qualitative and descriptive research data type versus data analysis

• DOI: 10.1177/1362168815572747
• Corpus ID: 145694704

Qualitative and descriptive research: Data type versus data analysis

• Hossein Nassaji
• Published 1 March 2015
• Education, Linguistics
• Language Teaching Research

912 Citations

Trends in qualitative research in three major language teaching and learning journals, 2006–2015, teachers’ strategies in esp learning program in indonesia: a comparative study, application of instructional design models by prospective teacher students, facilitation strategies in online group work from the perspective of community of inquiry, analysis of science subject evaluation instruments in elementary schools based on stimulus types and cognitive process dimensions, introduction to educational research: quantitative and qualitative approach, exploring science learning anxiety in the new normal - an exploratory factor analysis, exploring accounting lecturers’ use of feedback as a teaching practice: a case of a south african university, teachers’ perception of the english curriculum change in costa rica: the action-oriented approach, client and therapists’ subjective understanding of an ideal therapy room: a divergent reflection of experience, 3 references, educational research: an introduction, educational research: a contextual approach, a concise introduction to mixed methods research, related papers.

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Qualitative and descriptive research: Data type versus data analysis

2015, Language Teaching Research

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Research plays a very important role in making sense of the world around us and developing knowledge basis and systems. As such, understanding research methods and paradigms is very important to scholars and researchers if they are to come up with credible and comprehensive research. This paper discusses the differences between qualitative and quantitative research methods and also looks at how the two methods can be similar and how they can be used together. Qualitative research is a scientific and systematic method used to gather data that it not quantifiable (Yin 2018). This type of research, as Marshall (2016)) explains, "refers to the meanings, concepts definitions, characteristics, metaphors, symbols, and description of things". Therefore, as a research method, qualitative research is also used to unearth new trends in thought processes and actions, how people feel about specific circumstances and to get to the heart of issues and how they affect people (Wolcott 2016). Marshall (2016) emphasizes that qualitative research is primarily exploratory research and is used to obtain information such as intentions and motives that helps explain an occurrence. Thus, qualitative research methods help to understand new occurrences or trends and then helps to explain why such things are happening or occurring. Quantitative Research is used to quantify the problem by way of generating numerical data or data that can be transformed into useable statistics (Lichtman 2017). According to Wolcott (2016) quantitative research is used to quantify attitudes, opinions, behaviors, and other defined variables and generalize results from a larger sample population. Thus, quantitative Research uses measurable data to formulate facts and uncover patterns in research to make sense or deductions on how things have unfolded. Therefore, where qualitative data seeks to understand a phenomenon, quantitative methods seek to quantify them and identify variables that can be measured. Qualitative research uses data collecting methods that often require the direct participation of the researcher to gather data and information crucial to the study. Morgan (2017) notes that

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An overview of the qualitative descriptive design within nursing research

Louise doyle.

Associate Professor in Mental Health Nursing, School of Nursing and Midwifery, Trinity College Dublin, Ireland

Catherine McCabe

Associate Professor in General Nursing, School of Nursing and Midwifery, Trinity College Dublin, Ireland

Brian Keogh

Assistant Professor in Mental Health Nursing, School of Nursing and Midwifery, Trinity College Dublin, Ireland

Annemarie Brady

Chair of Nursing and Chronic Illness, School of Nursing and Midwifery, Trinity College Dublin, Ireland

Qualitative descriptive designs are common in nursing and healthcare research due to their inherent simplicity, flexibility and utility in diverse healthcare contexts. However, the application of descriptive research is sometimes critiqued in terms of scientific rigor. Inconsistency in decision making within the research process coupled with a lack of transparency has created issues of credibility for this type of approach. It can be difficult to clearly differentiate what constitutes a descriptive research design from the range of other methodologies at the disposal of qualitative researchers.

This paper provides an overview of qualitative descriptive research, orientates to the underlying philosophical perspectives and key characteristics that define this approach and identifies the implications for healthcare practice and policy.

Methods and results

Using real-world examples from healthcare research, the paper provides insight to the practical application of descriptive research at all stages of the design process and identifies the critical elements that should be explicit when applying this approach.

Conclusions

By adding to the existing knowledge base, this paper enhances the information available to researchers who wish to use the qualitative descriptive approach, influencing the standard of how this approach is employed in healthcare research.

Introduction

Qualitative descriptive approaches to nursing and healthcare research provide a broad insight into particular phenomena and can be used in a variety of ways including as a standalone research design, as a precursor to larger qualitative studies and commonly as the qualitative component in mixed-methods studies. Despite the widespread use of descriptive approaches within nursing research, there is limited methodological guidance about this type of design in research texts or papers. The lack of adequate representation in research texts has at times resulted in novice researchers using other more complex qualitative designs including grounded theory or phenomenology without meeting the requirements of these approaches ( Lambert and Lambert, 2012 ), or having an appropriate rationale for use of these approaches. This suggests there is a need to have more discussion about how and why descriptive approaches to qualitative research are used. This serves to not only provide information and guidance for researchers, but to ensure acceptable standards in how this approach is applied in healthcare research.

Rationale for qualitative descriptive research

The selection of an appropriate approach to answer research questions is one of the most important stages of the research process; consequently, there is a requirement that researchers can clearly articulate and defend their selection. Those who wish to undertake qualitative research have a range of approaches available to them including grounded theory, phenomenology and ethnography. However, these designs may not be the most suitable for studies that do not require a deeply theoretical context and aim to stay close to and describe participants’ experiences. The most frequently proposed rationale for the use of a descriptive approach to is to provide straightforward descriptions of experiences and perceptions ( Sandelowski, 2010 ), particularly in areas where little is known about the topic under investigation. A qualitative descriptive design may be deemed most appropriate as it recognises the subjective nature of the problem, the different experiences participants have and will present the findings in a way that directly reflects or closely resembles the terminology used in the initial research question ( Bradshaw et al., 2017 ). This is particularly relevant in nursing and healthcare research, which is commonly concerned with how patients experience illness and associated healthcare interventions. The utilisation of a qualitative descriptive approach is often encouraged in Master’s level nurse education programmes as it enables novice clinical nurse researchers explore important healthcare questions that have direct implications and impact for their specific healthcare setting (Colorafi and Evans, 2016). As a Master’s level project is often the first piece of primary research undertaken by nurses, the use of a qualitative descriptive design provides an excellent method to address important clinical issues where the focus is not on increasing theoretical or conceptual understanding, but rather contributing to change and quality improvement in the practice setting ( Chafe, 2017 ).

This design is also frequently used within mixed-methods studies where qualitative data can explain quantitative findings in explanatory studies, be used for questionnaire development in exploratory studies and validate and corroborate findings in convergent studies ( Doyle et al., 2016 ). There has also been an increase in the use of qualitative descriptive research embedded in large-scale healthcare intervention studies, which can serve a number of purposes including identifying participants’ perceptions of why an intervention worked or, just as importantly, did not work and how the intervention might be improved ( Doyle et al., 2016 ). Using qualitative descriptive research in this manner can help to make the findings of intervention studies more clinically meaningful.

Philosophical and theoretical influences

Qualitative descriptive research generates data that describe the ‘who, what, and where of events or experiences’ from a subjective perspective ( Kim et al., 2017 , p. 23). From a philosophical perspective, this approach to research is best aligned with constructionism and critical theories that use interpretative and naturalistic methods ( Lincoln et al., 2017 ). These philosophical perspectives represent the view that reality exists within various contexts that are dynamic and perceived differently depending on the subject, therefore, reality is multiple and subjective ( Lincoln et al., 2017 ). In qualitative descriptive research, this translates into researchers being concerned with understanding the individual human experience in its unique context. This type of inquiry requires flexible research processes that are inductive and dynamic but do not transform the data beyond recognition from the phenomenon being studied ( Ormston et al., 2014 ; Sandelwoski 2010). Descriptive qualitative research has also been aligned with pragmatism ( Neergaard et al., 2009 ) where decisions are made about how the research should be conducted based on the aims or objectives and context of the study ( Ormston et al., 2014 ). The pragmatist researcher is not aligned to one particular view of knowledge generation or one particular methodology. Instead they look to the concepts or phenomena being studied to guide decision making in the research process, facilitating the selection of the most appropriate methods to answer the research question ( Bishop, 2015 ).

Perhaps linked to the practical application of pragmatism to research, that is, applying the best methods to answer the research question, is the classification of qualitative descriptive research by Sandelowski ( 2010 , p. 82) into a ‘distributed residual category’. This recognises and incorporates uncertainty about the phenomena being studied and the research methods used to study them. For researchers, it permits the use of one or more different types of inquiry, which is essential when acknowledging and exploring different realities and subjective experiences in relation to phenomena ( Long et al., 2018 ). Clarity, in terms of the rationale for the phenomenon being studied and the methods used by the researcher, emerges from the qualitative descriptive approach because the data gathered continue to remain close to the phenomenon throughout the study ( Sandelowski, 2010 ). For this to happen a flexible approach is required and this is evident in the practice of ‘borrowing’ elements of other qualitative methodologies such as grounded theory, phenomenology and ethnography ( Vaismoradi et al., 2013 ).

Regarded as a positive aspect by many researchers who are interested in studying human nature and phenomenon, others believe this flexibility leads to inconsistency across studies and in some cases complacency by researchers. This can result in vague or unexplained decision making around the research process and subsequent lack of credibility. Accordingly, nurse researchers need to be reflexive, that is, clear about their role and position in terms of the phenomena being studied, the context, the theoretical framework and all decision-making processes used in a qualitative descriptive study. This adds credibility to both the study and qualitative descriptive research.

Methods in qualitative descriptive research

As with any research study, the application of descriptive methods will emerge in response to the aims and objectives, which will influence the sampling, data collection and analysis phases of the study.

Most qualitative research aligns itself with non-probability sampling and descriptive research is no different. Descriptive research generally uses purposive sampling and a range of purposive sampling techniques have been described ( Palinkas et al., 2015 ). Many researchers use a combination of approaches such as convenience, opportunistic or snowball sampling as part of the sampling framework, which is determined by the desired sample and the phenomena being studied.

Purposive sampling refers to selecting research participants that can speak to the research aims and who have knowledge and experience of the phenomenon under scrutiny ( Ritchie et al., 2014 ). When purposive sampling is used in a study it delimits and narrows the study population; however, researchers need to remember that other characteristics of the sample will also affect the population, such as the location of the researcher and their flexibility to recruit participants from beyond their base. In addition, the heterogeneity of the population will need to be considered and how this might influence sampling and subsequent data collection and analysis ( Palinkas et al ., 2015 ). Take, for example, conducting research on the experience of caring for people with Alzheimer’s disease (AD). For the most part AD is a condition that affects older people and experiences of participants caring for older people will ultimately dominate the sample. However, AD also affects younger people and how this will impact on sampling needs to be considered before recruitment as both groups will have very different experiences, although there will be overlap. Teddlie and Fu (2007) suggest that although some purposive sampling techniques generate representative cases, most result in describing contrasting cases, which they argue are at the heart of qualitative analysis. To achieve this, Sandelowski (2010) suggests that maximum variation sampling is particularly useful in qualitative descriptive research, which may acknowledge the range of experiences that exist especially in healthcare research. Palinkas et al . (2015) describe maximum variation sampling as identifying shared patterns that emerge from heterogeneity. In other words, researchers attempt to include a wide range of participants and experiences when collecting data. This may be more difficult to achieve in areas where little is known about the substantive area and may depend on the researcher’s knowledge and immersion within the subject area.

Sample size will also need to be considered and although small sample sizes are common in qualitative descriptive research, researchers need to be careful they have enough data collected to meet the study aims ( Ritchie et al., 2014 ). Pre-determining the sample size prior to data collection may stifle the analytic process, resulting in too much or too little data. Traditionally, the gold standard for sample size in qualitative research is data saturation, which differs depending on the research design and the size of the population ( Fusch and Ness, 2015 ). Data saturation is reached ‘when there is enough information to replicate the study, when the ability to obtain additional new information has been attained, and when further coding is no longer feasible’ ( Fusch and Ness, 2015 , p. 1408). However, some argue that although saturation is often reported, it is rarely demonstrated in qualitative descriptive research reports ( Caelli et al., 2003 ; Malterud et al., 2016 ). If data saturation is used to determine sample size, it is suggested that greater emphasis be placed on demonstrating how saturation was reached and at what level to provide more credibility to sample sizes ( Caelli et al., 2003 ). Sample size calculation should be an estimate until saturation has been achieved through the concurrent processes of data collection and analysis. Where saturation has not been achieved, or where sample size has been predetermined for resource reasons, this should be clearly acknowledged. However, there is also a movement away from the reliance on data saturation as a measure of sample size in qualitative research ( Malterud et al., 2016 ). O’Reilly and Parker (2012) question the appropriateness of the rigid application of saturation as a sample size measure arguing that outside of Grounded Theory, its use is inconsistent and at times questionable. Malterud et al. (2016) focus instead on the concept of ‘information power’ to determine sample size. Here, they suggest sample size is determined by the amount of information the sample holds relevant to the actual study rather than the number of participants ( Malterud et al., 2016 ). Some guidance on specific sample size depending on research design has been provided in the literature; however, these are sometimes conflicting and in some cases lack evidence to support their claims ( Guest et al., 2006 ). This is further complicated by the range of qualitative designs and data collection approaches available.

Data collection

Data collection methods in qualitative descriptive research are diverse and aim to discover the who, what and where of phenomena ( Sandelowski, 2000 ). Although semi-structured individual face-to-face interviews are the most commonly used data collection approaches ( Kim et al ., 2017 ), focus groups, telephone interviews and online approaches are also used.

Focus groups involve people with similar characteristics coming together in a relaxed and permissive environment to share their thoughts, experiences and insights ( Krueger and Casey, 2009 ). Participants share their own views and experiences, but also listen to and reflect on the experiences of other group members. It is this synergistic process of interacting with other group members that refines individuals’ viewpoints to a deeper and more considered level and produces data and insights that would not be accessible without the interaction found in a group (Finch et al., 2014). Telephone interviews and online approaches are gaining more traction as they offer greater flexibility and reduced costs for researchers and ease of access for participants. In addition, they may help to achieve maximum variation sampling or examine experiences from a national or international perspective. Face-to-face interviews are often perceived as more appropriate than telephone interviews; however, this assumption has been challenged as evidence to support the use of telephone interviews emerges ( Ward et al., 2015 ). Online data collection also offers the opportunity to collect synchronous and asynchronous data using instant messaging and other online media ( Hooley et al., 2011 ). Online interviews or focus groups conducted via Skype or other media may overcome some of the limitations of telephone interviews, although observation of non-verbal communication may be more difficult to achieve ( Janghorban et al., 2014 ). Open-ended free-text responses in surveys have also been identified as useful data sources in qualitative descriptive studies ( Kim et al . , 2017 ) and in particular the use of online open-ended questions, which can have a large geographical reach ( Seixas et al., 2018 ). Observation is also cited as an approach to data collection in qualitative descriptive research ( Sandelowski, 2000 ; Lambert and Lambert, 2012 ); however, in a systematic review examining the characteristics of qualitative research studies, observation was cited as an additional source of data and was not used as a primary source of data collection ( Kim et al. , 2017 ).

Data analysis and interpretation

According to Lambert and Lambert (2012) , data analysis in qualitative descriptive research is data driven and does not use an approach that has emerged from a pre-existing philosophical or epistemological perspective. Within qualitative descriptive research, it is important analysis is kept at a level at which those to whom the research pertains are easily able to understand and so can use the findings in healthcare practice ( Chafe, 2017 ). The approach to analysis is dictated by the aims of the research and as qualitative descriptive research is generally explorative, inductive approaches will commonly need to be applied although deductive approaches can also be used ( Kim et al . , 2017 ).

Content and thematic analyses are the most commonly used data analysis techniques in qualitative descriptive research. Vaismoradi et al . (2013) argue that content and thematic analysis, although poorly understood and unevenly applied, offer legitimate ways of a lower level of interpretation that is often required in qualitative descriptive research. Sandelowski (2000) indicated that qualitative content analysis is the approach of choice in descriptive research; however, confusion exists between content and thematic analysis, which sometimes means researchers use a combination of the two. Vaismoradi et al. (2013) argue there are differences between the two and that content analysis allows the researchers to analyse the data qualitatively as well as being able to quantify the data whereas thematic analysis provides a purely qualitative account of the data that is richer and more detailed. Decisions to use one over the other will depend on the aims of the study, which will dictate the depth of analysis required. Although there is a range of analysis guidelines available, they share some characteristics and an overview of these, derived from some key texts ( Sandleowski, 2010 ; Braun and Clark, 2006 ; Newell and Burnard, 2006), is presented in Table 1 . Central to these guidelines is an attempt by the researcher to immerse themselves in the data and the ability to demonstrate a consistent and systematic approach to the analysis.

Common characteristics of descriptive qualitative analysis.

Coding in qualitative descriptive research can be inductive and emerge from the data, or a priori where they are based on a pre-determined template as in template analysis. Inductive codes can be ‘in vivo’ where the researcher uses the words or concepts as stated by the participants ( Howitt, 2019 ), or can be named by the researcher and grouped together to form emerging themes or categories through an iterative systematic process until the final themes emerge. Template analysis involves designing a coding template, which is designed inductively from a subset of the data and then applied to all the data and refined as appropriate ( King, 2012 ). It offers a standardised approach that may be useful when several researchers are involved in the analysis process.

Within qualitative research studies generally, the analysis of data and subsequent presentation of research findings can range from studies with a relatively minimal amount of interpretation to those with high levels of interpretation ( Sandelowski and Barroso, 2003 ). The degree of interpretation required in qualitative descriptive research is contentious. Sandelowski (2010) argues that although descriptive research produces findings that are ‘data-near’, they are nevertheless interpretative. Sandelowski (2010) reports that a common misconception in qualitative descriptive designs is that researchers do not need to include any level of analysis and interpretation and can rely solely on indiscriminately selecting direct quotations from participants to answer the research question(s). Although it is important to ensure those familiar with the topic under investigation can recognise their experiences in the description of it ( Kim et al . , 2017 ), this is not to say that there should be no transformation of data. Researchers using a qualitative descriptive design need to, through data analysis, move from un-interpreted participant quotations to interpreted research findings, which can still remain ‘data-near’ ( Sandeklwoski, 2010 ). Willis et al. (2016) suggest that researchers using the qualitative descriptive method might report a comprehensive thematic summary as findings, which moves beyond individual participant reports by developing an interpretation of a common theme. The extent of description and/or interpretation in a qualitative descriptive study is ultimately determined by the focus of the study (Neergard et al ., 2009).

As with any research design, ensuring the rigor or trustworthiness of findings from a qualitative descriptive study is crucial. For a more detailed consideration of the quality criteria in qualitative studies, readers are referred to the seminal work of Lincoln and Guba (1985) in which the four key criteria of credibility, dependability, confirmability and transferability are discussed. At the very least, researchers need to be clear about the methodological decisions taken during the study so readers can judge the trustworthiness of the study and ultimately the findings ( Hallberg, 2013 ). Being aware of personal assumptions and the role they play in the research process is also an important quality criterion (Colorafi and Evans, 2016) and these assumptions can be made explicit through the use of researcher reflexivity in the study ( Bradshaw et al., 2017 ).

Challenges in using a qualitative descriptive design

One of the challenges of utilising a qualitative descriptive design is responding to the charge that many qualitative designs have historically encountered, which is that qualitative designs lack the scientific rigor associated with quantitative approaches ( Vaismoradi et al . , 2013 ). The descriptive design faces further critique in this regard as, unlike other qualitative approaches such as phenomenology or grounded theory, it is not theory driven or oriented ( Neergaard et al ., 2009 ). However, it is suggested that this perceived limitation of qualitative descriptive research only holds true if it is used for the wrong purposes and not primarily for describing the phenomenon ( Neergaard et al ., 2009 ). Kahlke (2014) argues that rather than being atheoretical, qualitative descriptive approaches require researchers to consider to what extent theory will inform the study and are sufficiently flexible to leave space for researchers to utilise theoretical frameworks that are relevant and inform individual research studies. Kim et al. (2017) reported that most descriptive studies reviewed did not identify a theoretical or philosophical framework, but those that did used it to inform the development of either the interview guide or the data analysis framework, thereby identifying the potential use of theory in descriptive designs.

Another challenge around the use of qualitative descriptive research is that it can erroneously be seen as a ‘quick fix’ for researchers who want to employ qualitative methods, but perhaps lack the expertise or familiarity with qualitative research ( Sandelowski, 2010 ). Kim et al. (2017) report how in their review fewer than half of qualitative descriptive papers explicitly identified a rationale for choosing this design, suggesting that in some cases the rationale behind its use was ill considered. Providing a justification for choosing a particular research design is an important part of the research process and, in the case of qualitative descriptive research, a clear justification can offset concerns that a descriptive design was an expedient rather than a measured choice. For studies exploring participants’ experiences, which could be addressed using other qualitative designs, it also helps to clearly make a distinction as to why a descriptive design was the best choice for the research study ( Kim et al ., 2017 ). Similarly, there is a perception that the data analysis techniques most commonly associated with descriptive research – thematic and content analysis are the ‘easiest’ approaches to qualitative analysis; however, as Vaismoradi et al . (2013) suggest, this does not mean they produce low-quality research findings.

As previously identified, a further challenge with the use of qualitative descriptive methods is that as a research design it has limited visibility in research texts and methodological papers ( Kim et al ., 2017 ). This means that novice qualitative researchers have little guidance on how to design and implement a descriptive study as there is a lack of a ‘methodological rulebook’ to guide researchers ( Kahlke, 2014 ). It is also suggested that this lack of strict boundaries and rules around qualitative descriptive research also offers researchers flexibility to design a study using a variety of data collection and analysis approaches that best answer the research question ( Kahlke, 2014 ; Kim et al . , 2017 ). However, should researchers choose to integrate methods ‘borrowed’ from other qualitative designs such as phenomenology or grounded theory, they should do so with the caveat that they do not claim they are using designs they are not actually using ( Neergaard et al . , 2009 ).

Examples of the use of qualitative descriptive research in healthcare

Findings from qualitative descriptive studies within healthcare have the potential to describe the experiences of patients, families and health providers, inform the development of health interventions and policy and promote health and quality of life ( Neergaard et al ., 2009 ; Willis et al ., 2016 ). The examples provided here demonstrate different ways qualitative descriptive methods can be used in a range of healthcare settings.

Simon et al. (2015) used a qualitative descriptive design to identify the perspectives of seriously ill, older patients and their families on the barriers and facilitators to advance care planning. The authors provided a rationale for using a descriptive design, which was to gain a deeper understanding of the phenomenon under investigation. Data were gathered through nine open-ended questions on a researcher-administered questionnaire. Responses to all questions were recorded verbatim and transcribed. Using descriptive, interpretative and explanatory coding that transformed raw data recorded from 278 patients and 225 family members to more abstract ideas and concepts ( Simon et al. , 2015 ), a deeper understanding of the barriers and facilitators to advance care planning was developed. Three categories were developed that identified personal beliefs, access to doctors and interaction with doctors as the central barriers and facilitators to advance care planning. The use of a qualitative descriptive design facilitated the development of a schematic based on these three themes, which provides a framework for use by clinicians to guide improvement in advance care planning.

Focus group interviews are a common data collection method in qualitative descriptive studies and were the method of choice in a study by Pelentsov et al. (2015), which sought to identify the supportive care needs of parents whose child has a rare disease. The rationale provided for using a qualitative descriptive design was to obtain a ‘straight description of the phenomena’ and to provide analysis and interpretation of the findings that remained data-near and representative of the responses of participants. In this study, four semi-structured focus group interviews were conducted with 23 parents. The data from these focus groups were then subjected to a form of thematic analysis during which emerging theories and inferences were identified and organised into a series of thematic networks and ultimately into three global themes. These themes identified that a number of factors including social isolation and lack of knowledge on behalf of healthcare professionals significantly affected how supported parents felt. Identifying key areas of the supportive needs of parents using qualitative description provides direction to health professionals on how best to respond to and support parents of children with a rare disease.

The potential for findings from a qualitative descriptive study to impact on policy was identified in a study by Syme et al. (2016) , who noted a lack of guidance and policies around sexual expression management of residents in long-term care settings. In this study, 20 directors of nursing from long-term care settings were interviewed with a view to identifying challenges in addressing sexual expression in these settings and elicit their recommendations for addressing these challenges in practice and policy. Following thematic analysis, findings relating to what directors of nursing believed to be important components of policy to address sexual expression were identified. These included providing educational resources, having a person-centred care delivery model when responding to sexual expression and providing guidance when working with families. Findings from this qualitative descriptive study provide recommendations that can then feed in to a broader policy on sexual expression in long-term care settings.

The final example of the use of a qualitative descriptive study comes from a mixed-methods study comprising a randomised control trial and a qualitative process evaluation. He et al. (2015) sought to determine the effects of a play intervention for children on parental perioperative anxiety and to explore parents’ perceptions of the intervention. Parents who had children going for surgery were assigned to a control group or an intervention group. The intervention group took part in a 1-hour play therapy session with their child whereas the control group received usual care. Quantitative findings identified there was no difference in parents’ anxiety levels between the intervention and control group. However, qualitative findings identified that parents found the intervention helpful in preparing both themselves and their child for surgery and perceived a reduction in their anxiety about the procedure thereby capturing findings that were not captured by the quantitative measures. In addition, in the qualitative interviews, parents made suggestions about how the play group could be improved, which provides important data for the further development of the intervention.

These examples across a range of healthcare settings provide evidence of the way findings from qualitative descriptive research can be directly used to more fully understand the experiences and perspectives of patients, their families and healthcare providers in addition to guiding future healthcare practice and informing further research.

Qualitative research designs have made significant contributions to the development of nursing and healthcare practices and policy. The utilisation of qualitative descriptive research is common within nursing research and is gaining popularity with other healthcare professions. This paper has identified that the utilisation of this design can be particularly relevant to nursing and healthcare professionals undertaking a primary piece of research and provides an excellent method to address issues that are of real clinical significance to them and their practice setting. However, the conundrum facing researchers who wish to use this approach is its lack of visibility and transparency within methodological papers and texts, resulting in a deficit of available information to researchers when designing such studies. By adding to the existing knowledge base, this paper enhances the information available to researchers who wish to use the qualitative descriptive approach, thus influencing the standard in how this approach is employed in healthcare research. We highlight the need for researchers using this research approach to clearly outline the context, theoretical framework and concepts underpinning it and the decision-making process that informed the design of their qualitative descriptive study including chosen research methods, and how these contribute to the achievement of the study’s aims and objectives. Failure to describe these issues may have a negative impact on study credibility. As seen in our paper, qualitative descriptive studies have a role in healthcare research providing insight into service users and providers’ perceptions and experiences of a particular phenomenon, which can inform healthcare service provision.

Key points for policy, practice and/or research

• Despite its widespread use, there is little methodological guidance to orientate novice nurse researchers when using the qualitative descriptive design. This paper provides this guidance and champions the qualitative descriptive design as appropriate to explore research questions that require accessible and understandable findings directly relevant to healthcare practice and policy.
• This paper identifies how the use of a qualitative descriptive design gives direct voice to participants including patients and healthcare staff, allowing exploration of issues of real and immediate importance in the practice area.
• This paper reports how within qualitative descriptive research, the analysis of data and presentation of findings in a way that is easily understood and recognised is important to contribute to the utilisation of research findings in nursing practice.
• As this design is often overlooked in research texts despite its suitability to exploring many healthcare questions, this paper adds to the limited methodological guidance and has utility for researchers who wish to defend their rationale for the use of the qualitative descriptive design in nursing and healthcare research.

Louise Doyle (PhD, MSc, BNS, RNT, RPN) is an Associate Professor in Mental Health Nursing at the School of Nursing and Midwifery, Trinity College Dublin. Her research interests are in the area of self-harm and suicide and she has a particular interest and expertise in mixed-methods and qualitative research designs.

Catherine McCabe (PhD, MSc, BNS, RNT, RGN) is an Associate Professor in General Nursing at the School of Nursing and Midwifery, Trinity College Dublin. Her research interests and expertise are in the areas of digital health (chronic disease self-management and social/cultural wellbeing), cancer, dementia, arts and health and systematic reviews.

Brian Keogh (PhD, MSc, BNS, RNT, RPN) is an Assistant Professor in Mental Health Nursing at the School of Nursing and Midwifery, Trinity College Dublin. His main area of research interest is mental health recovery and he specialises in qualitative research approaches with a particular emphasis on grounded theory.

Annemarie Brady (PhD, MSc, BNS, RNT, RPN) is Chair of Nursing and Chronic Illness and Head of School of Nursing and Midwifery at Trinity College Dublin. Her research work has focused on the development of healthcare systems and workforce solutions to respond to increased chronic illness demands within healthcare. She has conducted a range of mixed-method research studies in collaboration with health service providers to examine issues around patient-related outcomes measures, workload measurement, work conditions, practice development, patient safety and competency among healthcare workers.

Margaret McCann (PhD, MSc, BNS, RNT, RGN) is an Assistant Professor in General Nursing at the School of Nursing and Midwifery, Trinity College Dublin. Research interests are focused on chronic illness management, the use of digital health and smart technology in supporting patient/client education, self-management and independence. Other research interests include conducting systematic reviews, infection prevention and control and exploring patient outcomes linked to chronic kidney disease.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Ethical approval was not required for this paper as it is a methodological paper and does not report on participant data.

The author(s) received no financial support for the research, authorship and/or publication of this article.

Louise Doyle https://orcid.org/0000-0002-0153-8326

Margaret McCann https://orcid.org/0000-0002-7925-6396

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Qualitative and descriptive research: Data type versus data analysis

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Descriptive research can be utilized in both qualitative and quantitative studies. In qualitative research, descriptive research aims to describe a phenomenon and its characteristics, focusing on what has occurred. Data is often collected qualitatively but analyzed quantitatively using statistical methods. In quantitative studies, descriptive research involves collecting data quantitatively and analyzing it using frequencies, percentages, averages, or other statistical analyses to describe the characteristics of the phenomenon being studied. Therefore, descriptive research can be applied effectively in both qualitative and quantitative research studies to provide a detailed description of the subject under investigation.

Descriptive research in social sciences offers the advantage of providing a detailed description of a phenomenon, focusing on what has occurred. It allows for the collection of quantitative data through methods like surveys and observations. However, a limitation is that it may not delve into the reasons behind why something happened, lacking in-depth analysis. On the other hand, qualitative research offers a holistic understanding through rich data collection, including opinions and attitudes. It explores the how and why of a phenomenon. In summary, descriptive research is beneficial for providing a surface-level understanding, while qualitative research offers a deeper exploration in social sciences.

Descriptive research is beneficial for providing detailed descriptions of phenomena, focusing on what has occurred, and analyzing data quantitatively to determine relationships, enhancing understanding in research studies.

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Chapter 5: Qualitative descriptive research

Darshini Ayton

Learning outcomes

Upon completion of this chapter, you should be able to:

• Identify the key terms and concepts used in qualitative descriptive research.
• Discuss the advantages and disadvantages of qualitative descriptive research.

What is a qualitative descriptive study?

The key concept of the qualitative descriptive study is description.

Qualitative descriptive studies (also known as ‘exploratory studies’ and ‘qualitative description approaches’) are relatively new in the qualitative research landscape. They emerged predominantly in the field of nursing and midwifery over the past two decades. 1 The design of qualitative descriptive studies evolved as a means to define aspects of qualitative research that did not resemble qualitative research designs to date, despite including elements of those other study designs. 2

Qualitative descriptive studies  describe  phenomena rather than explain them. Phenomenological studies, ethnographic studies and those using grounded theory seek to explain a phenomenon. Qualitative descriptive studies aim to provide a comprehensive summary of events. The approach to this study design is journalistic, with the aim being to answer the questions who, what, where and how. 3

A qualitative descriptive study is an important and appropriate design for research questions that are focused on gaining insights about a poorly understood research area, rather than on a specific phenomenon. Since qualitative descriptive study design seeks to describe rather than explain, explanatory frameworks and theories are not required to explain or ‘ground’ a study and its results. 4 The researcher may decide that a framework or theory adds value to their interpretations, and in that case, it is perfectly acceptable to use them. However, the hallmark of genuine curiosity (naturalistic enquiry) is that the researcher does not know in advance what they will be observing or describing. 4 Because a phenomenon is being described, the qualitative descriptive analysis is more categorical and less conceptual than other methods. Qualitative content analysis is usually the main approach to data analysis in qualitative descriptive studies. 4 This has led to criticism of descriptive research being less sophisticated because less interpretation is required than with other qualitative study designs in which interpretation and explanation are key characteristics (e.g. phenomenology, grounded theory, case studies).

Diverse approaches to data collection can be utilised in qualitative description studies. However, most qualitative descriptive studies use semi-structured interviews (see Chapter 13) because they provide a reliable way to collect data. 3 The technique applied to data analysis is generally categorical and less conceptual when compared to other qualitative research designs (see Section 4). 2,3 Hence, this study design is well suited to research by practitioners, student researchers and policymakers. Its straightforward approach enables these studies to be conducted in shorter timeframes than other study designs. 3 Descriptive studies are common as the qualitative component in mixed-methods research ( see Chapter 11 ) and evaluations ( see Chapter 12 ), 1 because qualitative descriptive studies can provide information to help develop and refine questionnaires or interventions.

For example, in our research to develop a patient-reported outcome measure for people who had undergone a percutaneous coronary intervention (PCI), which is a common cardiac procedure to treat heart disease, we started by conducting a qualitative descriptive study. 5 This project was a large, mixed-methods study funded by a private health insurer. The entire research process needed to be straightforward and achievable within a year, as we had engaged an undergraduate student to undertake the research tasks. The aim of the qualitative component of the mixed-methods study was to identify and explore patients’ perceptions following PCI. We used inductive approaches to collect and analyse the data. The study was guided by the following domains for the development of patient-reported outcomes, according to US Food and Drug Administration (FDA) guidelines, which included:

• Feeling: How the patient feels physically and psychologically after medical intervention
• Function: The patient’s mobility and ability to maintain their regular routine
• Evaluation: The patient’s overall perception of the success or failure of their procedure and their perception of what contributed to it. 5(p458)

We conducted focus groups and interviews, and asked participants three questions related to the FDA outcome domains:

• From your perspective, what would be considered a successful outcome of the procedure?

Probing questions: Did the procedure meet your expectations? How do you define whether the procedure was successful?

• How did you feel after the procedure?

Probing question: How did you feel one week after and how does that compare with how you feel now?

• After your procedure, tell me about your ability to do your daily activities?

Prompt for activities including gardening, housework, personal care, work-related and family-related tasks.

Probing questions: Did you attend cardiac rehabilitation? Can you tell us about your experience of cardiac rehabilitation? What impact has medication had on your recovery?

• What, if any, lifestyle changes have you made since your procedure? 5(p459)

Data collection was conducted with 32 participants. The themes were mapped to the FDA patient-reported outcome domains, with the results confirming previous research and also highlighting new areas for exploration in the development of a new patient-reported outcome measure. For example, participants reported a lack of confidence following PCI and the importance of patient and doctor communication. Women, in particular, reported that they wanted doctors to recognise how their experiences of cardiac symptoms were different to those of men.

The study described phenomena and resulted in the development of a patient-reported outcome measure that was tested and refined using a discrete-choice experiment survey, 6 a pilot of the measure in the Victorian Cardiac Outcomes Registry and a Rasch analysis to validate the measurement’s properties. 7

Advantages and disadvantages of qualitative descriptive studies

A qualitative descriptive study is an effective design for research by practitioners, policymakers and students, due to their relatively short timeframes and low costs. The researchers can remain close to the data and the events described, and this can enable the process of analysis to be relatively simple. Qualitative descriptive studies are also useful in mixed-methods research studies. Some of the advantages of qualitative descriptive studies have led to criticism of the design approach, due to a lack of engagement with theory and the lack of interpretation and explanation of the data. 2

Table 5.1. Examples of qualitative descriptive studies

Qualitative descriptive studies are gaining popularity in health and social care due to their utility, from a resource and time perspective, for research by practitioners, policymakers and researchers. Descriptive studies can be conducted as stand-alone studies or as part of larger, mixed-methods studies.

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• Kim H, Sefcik JS, Bradway C. Characteristics of qualitative descriptive studies: a systematic review. Res Nurs Health. 2017;40(1):23-42. doi:10.1002/nur.21768
• Ayton DR et al. Exploring patient-reported outcomes following percutaneous coronary intervention: a qualitative study. Health Expect. 2018;21(2):457-465. doi:10.1111/hex.1263
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• Hiller RM et al. Coping and support-seeking in out-of-home care: a qualitative study of the views of young people in care in England. BMJ Open. 2021;11:e038461. doi:10.1136/bmjopen-2020-038461
• Backman C, Cho-Young D. Engaging patients and informal caregivers to improve safety and facilitate person- and family-centered care during transitions from hospital to home – a qualitative descriptive study. Patient Prefer Adherence. 2019;13:617-626. doi:10.2147/PPA.S201054

Qualitative Research – a practical guide for health and social care researchers and practitioners Copyright © 2023 by Darshini Ayton is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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Data Module #1: What is Research Data?

• Defining Research Data
• Qualitative vs. Quantitative
• Types of Research Data
• Data and Statistics
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Qualitative vs. Quantitative Data

Research data can be placed into two broad categories:  quantitative  or  qualitative.  .

Quantitative  data are used when a researcher is trying to quantify a problem, or address the "what" or "how many" aspects of a research question. It is data that can either be counted or compared on a numeric scale. For example, it could be the number of first year students at Macalester, or the ratings on a scale of 1-4 of the quality of food served at Cafe Mac. This data are usually gathered using instruments, such as a questionnaire which includes a ratings scale or a thermometer to collect weather data. Statistical analysis software, such as SPSS, is often used to analyze quantitative data.

Qualitative  data describes qualities or characteristics. It is collected using questionnaires, interviews, or observation, and frequently appears in narrative form. For example, it could be notes taken during a focus group on the quality of the food at Cafe Mac, or responses from an open-ended questionnaire. Qualitative data may be difficult to precisely measure and analyze. The data may be in the form of descriptive words that can be examined for patterns or meaning, sometimes through the use of coding. Coding allows the researcher to categorize qualitative data to identify themes that correspond with the research questions and to perform quantitative analysis.

Should I Use Qualitative or Quantitative Data for My Research?

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Qualitative vs Quantitative Research Methods & Data Analysis

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

• Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed numerically. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.
• Qualitative research gathers non-numerical data (words, images, sounds) to explore subjective experiences and attitudes, often via observation and interviews. It aims to produce detailed descriptions and uncover new insights about the studied phenomenon.

On This Page:

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography .

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis .

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Mixed methods research
• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

• MS in the Learning Sciences
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Qualitative vs. quantitative data analysis: How do they differ?

Learning analytics have become the cornerstone for personalizing student experiences and enhancing learning outcomes. In this data-informed approach to education there are two distinct methodologies: qualitative and quantitative analytics. These methods, which are typical to data analytics in general, are crucial to the interpretation of learning behaviors and outcomes. This blog will explore the nuances that distinguish qualitative and quantitative research, while uncovering their shared roles in learning analytics, program design and instruction.

What is qualitative data?

Qualitative data is descriptive and includes information that is non numerical. Qualitative research is used to gather in-depth insights that can't be easily measured on a scale like opinions, anecdotes and emotions. In learning analytics qualitative data could include in depth interviews, text responses to a prompt, or a video of a class period. 1

What is quantitative data?

Quantitative data is information that has a numerical value. Quantitative research is conducted to gather measurable data used in statistical analysis. Researchers can use quantitative studies to identify patterns and trends. In learning analytics quantitative data could include test scores, student demographics, or amount of time spent in a lesson. 2

Key difference between qualitative and quantitative data

It's important to understand the differences between qualitative and quantitative data to both determine the appropriate research methods for studies and to gain insights that you can be confident in sharing.

Data Types and Nature

Examples of qualitative data types in learning analytics:

• Observational data of human behavior from classroom settings such as student engagement, teacher-student interactions, and classroom dynamics
• Textual data from open-ended survey responses, reflective journals, and written assignments
• Feedback and discussions from focus groups or interviews
• Content analysis from various media

Examples of quantitative data types:

• Standardized test, assessment, and quiz scores
• Grades and grade point averages
• Attendance records
• Time spent on learning tasks
• Data gathered from learning management systems (LMS), including login frequency, online participation, and completion rates of assignments

Methods of Collection

Qualitative and quantitative research methods for data collection can occasionally seem similar so it's important to note the differences to make sure you're creating a consistent data set and will be able to reliably draw conclusions from your data.

Qualitative research methods

Because of the nature of qualitative data (complex, detailed information), the research methods used to collect it are more involved. Qualitative researchers might do the following to collect data:

• Conduct interviews to learn about subjective experiences
• Host focus groups to gather feedback and personal accounts
• Observe in-person or use audio or video recordings to record nuances of human behavior in a natural setting
• Distribute surveys with open-ended questions

Quantitative research methods

Quantitative data collection methods are more diverse and more likely to be automated because of the objective nature of the data. A quantitative researcher could employ methods such as:

• Surveys with close-ended questions that gather numerical data like birthdates or preferences
• Observational research and record measurable information like the number of students in a classroom
• Automated numerical data collection like information collected on the backend of a computer system like button clicks and page views

Analysis techniques

Qualitative and quantitative data can both be very informative. However, research studies require critical thinking for productive analysis.

Qualitative data analysis methods

Analyzing qualitative data takes a number of steps. When you first get all your data in one place you can do a review and take notes of trends you think you're seeing or your initial reactions. Next, you'll want to organize all the qualitative data you've collected by assigning it categories. Your central research question will guide your data categorization whether it's by date, location, type of collection method (interview vs focus group, etc), the specific question asked or something else. Next, you'll code your data. Whereas categorizing data is focused on the method of collection, coding is the process of identifying and labeling themes within the data collected to get closer to answering your research questions. Finally comes data interpretation. To interpret the data you'll take a look at the information gathered including your coding labels and see what results are occurring frequently or what other conclusions you can make. 3

Quantitative analysis techniques

The process to analyze quantitative data can be time-consuming due to the large volume of data possible to collect. When approaching a quantitative data set, start by focusing in on the purpose of your evaluation. Without making a conclusion, determine how you will use the information gained from analysis; for example: The answers of this survey about study habits will help determine what type of exam review session will be most useful to a class. 4

Next, you need to decide who is analyzing the data and set parameters for analysis. For example, if two different researchers are evaluating survey responses that rank preferences on a scale from 1 to 5, they need to be operating with the same understanding of the rankings. You wouldn't want one researcher to classify the value of 3 to be a positive preference while the other considers it a negative preference. It's also ideal to have some type of data management system to store and organize your data, such as a spreadsheet or database. Within the database, or via an export to data analysis software, the collected data needs to be cleaned of things like responses left blank, duplicate answers from respondents, and questions that are no longer considered relevant. Finally, you can use statistical software to analyze data (or complete a manual analysis) to find patterns and summarize your findings. 4

Qualitative and quantitative research tools

From the nuanced, thematic exploration enabled by tools like NVivo and ATLAS.ti, to the statistical precision of SPSS and R for quantitative analysis, each suite of data analysis tools offers tailored functionalities that cater to the distinct natures of different data types.

Qualitative research software:

NVivo: NVivo is qualitative data analysis software that can do everything from transcribe recordings to create word clouds and evaluate uploads for different sentiments and themes. NVivo is just one tool from the company Lumivero, which offers whole suites of data processing software. 5

ATLAS.ti: Similar to NVivo, ATLAS.ti allows researchers to upload and import data from a variety of sources to be tagged and refined using machine learning and presented with visualizations and ready for insert into reports. 6

SPSS: SPSS is a statistical analysis tool for quantitative research, appreciated for its user-friendly interface and comprehensive statistical tests, which makes it ideal for educators and researchers. With SPSS researchers can manage and analyze large quantitative data sets, use advanced statistical procedures and modeling techniques, predict customer behaviors, forecast market trends and more. 7

R: R is a versatile and dynamic open-source tool for quantitative analysis. With a vast repository of packages tailored to specific statistical methods, researchers can perform anything from basic descriptive statistics to complex predictive modeling. R is especially useful for its ability to handle large datasets, making it ideal for educational institutions that generate substantial amounts of data. The programming language offers flexibility in customizing analysis and creating publication-quality visualizations to effectively communicate results. 8

Applications in Educational Research

Both quantitative and qualitative data can be employed in learning analytics to drive informed decision-making and pedagogical enhancements. In the classroom, quantitative data like standardized test scores and online course analytics create a foundation for assessing and benchmarking student performance and engagement. Qualitative insights gathered from surveys, focus group discussions, and reflective student journals offer a more nuanced understanding of learners' experiences and contextual factors influencing their education. Additionally feedback and practical engagement metrics blend these data types, providing a holistic view that informs curriculum development, instructional strategies, and personalized learning pathways. Through these varied data sets and uses, educators can piece together a more complete narrative of student success and the impacts of educational interventions.

Master Data Analysis with an M.S. in Learning Sciences From SMU

Whether it is the detailed narratives unearthed through qualitative data or the informative patterns derived from quantitative analysis, both qualitative and quantitative data can provide crucial information for educators and researchers to better understand and improve learning. Dive deeper into the art and science of learning analytics with SMU's online Master of Science in the Learning Sciences program . At SMU, innovation and inquiry converge to empower the next generation of educators and researchers. Choose the Learning Analytics Specialization to learn how to harness the power of data science to illuminate learning trends, devise impactful strategies, and drive educational innovation. You could also find out how advanced technologies like augmented reality (AR), virtual reality (VR), and artificial intelligence (AI) can revolutionize education, and develop the insight to apply embodied cognition principles to enhance learning experiences in the Learning and Technology Design Specialization , or choose your own electives to build a specialization unique to your interests and career goals.

For more information on our curriculum and to become part of a community where data drives discovery, visit SMU's MSLS program website or schedule a call with our admissions outreach advisors for any queries or further discussion. Take the first step towards transforming education with data today.

• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/qualitative-data
• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/quantitative-data
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief19.pdf
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief20.pdf
• Retrieved on August 8, 2024, from lumivero.com/solutions/
• Retrieved on August 8, 2024, from atlasti.com/
• Retrieved on August 8, 2024, from ibm.com/products/spss-statistics
• Retrieved on August 8, 2024, from cran.r-project.org/doc/manuals/r-release/R-intro.html#Introduction-and-preliminaries

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Understanding Qualitative and Quantitative Data

• 7 minute read
• August 22, 2024

Written by:

Smith Alex is a committed data enthusiast and an aspiring leader in the domain of data analytics. With a foundation in engineering and practical experience in the field of data science

Summary: This article delves into qualitative and quantitative data, defining each type and highlighting their key differences. It discusses when to use each data type, the benefits of integrating both, and the challenges researchers face. Understanding these concepts is crucial for effective research design and achieving comprehensive insights.

Introduction

In the realm of research and Data Analysis , two fundamental types of data play pivotal roles: qualitative and quantitative data. Understanding the distinctions between these two categories is essential for researchers, analysts, and decision-makers alike, as each type serves different purposes and is suited to various contexts.

This article will explore the definitions, characteristics, uses, and challenges associated with both qualitative and quantitative data, providing a comprehensive overview for anyone looking to enhance their understanding of data collection and analysis.

Read More:   Exploring 5 Statistical Data Analysis Techniques with Real-World Examples

Defining Qualitative Data

Qualitative data is non-numerical in nature and is primarily concerned with understanding the qualities, characteristics, and attributes of a subject.

This type of data is descriptive and often involves collecting information through methods such as interviews, focus groups, observations, and open-ended survey questions. The goal of qualitative data is to gain insights into the underlying motivations, opinions, and experiences of individuals or groups.

Characteristics of Qualitative Data

• Descriptive : Qualitative data provides rich, detailed descriptions of phenomena, allowing researchers to capture the complexity of human experiences.
• Subjective : The interpretation of qualitative data can vary based on the researcher’s perspective, making it inherently subjective.
• Contextual : This type of data is often context-dependent, meaning that the insights gained can be influenced by the environment or situation in which the data was collected.
• Exploratory : Qualitative data is typically used in exploratory research to generate hypotheses or to understand phenomena that are not well understood.

Examples of Qualitative Data

• Interview transcripts that capture participants’ thoughts and feelings.
• Observational notes from field studies.
• Responses to open-ended questions in surveys.
• Personal narratives or case studies that illustrate individual experiences.

Defining Quantitative Data

Quantitative data, in contrast, is numerical and can be measured or counted. This type of data is often used to quantify variables and analyse relationships between them. Quantitative research typically employs statistical methods to test hypotheses, identify patterns, and make predictions based on numerical data.

Characteristics of Quantitative Data

• Objective : Quantitative data is generally considered more objective than qualitative data, as it relies on measurable values that can be statistically analysed.
• Structured : This type of data is often collected using structured methods such as surveys with closed-ended questions, experiments, or observational checklists.
• Generalizable : Because quantitative data is based on numerical values, findings can often be generalised to larger populations if the sample is representative.
• Statistical Analysis : Quantitative data lends itself to various statistical analyses , allowing researchers to draw conclusions based on numerical evidence.

Examples of Quantitative Data

• Age, height, and weight measurements.
• Survey results with numerical ratings (e.g., satisfaction scores).
• Test scores or academic performance metrics.
• Financial data such as income, expenses, and profit margins.

Key Differences Between Qualitative and Quantitative Data

Understanding the differences between qualitative and quantitative data is crucial for selecting the appropriate research methods and analysis techniques. Here are some key distinctions:

When to Use Qualitative Data

Qualitative data is particularly useful in situations where the research aims to explore complex phenomena, understand human behaviour, or generate new theories. Here are some scenarios where qualitative data is the preferred choice:

Exploratory Research

When investigating a new area of study where little is known, qualitative methods can help uncover insights and generate hypotheses.

Understanding Context

Qualitative data is valuable for capturing the context surrounding a particular phenomenon, providing depth to the analysis.

Gaining Insights into Attitudes and Behaviours

When the goal is to understand why individuals think or behave in a certain way, qualitative methods such as interviews can provide rich, nuanced insights.

Developing Theories

Qualitative research can help in the development of theories by exploring relationships and patterns that quantitative methods may overlook.

When to Use Quantitative Data

Quantitative data is best suited for research that requires measurement, comparison, and statistical analysis. Here are some situations where quantitative data is the preferred choice:

Testing Hypotheses

When researchers have specific hypotheses to test , quantitative methods allow for rigorous statistical analysis to confirm or reject these hypotheses.

Measuring Variables

Quantitative data is ideal for measuring variables and establishing relationships between them, making it useful for experiments and surveys.

Generalising Findings

When the goal is to generalise findings to a larger population, quantitative research provides the necessary data to support such conclusions.

Identifying Patterns and Trends

Quantitative analysis can reveal patterns and trends in data that can inform decision-making and policy development.

Integrating Qualitative and Quantitative Data

While qualitative and quantitative data are distinct, they can be effectively integrated to provide a more comprehensive understanding of a research question. This mixed-methods approach combines the strengths of both types of data, allowing researchers to triangulate findings and gain deeper insights.

Benefits of Integration

Integrating qualitative and quantitative data enhances research by combining numerical analysis with rich, descriptive insights. This mixed-methods approach allows for a comprehensive understanding of complex phenomena, validating findings and providing a more nuanced perspective on research questions.

• Enhanced Validity: By using both qualitative and quantitative data, researchers can validate their findings through multiple sources of evidence.
• Rich Insights : Qualitative data can provide context and depth to quantitative findings, helping to explain the “why” behind numerical trends.
• Comprehensive Understanding: Integrating both types of data allows for a more holistic understanding of complex phenomena, leading to more informed conclusions and recommendations.

Examples of Integration

• Surveys with Open-Ended Questions: Combining closed-ended questions (quantitative) with open-ended questions (qualitative) in surveys can provide both measurable data and rich descriptive insights.
• Case Studies with Statistical Analysis: Researchers can conduct case studies (qualitative) while also collecting quantitative data to support their findings, offering a more robust analysis.
• Focus Groups with Follow-Up Surveys: After conducting focus groups (qualitative), researchers can administer surveys (quantitative) to a larger population to validate the insights gained.

Challenges and Considerations

While qualitative and quantitative data offer distinct advantages, researchers must also be aware of the challenges and considerations associated with each type:

Challenges of Qualitative Data

The challenges of qualitative data are multifaceted and can significantly impact the research process. Here are some of the primary challenges faced by researchers when working with qualitative data:

Subjectivity and Bias

One of the most significant challenges in qualitative research is the inherent subjectivity involved in data collection and analysis. Researchers’ personal beliefs, assumptions, and experiences can influence their interpretation of data.

Data Overload

Qualitative research often generates large volumes of data, which can be overwhelming. This data overload can make it challenging to identify key themes and insights. Researchers may struggle to manage and analyse vast amounts of qualitative data, leading to potential insights being overlooked.

Lack of Structure

Qualitative data is often unstructured, making it difficult to analyse systematically. The absence of a predefined format can lead to challenges in drawing meaningful conclusions from the data.

Time-Consuming Nature

Qualitative analysis can be extremely time-consuming, especially when dealing with extensive data sets. The process of collecting, transcribing, and analysing qualitative data often requires significant time and resources, which can be a barrier for researchers.

Challenges of Quantitative Data

Quantitative data provides objective, measurable evidence, it also faces challenges in capturing the full complexity of human experiences, maintaining data accuracy, and avoiding misinterpretation of statistical results. Integrating qualitative data can help overcome some of these limitations.

Limits in Capturing Complexity

Quantitative data, by its nature, can oversimplify complex phenomena and miss important nuances that qualitative data can capture. The focus on numerical measurements may not fully reflect the depth and richness of human experiences and behaviours.

Chances for Misinterpretation

Numbers can be twisted or misinterpreted if not analysed properly. Researchers must be cautious in interpreting statistical results, as correlation does not imply causation. Poor knowledge of statistical analysis can negatively impact the analysis and interpretation of quantitative data.

Influence of Measurement Errors

Due to the numerical nature of quantitative data, even small measurement errors can skew the entire dataset. Inaccuracies in data collection methods can lead to drawing incorrect conclusions from the analysis.

Lack of Context

Quantitative experiments often do not take place in natural settings. The data may lack the context and nuance that qualitative data can provide to fully explain the phenomena being studied.

Sample Size Limitations

Small sample sizes in quantitative studies can reduce the reliability of the data. Large sample sizes are needed for more accurate statistical analysis. This also affects the ability to generalise findings to wider populations.

Confirmation Bias

Researchers may miss observing important phenomena due to their focus on testing pre-determined hypotheses rather than generating new theories. The confirmation bias inherent in hypothesis testing can limit the discovery of unexpected insights.

In conclusion, understanding the distinctions between qualitative and quantitative data is essential for effective research and Data Analysis . Each type of data serves unique purposes and is suited to different contexts, making it crucial for researchers to select the appropriate methods based on their research objectives.

By integrating both qualitative and quantitative data, researchers can gain a more comprehensive understanding of complex phenomena, leading to richer insights and more informed decision-making.

As the landscape of research continues to evolve, the ability to effectively utilise and integrate both types of data will remain a valuable skill for researchers and analysts alike.

Frequently Asked Questions

What is the primary difference between qualitative and quantitative data.

The primary difference is that qualitative data is descriptive and non-numerical, focusing on understanding qualities and experiences, while quantitative data is numerical and measurable, focusing on quantifying variables and testing hypotheses.

When Should I Use Qualitative Data in My Research?

Qualitative data is best used when exploring new topics, understanding complex behaviours, or generating hypotheses, particularly when context and depth are important.

Can Qualitative and Quantitative Data Be Used Together?

Yes, integrating qualitative and quantitative data can provide a more comprehensive understanding of a research question, allowing researchers to validate findings and gain richer insights.

Reviewed by:

Post written by: Smith Alex

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