What Are the Benefits of Using Mixed Research Methods?

Many research questions cannot be fully answered by either quantitative or qualitative methods alone. Mixed methods studies use both qualitative and quantitative components and can be a good approach to more fully explore your research question. Mixed methods draws on the benefits of each type of research: quantitative research can answer questions about breadth – how many, to what extent, how often – while qualitative research can provide depth, answering questions on the why and how.

Approaches to mixed methods

Mixed methods studies can use several approaches, which are summarised below.

Explanatory Approach

Qualitative research is conducted after quantitative research. It is used to explain the reasons behind quantitative findings.

Example

AIM: To assess how school enrolment differs between children with and without disabilities in Nepal.

QUANT: We first conduct a survey of children of school-going age that includes questions on disability and school enrolment. We disaggregate our data by disability, and find that children with disabilities are much less likely to attend school (70% vs 90% for children with disabilities, p=0.01).

QUAL: We then conduct in-depth interviews with girls and boys with disabilities and their parents to explore factors that affect their decisions to enrol in school.

ANALYSIS: Qualitative data is used to explain the quantitative data.

Exploratory Approach

Qualitative research is conducted before quantitative research. This can be used when little is known on the topic and to inform the design of the quantitative research tools.

Example

AIM: To identify the main reasons people with disabilities are not working in Nairobi, Kenya.

QUAL: We first conduct focus group discussions with men and women with different impairments to identify common reasons why they are not working.

QUANT: Qualitative data is then used to create a questionnaire, which is administered as part of a survey on work amongst people with disabilities.

ANALYSIS: Qualitative data informs the development of quantitative data collection tools. The quantitative data then can be compared with the qualitative data.

Parallel Approach

Qualitative and quantitative data are collected and analysed at the same time. Analysis focuses on comparing and contrasting findings from the two methods.

Example

AIM: To explore the risk of violence amongst children with disabilities in Uganda.

QUANT: We conduct a survey of children of school-going age about if they have experienced different types of violence.

QUAL: We also conduct in-depth interviews with girls and boys with different impairment types and their caregivers. These interviews explore in greater detail any experiences of violence and safety concerns expressed by the children and their caregivers.

ANALYSIS: Findings from the qualitative and quantitative are compared. What are the similarities, what are the differences? Quantitative research can help us explore the extent to which children with disabilities are at risk of violence, while the qualitative can explore reasons why children are at risk of violence.

*Adapted from Shorten & Smith (2017)

Using mixed methods to explore access of people with disabilities to social protection in Nepal, Vietnam and the Maldives

We used parallel mixed methods to explore access to cash transfer programmes amongst people with disabilities in Nepal, Vietnam and the Maldives. In each of these settings, people with disabilities can receive a monthly cash transfer, called a “Disability Allowance”. To enrol in the Disability Allowance, people with disabilities must submit an application and undergo an assessment of disability by either medical professionals or programme staff.

To start, we wanted to know how many people with disabilities are enrolled in the Disability Allowance (programme coverage). We used quantitative methods to answer this question. We conducted surveys in each setting to measure the prevalence of disability in the population and then asked people identified as having a disability if they were enrolled in the Disability Allowance. We found that the proportion of people with disabilities enrolled in the Disability Allowance was 25% in the Maldives (national), 40% in Vietnam (Cam Le district) and 13% in Nepal (Tanahun district). We can see from these figures that many people with disabilities who are eligible for the Disability Allowance are not enrolled.

We also wanted to explore reasons why people with disabilities were not enrolled in the Disability Allowance. We used both quantitative and qualitative methods to identify common reasons why people with disabilities were not enrolled in the Disability Allowance. For the quantitative, we included questions on reasons for not receiving the Disability Allowance that were asked to all people with disabilities who reported that they were not receiving the Disability Allowance in the original survey. The questionnaire options for reasons not enrolled were based on a review of the literature. For the qualitative, we conducted in-depth interviews with 20-30 people with disabilities in each setting who were and were not enrolled in the Disability Allowance to understand factors that affected their decision to apply and the challenges they encountered during the application process.

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Figure 1: Reasons for not receiving the disability allowance in in the qualitative study

We then compared the quantitative and qualitative findings on reasons why people with disabilities were not enrolled in the Disability Allowance. We found some similarities in reasons between the two methods. For example, both components found people experienced difficulties with the application process, particularly the medical assessment, which prevented them from registering. Here, the qualitative research revealed much more detail about this challenge such as:

• People had to travel far distances to reach the offices where assessments were conducted and transport was not always available or accessible in certain regions.
• People had to spend large amounts of money on transport and accommodations to go for assessments, and for gathering the medical documentation they needed to support their application.
• People had misconceptions about eligibility criteria (e.g. types of impairments covered) and so wrongly thought they could not apply.

The qualitative research also identified other reasons for not applying that were not captured in the quantitative, such as not applying due to stigma of identifying as a person with a disability and being discouraged from applying by programme staff.

The use of mixed methods greatly improved the utility of the study findings. If we had only used quantitative methods, we would know how many people with disabilities were enrolled in the Disability Allowance, but we would have had only limited knowledge on why people with disabilities weren’t enrolled. Not knowing in detail the reasons why people with disabilities aren’t enrolled makes it very difficult to design interventions to address the gaps in coverage. In contrast, if we had only used qualitative research, we would have good evidence on reasons why people with disabilities do not enrol. However, without data on programme coverage, we might find it difficult to convince policymakers and programme staff that the challenges we report are widespread and can affect overall enrolment.

Questions to consider:

• Based on the qualitative responses highlighted in Figure 1, how might you change the quantitative survey tool if you were to repeat the survey?
• Why do you think so few people used the “other” option in the survey, even though the qualitative research revealed there were many other or more complex reasons for not applying?
• How would your advice to planners of the Disability Allowance differ if you only had the quantitative data and not the qualitative? What are the benefits of having both types of data in making your recommendations?