MM notes

 #STRATEGIES TO PERFORM A MIXED METHODS STUDY Almeida (2018).

Cresswell & Clark (2011) that state “mixed methods research is a research design (or methodology) in which the researcher collects, analyzes, and mixes (integrates or connects) both quantitative and qualitative data in a single study or a multiphase program of inquiry”

The classical approach to categorize mixed methods designs organizes them into two major categories (Creswell et al., 2003): sequential and concurrent. The sequential 

design organizes the process into two stages: in an initial stage, either the qualitative or quantitative data are collected; then, in a second stage, other data type is collected. On the other side, in concurrent design establish that all both types of data are collected during the same stage.

One of the main reference works on mixed methods 

research was presented by Creswell & Clark (2007), which introduce the phases in the process of mixed methods research and propose four specific mixed methods designs, respectively:

 Triangulation design – it is the most common and well-known approach. It has the purpose to obtain different but complementary data on the same topic. The interpretation is based on Quantitative (QUAN) and Qualitative (QUAL) results. 

Different types of mixed method evaluations can be used, such as different conceptual frameworks, different methods of data collection, different interviews, different times or different locations and contexts (Bamberger, 2012);

 Embedded design – this approach assumes that a single data set is not sufficient and, therefore, it is required to use different types of data. It is established the 

concept of primary data, which may be qualitative or quantitative, and a secondary role assumed by other data type. Cronholm& Hjalmarsson (2011) state that configuration type (QUAL -> QUAN) is preferred when there is a low 

pre-knowledge of the studied phenomenon. Additionally, Hughes (2016) refers that embedded exploratory design is adequate for testing emergent theory because both types of data are interpreted during the data integration phase;

 Explanatory design – it is a two-phased approach, in which the qualitative data helps explain or build upon initial quantitative results. This design has a strong quantitative orientation because quantitative data is the key element to start the process;

 Exploratory design – it is similar to the explanatory design approach, but in which the qualitative data is the primary source of information. This design is particularly suitable for exploring a phenomenon, in which there isn’t a guiding framework or theory and measures or instruments are not available.

The classical approach has been extended and combined by Johnson et al. (2007) and Bergman (2008) that suggest six design approaches based on sequential and concurrent approaches:

Sequential explanatory design – quantitative data is collected in a first instance followed by qualitative data collection;

 Sequential exploratory design – similar to previous approach, but in which qualitative data is collected first;

 Sequential transformative design – the order of data collection is determined by the theoretical perspective of the researcher. Both methods are integrated during the interpretation phase;

 Concurrent triangulation design – this approach uses concurrently and simultaneously the qualitative and quantitative approach. Koskey & Stewart (2013) advocate that this approach is particularly useful for decreasing the 

implementation time, but presents low flexibility and learning potential regarding the results obtained by the individual execution of each one of them. Bryman (2006) also employs the “parallel” term to define a concurrent approach;

 Concurrent nested/embedded design – similar to previous approach but in which priority is given to one approach that guides the project;

 Concurrent transformative design – similar to the sequential transformative design, but in which both methodological choices are executed concurrently.

 Traditionally, mixed methods research considers the existence of one qualitative and another quantitative study, independently of the order. However, Onwegbuzie & Collins (2007) extends this vision by proposing the use of three or 

more qualitative and quantitative studies, which originated the multiphase or iterative mixed methods design. 

Another relevant contribution is given by Ponce & Pagán-Maldonado (2015) that present three mixed methods design frameworks, respectively:

 Convergence design – it is used to study a problem in its entirety and dimension. It uses two parallel phases: the quantitative approach is used to measure the properties and objective aspects of the problem; while the qualitative approach is applied to understand and describe the subjective aspect. Hughes (2016) advocates that this approach allows the researcher to examine phenomena on several different levels;

 Complementary design – this approach is very similar to the embedded design, in which one of the research methodologies is used to counter the deficiencies of the other. There is also the notion of primary data that may be quantitative or 

qualitative. Greene (2007) also refers to this model as an integrative design, in which the limitations of the first methodology are pointed out during the design process;

 Multilevel design – this approach assumes that the problem has several dimensions, manifestations or ramifications. Consequently, it requires the use of different samples and the adoption of different research approaches to understand and decrypt it. Baran & Jones (2016) employ the “multi-layered” 

term to refer the use of mixed methods in multi-dimensional problems. This approach uses more than two qualitative and quantitative studies like in the multiphase design.

This study has identified and synthesized the mixed methodologies in ten design approaches that globally fall into four major groups: (i) sequential approaches; (ii) concurrent approaches; (iii) multiphase design; and (iv) multilevel design.

#Six strategies for mixing methods and linking data in 

social science research - Mason (2006)

It explores six broad strategies that can underpin the 

mixing of methods and linking of different forms of data, be they qualitative, quantitative, or spanning this divide.

For those with a quantitative orientation, the ‘big picture’ gained through quantitative means may be rigorous, and based on representative or statistical forms of sampling and analysis, yet also feel superficial or lacking in ‘real life’ 

resonance. From that perspective, the use of selected qualitative approaches – for example in the form of in-depth case studies – can be illustrative and evocative, and provides a more close-up view. Conversely, for a researcher with 

a primarily qualitative orientation, which focuses on social processes in rich and proximate detail, the inclusion of some background quantitative material, perhaps in the form of local or national demographic data, can help in making the research part of a bigger set of observations. 

From blog:

Sequential mixed methods research designs enable the systematic collection and analysis of both quantitative and qualitative data in distinct phases, each informing and enriching subsequent stages of the research (Creswell & Plano Clark, 2018).

Surveys are effective in generating generalisable data across a broad population and can reveal patterns or trends (Bryman, 2016).

Focus groups allow for the in-depth exploration of participants' views and facilitate interaction that can surface new ideas or concerns that may not emerge through individual interviews (Kitzinger, 1995).

Finally, a pilot study involving the teaching of digital accessibility awareness to pupils at upper Key Stage 2 (ages 9–11) provides a practical test of curricular feasibility and pedagogical effectiveness. This phase serves as a form of design-based research, enabling iterative refinement of teaching methods based on pupil engagement and understanding (Barab & Squire, 2004).

The sequential order—quantitative → qualitative → intervention—is methodologically appropriate because each phase addresses a distinct but interrelated research objective, with earlier phases informing the development and design of later stages. This structure ensures that interventions are evidence-based and contextually grounded (Ivankova, Creswell, & Stick, 2006).

Given the exploratory nature of integrating a relatively novel topic—digital accessibility awareness—into an established curriculum, a mixed methods approach allows the research to remain flexible yet rigorous. It supports both breadth (through the survey) and depth (through the focus group and pilot study), which is vital in educational research addressing emerging digital literacies (Johnson & Onwuegbuzie, 2004). Furthermore, it aligns with the pragmatic paradigm, prioritising methodological pluralism to solve real-world educational problems (Tashakkori & Teddlie, 2003).