ‘Are the inferences consistent with theory and state of knowledge in the eld?’);

Explicative concurrence (i.e., ‘Are other scholars likely to reach the same conclusions on the basis of the same results?’; ‘Do the inferences match participants’ constructions?’).

Analytical uniqueness (i.e., ‘Is each inference distinctively more credible/plausible than other possible conclusions that might be made on the basis of the same results?’).

Combining efficiency (i.e., ‘Do the meta-inferences adequately incorporate the inferences that are made in each strand of the study?’; ‘If there are credible inconsistencies between inferences made within/across strands, are the theoretical explanations for these inconsistencies explored, and possible explanations offered?’).

Exploratory agreement (i.e., ‘Do the inferences correspond to the stated purpose/questions of the study?’; ‘Do the inferences made in each strand address the purposes of the study in that strand?’; ‘Do the meta-inferences meet the stated need for using a mixed methods design?’)

Onwuegbuzie and Teddlie (2003) present a seven-stage conception of the mixed methods data analysis process within the MMR model. These stages are respectively data reduction, data display, data transformation, data correlation, data consolidation, data comparison, and data integration. Data display, the second stage, comprises defining the qualitative data visually through charts, graphs, lists etc. and quantitative data through tables and graphs.