with the nature of truth, i.e., what is the nature of reality? whereas epistemology refers to the nature and forms of human

knowledge, i.e., how do we know what reality is (Cohen et al., 2007). A researcher, based on their purpose, may adapt

different approaches to uncover the truth and/or knowledge. Mixed-methods research (MMR) is a research methodology that

incorporates multiple methods to address research questions in an appropriate and principled manner (Bryman, 2012;

Creswell, 2015; Creswell & Plano Clark, 2011), which involves collecting, analysing, interpreting and reporting both

qualitative and quantitative data.

characteristics, reasons for using it, and its major types. The language and organisation of the article are deliberately simple

to assist researchers to understand what different types of MMR approaches there are, how to decide which type of MMR is

appropriate for their research study, and what the key considerations are when choosing a mixed-method design.

Additionally, the chapter provides an understanding of practical considerations and the potential challenges a researcher is

likely to experience when adopting a particular MMR design. What follows, then, is a brief discussion of major research

paradigms followed by an introduction to mixed-methods research, its types, key considerations, and challenges.

2. Major Research Paradigms

was guided by quantitative research design or the positivistic approach. Post-positivism, on the other hand, “is a milder form

of positivism that follows the same principles but allows more interaction between the researcher and his/her research

participants” (Taylor & Medina, 2011, p. 3). While positivism focuses on the objectivity of the research process, post-

positivism has room for subjectivity as well. Therefore, it uses both quantitative (such as a survey) and qualitative methods

(such as interviews and participant-observation).

the followers of this paradigm are critical of the application of the scientific (or positivist) model to the study (Bryman,

2012). The social scientists who are guided by this paradigm respect the subjective meaning of social action (Taylor &

Medina, 2011). Interpretivists, as a consequence of that, understand social phenomena and interpret them further. Since the

qualitative researchers use the tools such as interviews, focus groups, and participant observation to understand the situation

Therefore, it posits that scientific investigation should be conducted with a noble goal of social change. The primary purpose

of research is to identify and support resolve 'gross power imbalances' in society (Taylor & Medina, 2011). Thus, in this

paradigm, “the researcher’s role is one of advocate, a change agent, who argues for and leads the way towards a more

equitable, fair and sustainable society” (Taylor & Medina, 2011, p. 6). To sum up, the two main paradigms, which are

conventionally considered to be fundamentally opposed to each other, are positivism/post-positivism and

constructivism/interpretivism, the former relates to quantitative methodology whereas the latter drives qualitative research.

The qualitative research emerged as the quantitative research alone could not address all the research questions.

philosophical stance (Creswell, 2007) but argues that the forced choices between positivism and interpretivism should be

abandoned as it views reality as both singular and multiple. Pragmatism “is pluralistic and oriented towards ‘what works’ and

study what interests them and are of value to them. They study research problems in different ways that they deem

appropriate. Therefore, the main reason for adopting a pragmatist position in a study is to allow a researcher to have a

pluralistic stance of gathering all sorts of data in order to best answer the research questions. In essence, a pragmatist

employs a mixed-methods design to follow one or multiple combinations of some of the prevalent research paradigms

mentioned above. In a mixed-methods research design, qualitative research approaches help understand the situation through

indicative results by exploring through the tools like participant observation and interviews whereas quantitative approaches

3. Mixed Methods as a Research Methodology

mixed-methods research design is a research design that has its own philosophical assumptions and methods of inquiry. As a

methodology, it includes philosophical assumptions to provide directions for the collection and analysis of data from multiple

sources in a single study.

philosophical frameworks of both post-positivism and interpretivism (Fetters, 2016) interweaving qualitative and quantitative

data in such a way that research issues are meaningfully explained. It also offers a logical ground, methodological flexibility

and an in-depth understanding of smaller cases (Maxwell, 2016). In other words, the use of mixed-methods enables

researchers to answer research questions with sufficient depth and breadth (Enosh, Tzafrir, & Stolovy, 2014) and helps

generalise findings and implications of the researched issues to the whole population. For example, the quantitative approach

helps a researcher to collect the data from a large number of participants; thus, increasing the possibility to generalise the

combining two sets of strengths while compensating at the same time for the weaknesses of each method (Johnson &

Onwuegbuzie, 2004). Consequently, "mixed-method research designs are becoming increasingly relevant to addressing

impact research questions” (Saville, 2012, p.7).

2016; Morgan, 2014; Tashakkori & Teddlie, 1998) around the theory of mixed-methods, and on the breadth and depth of this

design. However, it seems that there is very limited literature on a mixed-methods research design that can effectively guide

early career researchers through selecting a proper design for their study thereby enabling them to understand its rationale.

Despite its merits and popularity among researchers, some scholars might consider it as a design that can potentially cause a

lot of troubles to a researcher when they plan to organize both qualitative and quantitative methods in a study as a researcher

may not be equally capable of handling both methods. In the following sections, then, reasons for selecting mixed-methods

4. Why Mixed Methods?

integrate and synergize multiple data sources which can assist to study complex problems (Poth & Munce, 2020). The

application of MMR, as mentioned in the previous section, means purposeful data consolidation which allows researchers to

seek a wide view of their study by enabling them to view a phenomenon from different perspectives and research lenses

(Shorten & Smith, 2017).

employing an MMR approach is the expansion of study. This means an MMR approach allows researchers widen their

inquiry with sufficient depth and breadth. For instance, when a researcher wants to generalize the findings to a population

and develop a detailed view of the meaning of a phenomenon or concept for individuals, the advantages of collecting both

closed-ended quantitative data and open-ended qualitative data support understanding a research problem (Creswell, 2003).

Furthermore, qualitative data (such as interviews and focus groups) can provide depth in the research inquiry as the

conclusion (Maxwell, 2016; Morgan, 2014). In other words, mixing two methods helps to produce a more complete picture

and provides an opportunity for a greater assortment of divergent or complementary views; which are valuable as they not

only lead to extra reflection and enrich our understanding of a phenomenon, but also open new avenues for future inquiries

(Teddlie & Tashakkori, 2009). Additionally, findings from mixed-methods research offer a holistic view of a phenomenon

and provide additional insights into different components of a phenomenon which might help for generating substantive

theories (Ventakesh et al., 2013).

study of smaller samples which helps to study the process and change.

strengths of the qualitative methods offset the weaknesses of the quantitative methods and vice versa (Plano Clark &

Ivankova, 2016). This implies that a quantitative method can be strong in those areas where a qualitative method is weak and

vice versa. Putting it in another way, one method is more suitable to answer one type of question and another method is more

suitable for another type of question. Mixing the two methods, therefore, offers the possibility of combining two sets of

strengths while compensating at the same time for the weaknesses of each method. Thus, the combination of quantitative and

qualitative methods is often proposed on the grounds that a researcher can utilize the respective strengths, escape the

respective weaknesses of the two approaches and produce a more accurate conclusion.

promotes the credibility of inferences obtained from one approach (Ventakesh et al., 2013).

results from one method (qualitative or quantitative) to inform or shape the use of another method (qualitative or

quantitative)” (Plano Clark & Ivankova, 2016, p. 86). For instance, researchers who want to understand possible factors that

cause obesity in children might argue for the need to quantitatively assess significant predictors and then they use the

quantitative results to develop qualitative follow-up exploration (potentially through interviews, observation, and focus

groups) to explore why certain factors were significant. This means the development of a new method based on the previous

method is possible only in a (mixed-methods) sequential design. The following section elucidates fundamental considerations

when developing a sequential (MMR) design.

5. Key Considerations

participants. A study can have three priority options: quantitative priority (i.e., more emphasis on the quantitative data

collection and analysis), qualitative priority (i.e., more emphasis on the qualitative data collection and analysis), or equal

priority (i.e., considering both data sets to be equally important to answer the research questions) (Plano Clark & Ivankova,

2016). A researcher, then, must weigh carefully the purpose of their research and the data they need to address it before

prioritising research approaches.

and quantitative approaches “are kept independent or interact with each other” (Creswell & Plano Clark, 2011, p. 64). When

they are independent, the researcher mixes the two approaches only at the final stage, i.e., after the analysis of the data. As

one of the purposes of using mixed methods methodology in a study is to obtain different but complementary data on the

same issue to best understand the research problems, the data can be collected separately, and the findings can be mixed

qualitative and quantitative strands. The sample participating in a qualitative strand can be a subset of the participants who

participate in the quantitative study. The researcher should also be aware of the issue that it will bring complexity in the

merging process while analysing and interpreting the data. And since one of the purposes is also to synthesize different

results into a complementary picture of the issue being explored (Creswell & Plano Clark, 2018), the size differential should

not be a big issue. Creswell and Plano Clark (2018) state that having a small size in qualitative component and larger size in

quantitative component supports researchers to get in-depth qualitative exploration and rigorous quantitative examination of

the issue.

study. Once a researcher decides to use mixed-methods as a design, they need to delve deeper into deciding which mixed

methods design is appropriate. The following section introduces core mixed-methods designs and lists the challenges of each

6. Which Mixed-Methods Design?

separated from their native epistemology to work, it is necessary to be within a qualitative and quantitative research approach

which will also be indicated by the data they use. While acknowledging merits in the Timans et al.'s views, this article is

based on the premise that the research-novices need to treat the mixing of methods as one research approach as keeping them

epistemologically separate within MMR may create complications at the data integration and interpretation stage. This

section, therefore, presents core common mixed-methods research types which are prevalent in the field of research. Several

scholars (Plano Clark & Ivankova 2016; Terrell, 2012; Wilkinson & Staley, 2019) have listed various types of mixed-

methods research design. Creswell and Plano Clark (2018) consider these core designs as parsimonious and practical since

they have the potential to make researchers understand the best possible options of mixed methods research designs. In this

section some common types of mixed methods are presented. Attempts have been made to illustrate the MMR types with

suitable examples.

of both data will help a researcher gain a complete understanding of the one provided by the quantitative or qualitative results

alone. It is an approach in which two data sets are combined to get a complete picture of the issue being explored and to

validate one set of findings with the other (Creswell and Plano Clark, 2018). For instance, if a researcher is examining

experiences of using digital technologies in education, s/he administers a survey and also conducts interviews with teachers

and students to understand the issue. S/he collects quantitative data from a survey and qualitative data from interviews and

examines if the findings obtained from these two different data sets converge or diverge. In case the results diverge, the

researcher explains the finding by re-examining the results and collecting more data, or explaining the quality of the dataset.

"The intent of integration in a convergent design is to develop results and interpretations that expand understanding, are

comprehensive and are validated and confirmed" (Creswell & Plano Clark, 2018, p. 221). Fàbregues et al. (2020) argue that

convergent studies are apt designs for integration as both data results are available when interpretation is planned.

when a researcher has limited time; when s/he needs both qualitative and quantitative information from the participants and

when s/he (or a team of researchers) has required skills to handle both quantitative and qualitative methods. They also list

issues of different sample sizes, the need to merge a text and numeric database and the need to explain divergence when

comparing results as the challenges of convergent design. Firstly, in this design, a researcher needs to think of the possibility

and outcomes of having different sample sizes when quantitative and qualitative data are collected for generalization and

Education Examination English Test on students (aged 15-16 years old) and their parents in Nepal. In the study, the data was

collected through a longitudinal survey (n=247) with students, oral diaries recorded by six students intermittently for three

months (n=72) and interviews with those six students and their parents (n=24). The study used concurrent timing meaning

both quantitative and qualitative data were collected at the same time, but independent of each other. The author argues that

this timing was used to ensure that there was no chance that one approach influenced another approach as in a sequential

design. Therefore, both qualitative and quantitative data in the study were collected concurrently but analysed separately, and

the findings were mixed before interpreting the results. This means that the two data sets were combined during the data

interpretation phase only. Dawadi argues, "If the quantitative data had been analysed first, the qualitative findings might

have been affected by the quantitative results, but the reverse was not possible" (p. 66). It is also worth pointing out that

equal priority was given to both data sets considering the equal importance of both types of data in answering the research

2017; Wisdom & Creswell, 2013). In this design, a researcher follows up on a specific quantitative finding and explains it

with the qualitative data (Wisdom & Creswell, 2013). For example, once the significant predictors are identified through

statistical measures, such as it is found that pandemic is one of the reasons that has made teachers and learners use digital

technologies profusely, an interview is designed to delve deeper and explain this predictor. In this design, the qualitative

design helps explain certain quantitative results that include unexpected findings in more detail (Terrell, 2012). Creswell and

Plano Clark (2018) suggest that a researcher should shift from postpositivist to constructivist theoretical assumption when

this design is adopted in a study. The researcher follows the postpositivist assumption to select instruments and moves to

constructivist assumption as they value multiple perspectives and in-depth exploration (Creswell & Plano Clark, 2018).

will be explained is decided. Secondly, a qualitative strand is designed and developed to explain the quantitative findings.

Wilkinson and Staley (2019) in their study found that sampling was one of the problems associated with this design. They

pointed out that the reviewers of the research papers, which they analysed, were concerned with "how well the sample for the

qualitative component represented the phenomenon identified in data from a larger sample of participants and analysed in the

quantitative portion of the study" (p. 70). Creswell and Plano Clark (2018) enumerate the extended time needed for

completion, the complexity in specifying the qualitative phase in advance, the compulsion for the identification of

quantitative results to be followed up, and the need to specify the participants who can provide the explanation as challenges

of this design. In this design, a researcher needs to spend too much time to implement two phases and a researcher might face

difficulty to get approval from the institutional board since it will be challenging for a researcher to specify the qualitative

phase beforehand. A researcher also needs to decide the quantitative results to be followed on, and they also need to decide

who to study and what will be the criteria for sampling (Creswell & Plano Clark, 2018). The following is an example of the

group discussion questions/prompts were guided by the survey results. It is worth pointing out that the connection between

the two data sets happened in two places:

the first phase, a researcher explores an issue in-depth, and as they reach the second phase, they shift to the post-positivist

principle to identify and measure the variable and statistical trend (Creswell & Plano Clark, 2018). In this design, at first, the

qualitative data are gathered and analysed, and later quantitative data are collected and tested (Schoonenboom & Johnson,

2017; Shorten & Smith, 2017). This design begins with the collection and analysis of qualitative data. Building from the

qualitative findings, quantitative measures or instruments are developed (Terrell, 2012; Wisdom & Creswell, 2013), and

finally, a researcher quantitatively tests the variable that they have identified and interprets in what ways the quantitative data

generalizes and extends the qualitative findings (Creswell & Plano Clark, 2018). For example, once a researcher identifies

variables that have a role in promoting the use of digital technologies in education, they design the research instruments to

check if those variables are prevalent in a large mass as well. As far as the integration of this design is concerned, it begins

(Creswell & Plano Clark, 2018). They further claim that it is straightforward to implement and describe and it makes the

qualitative result acceptable to quantitative-biased audiences since it combines the quantitative component. They argue that

in this design, a researcher can develop a new instrument during the research process. They also list challenges as a

compulsion to plan for an extended time to complete, a necessity to tentatively specify the quantitative phase in advance, a

prospective requirement of the identification of two different samples, the necessity to determine the qualitative results to

use, and the requirement of the skillful researcher. This research design requires a lot of time for a researcher to complete a

study and as in the explanatory sequential design, as s/he needs to decide tentatively quantitative phase for the institutional

board review which is challenging. A researcher should use a small, purposeful sample in the first phase and a large sample

in the second phase to have the extended finding which is difficult too. Next, s/he needs to decide the qualitative result which

will be used to build the quantitative measure, and s/he must be skilled and proficient in qualitative, quantitative, mixed

used to designing a survey, and the survey was administered. Phase III collected quantitative data via a survey with 99

participants, and both the quantitative and qualitative findings were merged (especially integrated) in the final phase. The

authors argue that the design “provided the opportunity to identify complementarity, convergence, and/or divergence” (p.

45). Figure 1 summarises different phases of the study.

several threats. Researchers, such as David et al. (2018); Dawadi (2019); and Fauser (2018), have pointed out some practical

difficulties associated with mixing qualitative and quantitative components which have been summarised here as five major

challenges. First, data collection and analysis might be a very lengthy process. Therefore, it might be more expensive in

terms of cost and time. Researchers usually struggle in designing their research within their estimated time and budgets

(Fauser, 2018; Hauken et al., 2019). Some researchers indicate that the timeline of recruitment is demanding and the labor in

data collection is intensive (David et al., 2018; Linnander et al., 2019).

For instance, Dawadi (2019) pointed out that she was not confident about her approach in bringing together quantitative

(survey) and qualitative data (interviews and oral diaries). Casey et al. (2016) had a similar experience as they were unsure

about a strategy to integrate their data sets. They also indicate that existing literature provides sparse guidance on how to

Hammersley (1996), for example, states that qualitative and quantitative research paradigms are “founded on

incommensurable philosophical and/or political presuppositions'' (p. 2). Indeed, a quantitative approach, which is guided by

positivism, envisions the world mostly as static but for a qualitative approach, which is guided by interpretivism, the reality is