• CASP Subquestions
Note . The CASP questions are adapted from “10 questions to help you make sense of qualitative research,” by Critical Appraisal Skills Programme, 2013, retrieved from http://media.wix.com/ugd/dded87_29c5b002d99342f788c6ac670e49f274.pdf . Its license can be found at http://creativecommons.org/licenses/by-nc-sa/3.0/
Once articles were assessed by the two authors independently, all three authors discussed and reconciled our assessment. No articles were excluded based on CASP results; rather, results were used to depict the general adequacy (or rigor) of all 55 articles meeting inclusion criteria for our systematic review. In addition, the CASP was included to enhance our examination of the relationship between the methods and the usefulness of the findings documented in each of the QD articles included in this review.
To further assess each of the 55 articles, data were extracted on: (a) research objectives, (b) design justification, (c) theoretical or philosophical framework, (d) sampling and sample size, (e) data collection and data sources, (f) data analysis, and (g) presentation of findings (see Table 2 ). We discussed extracted data and identified common and unique features in the articles included in our systematic review. Findings are described in detail below and in Table 3 .
Elements for Data Extraction
Elements | Data Extraction |
---|---|
Research objectives | • Verbs used in objectives or aims |
• Focuses of study | |
Design justification | • If the article cited references for qualitative description |
• If the article offered rationale to choose qualitative description | |
• References cited | |
• Rationale reported | |
Theoretical or philosophical frameworks | • If the article has theoretical or philosophical frameworks for study |
• Theoretical or philosophical frameworks reported | |
• How the frameworks were used in data collection and analysis | |
Sampling and sample sizes | • Sampling strategies (e.g., purposeful sampling, maximum variation) |
• Sample size | |
Data collection and sources | • Data collection techniques (e.g., individual or focus-group interviews, interview guide, surveys, field notes) |
Data analysis | • Data analysis techniques (e.g., qualitative content analysis, thematic analysis, constant comparison) |
• If data saturation was achieved | |
Presentation of findings | • Statement of findings |
• Consistency with research objectives |
Data Extraction and Analysis Results
Authors Country | Research Objectives | Design justification | Theoretical/ philosophical frameworks | Sampling/ sample size | Data collection and data sources | Data analysis | Findings |
---|---|---|---|---|---|---|---|
• USA | • Explore • Responses to communication strategies | • (-) Reference • (-) Rationale | Not reported (NR) | • Purposive sampling/ maximum variation • 32 family members | • Interviews • Observations • Review of daily flow sheet • Demographics | • Inductive and deductive qualitative content analysis • (-) Data saturation | Five themes about family members’ perceptions of nursing communication approaches |
• Sweden | • Describe • Experiences of using guidelines in daily practice | • (-) Reference • (+) Rationale • Part of a research program | NR | • Unspecified • 8 care providers | • Semistructured, individual interviews • Interview guide | • Qualitative content analysis • (-) Data saturation | One theme and seven subthemes about care providers’ experiences of using guidelines in daily practice |
• USA | • Examine • Culturally specific views of processes and causes of midlife weight gain | • (-) Reference • (-) Rationale | Health belief model and Kleiman’s explanatory model | • Unspecified • 19 adults | • Semistructured, individual interview | • Conventional content analysis • (-) Data saturation | Three main categories (from the model) and eight subthemes about causes of weight gain in midlife |
• Iran | • Explore • Factors initiating responsibility among medical trainees | • (-) Reference • (+) Rationale | NR | • Convenience, snowball, and maximum variation sampling • 15 trainees and other professionals | • Semistructured, individual interview • Interview guide | • Conventional content analysis • Constant comparison • (+) Data saturation | Two themes and individual and non- individual-based factors per theme |
• Iran | • Explore • Factors related to job satisfaction and dissatisfaction | • (-) Reference • (-) Rationale | NR | • Convenience sampling • 85 nurses | • Semistructured focus group interviews • Interview guide | • Thematic analysis • (+) Data saturation | Three main themes and associated factors regarding job satisfaction and dissatisfaction |
• Norway | • Describe • Perceptions on simulation-based team training | • (-) Reference • (-) Rationale | NR | • Strategic sampling • 18 registered nurses | • Semistructured individual interviews | • Inductive content analysis • (-) Data saturation | One main category, three categories, and six sub- categories regarding nurses’ perceptions on simulation-based team training |
• USA | • Determine • Barriers and supports for attending college and nursing school | • (-) Reference • (-) Rationale | NR | • Unspecified • 45 students | • Focus-group interviews • Using Photovoice and SHOWeD | • Constant comparison • (-) Data saturation | Five themes about facilitators and barriers |
• USA | • Explore • Reasons for choosing home birth and birth experiences | • (-) Reference • (-) Rationale | NR | • Purposeful sampling • 20 women | • Semistructured focus-group interviews • Interview guide • Field notes | • Qualitative content analysis • (+) Data saturation | Five common themes and concepts about reasons for choosing home birth based on their birth experiences |
• New Zealand | • Explore • Normal fetal activity related to hunger and satiation | • (+) Reference • (+) Rationale • • Denzin & Lincoln (2011) | NR | • Purposive sampling • 19 pregnant women | • Semistructured individual interviews • Open-ended questions | • Inductive qualitative content analysis • Descriptive statistical analysis • (+) Data saturation | Four patterns regarding fetal activities in relation to meal anticipation, maternal hunger, maternal meal consummation, and maternal satiety |
• Italy | • Explore, describe, and compare • perceptions of nursing caring | • (+) Reference • (-) Rationale • | NR | • Purposive sampling • 20 nurses and 20 patients | • Semistructured individual interviews • Interview guide • Field notes during interviews | • Unspecified various analytic strategies including constant comparison • (-) Data saturation | Nursing caring from both patients’ and nurses’ perspectives – a summary of data in visible caring and invisible caring |
• Hong Kong | • Address • How to reduce coronary heart disease risks | • (+) Reference • (+) Rationale • Secondary analysis • • | NR | • Convenience and snowball sampling • 105 patients | • Focus-group interviews • Interview guide | • Content analysis • (+) Data saturation | Four categories about patients’ abilities to reduce coronary heart disease |
• Taiwan | • Explore • Reasons for young–old people not killing themselves | • (-) Reference • (-) Rationale | NR | • Convenience sampling • 31 older adults | • Semistructured individual interviews • Interview guide • Observation with memos/reflective journal | • Content analysis • (+) Data saturation | Six themes regarding reasons for not committing to suicide |
• USA | • Explore • Neonatal intensive care unit experiences | • (+) Reference • (+) Rationale • | NR | • Purposive sampling and convenience sample • 15 mothers | • Semistructured individual interviews • Interview guide | • Qualitative content analysis • (+) Data saturation | Four themes about participants’ experiences of neonatal intensive care unit |
• Colombia | • Investigate • Barriers/facilitators to implementing evidence-based nursing | • (+) Reference • (-) Rationale • | Ottawa model for research use: knowledge translation framework | • Convenience sampling • 13 nursing professionals | • Semistructured individual interviews • Interview guide | • Inductive qualitative content analysis • Constant comparison • (-) Data saturation | Four main barriers and potential facilitators to evidence-based nursing |
• Australia | • Explore • Perceptions and utilization of diaries | • (+) Reference • (-) Rationale • | NR | • Unspecified • 19 patients and families | • Responses to open-ended questions on survey | • Unspecified analysis strategy • (-) Data saturation | Five themes regarding perceptions on use of diaries and descriptive statistics using frequencies of utilization |
• USA | • Explore • Knowledge, attitudes, and beliefs about sexual consent | • (-) Reference • (-) Rationale • Part of a larger mixed-method study | Theory of planned behavior | • Purposive sampling • snowball sampling • 26 women | • Semistructured focus-group interviews • Interview guide | • Content analysis • (+) Data saturation | Three main categories and subthemes regarding sexual consent |
• Sweden | • Describe • Experiences of knowledge development in wound management | • (+) Reference • (+) Rationale: weak • | NR | • Purposive sampling • 16 district nurses | • Individual interviews • Interview guide | • Qualitative content analysis • (-) Data saturation | Three categories and eleven sub-categories about knowledge development experiences in wound management |
• USA | • Describe • Parental-pain journey, beliefs about pain, and attitudes/behaviors related to children’s responses | • (+) Reference • (+) Rationale • • • Part of a larger mixed methods study | NR | • Purposive sampling • 9 parents | • Individual interviews • One open- ended question | • Qualitative content analysis • (+) Data saturation | Two main themes, categories, and subcategories about parents’ experiences of observing children’s pain |
• USA | • Describe • Challenges and barriers in providing culturally competent care | • (+) Reference • (+) Rationale • • Secondary analysis | NR | • Stratified sampling • 253 nurses | • Written responses to 2 open-ended questions on survey | • Thematic analysis • (-) Data saturation | Three themes regarding challenges/barriers |
• Denmark | • Describe • Experiences of childbirth | • (-) Reference • (-) Rationale • A substudy | NR | • Purposive sampling with maximum variation • Partners of 10 women | • Semistructured, individual interviews • Interview guide | • Thematic analysis • (+) Data saturation | Three themes and four subthemes about partners’ experiences of women’s childbirth |
• Australia | • Explore • Perceptions about medical nutrition and hydration at the end of life | • (+) Reference • (+) Rationale • | NR | • Purposeful sampling • 10 nurses | • Focus-group interviews | • “analyzed thematically” • (-) Data saturation | One main theme and four subthemes regarding nurses’ perceptions on EOL- related medical nutrition and hydration |
• USA | • Describe • Reasons for leaving a home visiting program early | • (-) Reference • (-) Rationale | NR | • Convenience sample • 32 mothers, nurses, and nurse supervisors | • Semistructured, individual interviews • Focus-group interviews • Interview guide | • Inductive content analysis • Constant comparison approach • (+) Data saturation | Three sets of reasons for leaving a home visiting program |
• Sweden | • Explore and describe • Beliefs and attitudes around the decision for a caesarean section | • (+) Reference • (+) Rationale • • | NR | • Unspecified • 21 males | • Individual telephone interviews | • Thematic analysis • Constant comparison approach • (-) Data saturation | Two themes and subthemes in relation to the research objective |
• Taiwan | • Explore • Illness experiences of early onset of knee osteoarthritis | • (+) Reference • (+) Rationale • • • Part of a large research series | NR | • Purposive sampling • 17 adults | • Semistructured, Individual interviews • Interview guide • Memo/field notes (observations) | • Inductive content analysis • (+) Data saturation | Three major themes and nine subthemes regarding experiences of early onset-knee osteoarthritis |
• Australia | • Explore • Perceptions about bedside handover (new model) by nurses | • (+) Reference • (+) Rationale • • | NR | • Purposive sampling • 30 patients | • Semistructured, individual interviews • Interview guide | • Thematic content analysis • (-) Data analysis | Two dominant themes and related subthemes regarding patients’ thoughts about nurses’ bedside handover |
• Sweden | • Identify • Patterns in learning when living with diabetes | • (-) Reference • (-) Rationale | NR | • Purposive sampling with variations in age and sex • 13 participants | • Semistructured, individual interviews (3 times over 3 years) | • analysis process • Inductive qualitative content analysis • (-) Data saturation | Five main patterns of learning when living with diabetes for three years following diagnosis |
• Canada | • Evaluate • Book chat intervention based on a novel | • (-) Reference • (-) Rationale • Part of a larger research project | NR | • Unspecified • 11 long-term- care staff | • Questionnaire with two open- ended questions | • Thematic content analysis • (-) Data saturation | Five themes (positive comments) about the book chat with brief description |
• Taiwan | • Explore • Facilitators and barriers to implementing smoking- cessation counseling services | • (-) Reference • (-) Rationale | NR | • Unspecified • 16 nurse- counselors | • Semistructured individual interviews • Interview guide | • Inductive content analysis • Constant comparison • (-) Data saturation | Two themes and eight subthemes about facilitators and barriers described using 2-4 quotations per subtheme |
• USA | • Identify • Educational strategies to manage disruptive behavior | • (-) Reference • (-) Rationale • Part of a larger study | NR | • Unspecified • 9 nurses | • Semistructured, individual interviews • Interview guide | • Content analysis procedures • (-) Data saturation | Two main themes regarding education strategies for nurse educators |
• USA | • Explore • Experiences of difficulty resolving patient- related concerns | • (-) Reference • (-) Rationale • Secondary analysis | NR | • Unspecified • 1932 physician, nursing, and midwifery professionals | • E-mail survey with multiple- choice and free- text responses | • Inductive thematic analysis • Descriptive statistics • (-) Data saturation | One overarching theme and four subthemes about professionals’ experiences of difficulty resolving patient-related concerns |
• Singapore | • Explicate • Experience of quality of life for older adults | • (+) Reference • (+) Rationale • | Parse’s human becoming paradigm | • Unspecified • 10 elderly residents | • Individual interviews • Interview questions presented (Parse) | • Unspecified analysis techniques • (-) Data saturation | Three themes presented using both participants’ language and the researcher’s language |
• China | • Explore • Perspectives on learning about caring | • (-) Reference • (-) Rationale | NR | • Purposeful sampling • 20 nursing students | • Focus-group interviews • Interview guide | • Conventional content analysis • (-) Data saturation | Four categories and associated subcategories about facilitators and challenges to learning about caring |
• Poland | • Describe and assess • Components of the patient–nurse relationship and pediatric-ward amenities | • (+) Reference • (-) Rationale • | NR | • Purposeful, maximum variation sampling • 26 parents or caregivers and 22 children | • Individual interviews | • Qualitative content analysis • (-) Data saturation | Five main topics described from the perspectives of children and parents |
• Canada | • Evaluate • Acceptability and feasibility of hand-massage therapy | • (-) Reference • (-) Rationale • Secondary to a RCT | Focused on feasibility and acceptability | • Unspecified • 40 patients | • Semistructured, individual interviews • Field notes • Video recording | • Thematic analysis for acceptability • Quantitative ratings of video items for feasibility • (-) Data analysis | Summary of data focusing on predetermined indicators of acceptability and descriptive statistics to present feasibility |
• USA | • Understand • Challenges occurring during transitions of care | • (+) Reference • (+) Rationale • • Part of a larger study | NR | • Convenience sample • 22 nurses | • Focus groups • Interview guide | • Qualitative content analysis methods • (+) Data analysis | Three themes about challenges regarding transitions of care: |
• Canada | • Understand • Factors that influence nurses’ retention in their current job | • (-) Reference • (-) Rationale | NR | • Purposeful sampling • 41 nurses | • Focus-group interviews • Interview guide | • Directed content analysis • (+) Data saturation | Nurses’ reasons to stay and leave their current job |
• Australia | • Extend • Understanding of caregivers’ views on advance care planning | • (+) Reference • (+) Rationale • • Grounded theory overtone | NR | • Theoretical sampling • 18 caregivers | • Semistructured focus group and individual interviews • Interview guide • Vignette technique | • Inductive, cyclic, and constant comparative analysis • (-) Data analysis | Three themes regarding caregivers’ perceptions on advance care planning |
• USA | • Describe • Outcomes older adults with epilepsy hope to achieve in management | • (-) Reference • (-) Rationale | NR | • Unspecified • 20 patients | • Individual interview | • Conventional content analysis • (-) Data saturation | Six main themes and associated subthemes regarding what older adults hoped to achieve in management of their epilepsy |
• The Netherlands | • Gain • Experience of personal dignity and factors influencing it | • (+) Reference • (-) Rationale • | Model of dignity in illness | • Maximum variation sampling • 30 nursing home residents | • Individual interviews • Interview guide | • Thematic analysis • Constant comparison • (+) Data saturation | The threatening effect of illness and three domains being threatened by illness in relation to participants’ experiences of personal dignity |
• USA | • Identify and describe • Needs in mental health services and “ideal” program | • (+) Reference • (+) Rationale • • There is a primary study | NR | • Unspecified • 52 family members | • Semistructured, individual and focus-group interviews | • “Standard content analytic procedures” with case-ordered meta-matrix • (-) Data saturation | Two main topics – (a) intervention modalities that would fit family members’ needs in mental health services and (b) topics that programs should address |
• USA | • “What are the perceptions of staff nurses regarding palliative care…?” | • (-) Reference • (-) Rationale | NR | • Purposive, convenience sampling • 18 nurses | • Semistructured and focus-group interviews • Interview guide | • Ritchie and Spencer’s framework for data analysis • (-) Data saturation | Five thematic categories and associated subcategories about nurses’ perceptions of palliative care |
• Canada | • Describe • Experience of caring for a relative with dementia | • (+) Reference • (+) Rationale • Sandelowski ( ; ) • Secondary analysis • Phenomenological overtone | NR | • Purposive sampling • 11 bereaved family members | • Individual interviews • 27 transcripts from the primary study | • Unspecified • (-) Data saturation | Five major themes regarding the journey with dementia from the time prior to diagnosis and into bereavement |
• Canada | • Describe Experience of fetal fibronectin testing | • (+) Reference • (+) Rationale • • | NR | • Unspecified • 17 women | • Semistructured individual interviews • Interview guide | • Conventional content analysis • (+) Data saturation | One overarching theme, three themes, and six subthemes about women’s experiences of fetal fibronectin testing |
• New Zealand | • Explore • Role of nurses in providing palliative and end-of-life care | • (+) Reference • (+) Rationale • • Part of a larger study | NR | • Purposeful sampling • 21 nurses | • Semistructured individual interviews | • Thematic analysis • (-) Data saturation | Three themes about practice nurses’ experiences in providing palliative and end-of-life care |
• Brazil | • Understand • Experience with postnatal depression | • (+) Reference • (-) Rationale • | NR | • Purposeful, criterion sampling • 15 women with postnatal depression | • Minimally structured, individual interviews | • Thematic analysis • (+) Data saturation | Two themes – women’s “bad thoughts” and their four types of responses to fear of harm (with frequencies) |
• Australia | • Understand • Experience of peripherally inserted central catheter insertion | • (+) Reference • (+) Rationale • | NR | • Purposeful sampling • 10 patients | • Semistructured, individual interviews • Interview guide | • Thematic analysis • (+) Data saturation | Four themes regarding patients’ experiences of peripherally inserted central catheter insertion |
• USA | • Discover • Context, values, and background meaning of cultural competency | • (+) Reference • (+) Rationale • | Focused on cultural competence | • Purposive, maximum variation, and network • 20 experts | • Semistructured, individual interviews | • Within-case and across-case analysis • (-) Data saturation | Three themes regarding cultural competency |
• USA | • Explore and describe • Cancer experience | • (+) Reference • (+) Rationale • | NR | • Unspecified • 15 patients | • Longitudinal individual interviews (4 time points) • 40 interviews | • Inductive content analysis • (-) Data saturation | Processes and themes about adolescent identify work and cancer identify work across the illness trajectory |
• Sweden | • Explore • Experiences of giving support to patients during the transition | • (-) Reference • (-) Rationale | Focused on support and transition | • Unspecified (but likely purposeful sampling) • 8 nurses | • Semistructured Individual interviews • Interview guide | • Content analysis • (-) Data saturation | One theme, three main categories, and eight associated categories |
• Taiwan | • Describe • Process of women’s recovery from stillbirth | • (+) Reference • (+) Rationale • | NR | • Purposeful sampling • 21 women | • Individual interview techniques | • Inductive analytic approaches ( ) • (+) Data saturation | Three stages (themes) regarding the recovery process of Taiwanese women with stillbirth |
• Iran | • Describe • Perspectives of causes of medication errors | • (+) Reference • (+) Rationale • | NR | • Purposeful sampling • 24 nursing students | • Focus-group interviews • Observations with notes | • Content analysis • (-) Data saturation | Two main themes about nursing students’ perceptions on causes of medication errors |
• Iran | • Explore • Image of nursing | • (-) Reference • (-) Rationale | NR | • Purposeful sampling • 18 male nurses | • Semistructured individual, interviews • Field notes | • Content analysis • (-) Data saturation | Two main views (themes) on nursing presented with subthemes per view |
• Spain | • Ascertain • Barriers to sexual expression | • (-) Reference • (-) Rationale | NR | • Maximum variation • 100 staff and residents | • Semistructured, individual interview | • Content analysis • (-) Data saturation | 40% of participants without identification of barriers and 60% with seven most cited barriers to sexual expression in the long-term care setting |
• Canada | • Explore • Perceptions of empowerment in academic nursing environments | • (+) Reference • (+) Rationale • Sandelowski ( , ) | Theories of structural power in organizations and psychological empowerment | • Unspecified • 8 clinical instructors | • Semistructured, individual • interview guide | • Unspecified (but used pre-determined concepts) • (+) Data saturation | Structural empowerment and psychological empowerment described using predetermined concepts |
• China | • Investigate • Meaning of life and health experience with chronic illness | • (+) Reference • (+) Rationale • Sandelowski ( , ) | Positive health philosophy | • Purposive, convenience sampling • 11 patients | • Individual interviews • Observations of daily behavior with field notes | • Thematic analysis • (-) Data saturation | Four themes regarding the meaning of life and health when living with chronic illnesses |
Note . NR = not reported
Justification for use of a QD design was evident in close to half (47.3%) of the 55 publications. While most researchers clearly described recruitment strategies (80%) and data collection methods (100%), justification for how the study setting was selected was only identified in 38.2% of the articles and almost 75% of the articles did not include any reason for the choice of data collection methods (e.g., focus-group interviews). In the vast majority (90.9%) of the articles, researchers did not explain their involvement and positionality during the process of recruitment and data collection or during data analysis (63.6%). Ethical standards were reported in greater than 89% of all articles and most articles included an in-depth description of data analysis (83.6%) and development of categories or themes (92.7%). Finally, all researchers clearly stated their findings in relation to research questions/objectives. Researchers of 83.3% of the articles discussed the credibility of their findings (see Table 1 ).
In statements of study objectives and/or questions, the most frequently used verbs were “explore” ( n = 22) and “describe” ( n = 17). Researchers also used “identify” ( n = 3), “understand” ( n = 4), or “investigate” ( n = 2). Most articles focused on participants’ experiences related to certain phenomena ( n = 18), facilitators/challenges/factors/reasons ( n = 14), perceptions about specific care/nursing practice/interventions ( n = 11), and knowledge/attitudes/beliefs ( n = 3).
A total of 30 articles included references for QD. The most frequently cited references ( n = 23) were “Whatever happened to qualitative description?” ( Sandelowski, 2000 ) and “What’s in a name? Qualitative description revisited” ( Sandelowski, 2010 ). Other references cited included “Qualitative description – the poor cousin of health research?” ( Neergaard et al., 2009 ), “Reaching the parts other methods cannot reach: an introduction to qualitative methods in health and health services research” ( Pope & Mays, 1995 ), and general research textbooks ( Polit & Beck, 2004 , 2012 ).
In 26 articles (and not necessarily the same as those citing specific references to QD), researchers provided a rationale for selecting QD. Most researchers chose QD because this approach aims to produce a straight description and comprehensive summary of the phenomenon of interest using participants’ language and staying close to the data (or using low inference).
Authors of two articles distinctly stated a QD design, yet also acknowledged grounded-theory or phenomenological overtones by adopting some techniques from these qualitative traditions ( Michael, O'Callaghan, Baird, Hiscock, & Clayton, 2014 ; Peacock, Hammond-Collins, & Forbes, 2014 ). For example, Michael et al. (2014 , p. 1066) reported:
The research used a qualitative descriptive design with grounded theory overtones ( Sandelowski, 2000 ). We sought to provide a comprehensive summary of participants’ views through theoretical sampling; multiple data sources (focus groups [FGs] and interviews); inductive, cyclic, and constant comparative analysis; and condensation of data into thematic representations ( Corbin & Strauss, 1990 , 2008 ).
Authors of four additional articles included language suggestive of a grounded-theory or phenomenological tradition, e.g., by employing a constant comparison technique or translating themes stated in participants’ language into the primary language of the researchers during data analysis ( Asemani et al., 2014 ; Li, Lee, Chen, Jeng, & Chen, 2014 ; Ma, 2014 ; Soule, 2014 ). Additionally, Li et al. (2014) specifically reported use of a grounded-theory approach.
In most (n = 48) articles, researchers did not specify any theoretical or philosophical framework. Of those articles in which a framework or philosophical stance was included, the authors of five articles described the framework as guiding the development of an interview guide ( Al-Zadjali, Keller, Larkey, & Evans, 2014 ; DeBruyn, Ochoa-Marin, & Semenic, 2014 ; Fantasia, Sutherland, Fontenot, & Ierardi, 2014 ; Ma, 2014 ; Wiens, Babenko-Mould, & Iwasiw, 2014 ). In two articles, data analysis was described as including key concepts of a framework being used as pre-determined codes or categories ( Al-Zadjali et al., 2014 ; Wiens et al., 2014 ). Oosterveld-Vlug et al. (2014) and Zhang, Shan, and Jiang (2014) discussed a conceptual model and underlying philosophy in detail in the background or discussion section, although the model and philosophy were not described as being used in developing interview questions or analyzing data.
In 38 of the 55 articles, researchers reported ‘purposeful sampling’ or some derivation of purposeful sampling such as convenience ( n = 10), maximum variation ( n = 8), snowball ( n = 3), and theoretical sampling ( n = 1). In three instances ( Asemani et al., 2014 ; Chan & Lopez, 2014 ; Soule, 2014 ), multiple sampling strategies were described, for example, a combination of snowball, convenience, and maximum variation sampling. In articles where maximum variation sampling was employed, “variation” referred to seeking diversity in participants’ demographics ( n = 7; e.g., age, gender, and education level), while one article did not include details regarding how their maximum variation sampling strategy was operationalized ( Marcinowicz, Abramowicz, Zarzycka, Abramowicz, & Konstantynowicz, 2014 ). Authors of 17 articles did not specify their sampling techniques.
Sample sizes ranged from 8 to 1,932 with nine studies in the 8–10 participant range and 24 studies in the 11–20 participant range. The participant range of 21–30 and 31–50 was reported in eight articles each. Six studies included more than 50 participants. Two of these articles depicted quite large sample sizes (N=253, Hart & Mareno, 2014 ; N=1,932, Lyndon et al., 2014 ) and the authors of these articles described the use of survey instruments and analysis of responses to open-ended questions. This was in contrast to studies with smaller sample sizes where individual interviews and focus groups were more commonly employed.
In a majority of studies, researchers collected data through individual ( n = 39) and/or focus-group ( n = 14) interviews that were semistructured. Most researchers reported that interviews were audiotaped ( n = 51) and interview guides were described as the primary data collection tool in 29 of the 51 studies. In some cases, researchers also described additional data sources, for example, taking memos or field notes during participant observation sessions or as a way to reflect their thoughts about interviews ( n = 10). Written responses to open-ended questions in survey questionnaires were another type of data source in a small number of studies ( n = 4).
The analysis strategy most commonly used in the QD studies included in this review was qualitative content analysis ( n = 30). Among the studies where this technique was used, most researchers described an inductive approach; researchers of two studies analyzed data both inductively and deductively. Thematic analysis was adopted in 14 studies and the constant comparison technique in 10 studies. In nine studies, researchers employed multiple techniques to analyze data including qualitative content analysis with constant comparison ( Asemani et al., 2014 ; DeBruyn et al., 2014 ; Holland, Christensen, Shone, Kearney, & Kitzman, 2014 ; Li et al., 2014 ) and thematic analysis with constant comparison ( Johansson, Hildingsson, & Fenwick, 2014 ; Oosterveld-Vlug et al., 2014 ). In addition, five teams conducted descriptive statistical analysis using both quantitative and qualitative data and counting the frequencies of codes/themes ( Ewens, Chapman, Tulloch, & Hendricks, 2014 ; Miller, 2014 ; Santos, Sandelowski, & Gualda, 2014 ; Villar, Celdran, Faba, & Serrat, 2014 ) or targeted events through video monitoring ( Martorella, Boitor, Michaud, & Gelinas, 2014 ). Tseng, Chen, and Wang (2014) cited Thorne, Reimer Kirkham, and O’Flynn-Magee (2004)’s interpretive description as the inductive analytic approach. In five out of 55 articles, researchers did not specifically name their analysis strategies, despite including descriptions about procedural aspects of data analysis. Researchers of 20 studies reported that data saturation for their themes was achieved.
Researchers described participants’ experiences of health care, interventions, or illnesses in 18 articles and presented straightforward, focused, detailed descriptions of facilitators, challenges, factors, reasons, and causes in 15 articles. Participants’ perceptions of specific care, interventions, or programs were described in detail in 11 articles. All researchers presented their findings with extensive descriptions including themes or categories. In 25 of 55 articles, figures or tables were also presented to illustrate or summarize the findings. In addition, the authors of three articles summarized, organized, and described their data using key concepts of conceptual models ( Al-Zadjali et al., 2014 ; Oosterveld-Vlug et al., 2014 ; Wiens et al., 2014 ). Martorella et al. (2014) assessed acceptability and feasibility of hand massage therapy and arranged their findings in relation to pre-determined indicators of acceptability and feasibility. In one longitudinal QD study ( Kneck, Fagerberg, Eriksson, & Lundman, 2014 ), the researchers presented the findings as several key patterns of learning for persons living with diabetes; in another longitudinal QD study ( Stegenga & Macpherson, 2014 ), findings were presented as processes and themes regarding patients’ identity work across the cancer trajectory. In another two studies, the researchers described and compared themes or categories from two different perspectives, such as patients and nurses ( Canzan, Heilemann, Saiani, Mortari, & Ambrosi, 2014 ) or parents and children ( Marcinowicz et al., 2014 ). Additionally, Ma (2014) reported themes using both participants’ language and the researcher’s language.
In this systematic review, we examined and reported specific characteristics of methods and findings reported in journal articles self-identified as QD and published during one calendar year. To accomplish this we identified 55 articles that met inclusion criteria, performed a quality appraisal following CASP guidelines, and extracted and analyzed data focusing on QD features. In general, three primary findings emerged. First, despite inconsistencies, most QD publications had the characteristics that were originally observed by Sandelowski (2000) and summarized by other limited available QD literature. Next, there are no clear boundaries in methods used in the QD studies included in this review; in a number of studies, researchers adopted and combined techniques originating from other qualitative traditions to obtain rich data and increase their understanding of the phenomenon under investigation. Finally, justification for how QD was chosen and why it would be an appropriate fit for a particular study is an area in need of increased attention.
In general, the overall characteristics were consistent with design features of QD studies described in the literature ( Neergaard et al., 2009 ; Sandelowski, 2000 , 2010 ; Vaismoradi et al., 2013 ). For example, many authors reported that study objectives were to describe or explore participants’ experiences and factors related to certain phenomena, events, or interventions. In most cases, these authors cited Sandelowski (2000) as a reference for this particular characteristic. It was rare that theoretical or philosophical frameworks were identified, which also is consistent with descriptions of QD. In most studies, researchers used purposeful sampling and its derivative sampling techniques, collected data through interviews, and analyzed data using qualitative content analysis or thematic analysis. Moreover, all researchers presented focused or comprehensive, descriptive summaries of data including themes or categories answering their research questions. These characteristics do not indicate that there are correct ways to do QD studies; rather, they demonstrate how others designed and produced QD studies.
In several studies, researchers combined techniques that originated from other qualitative traditions for sampling, data collection, and analysis. This flexibility or variability, a key feature of recently published QD studies, may indicate that there are no clear boundaries in designing QD studies. Sandelowski (2010) articulated: “in the actual world of research practice, methods bleed into each other; they are so much messier than textbook depictions” (p. 81). Hammersley (2007) also observed:
“We are not so much faced with a set of clearly differentiated qualitative approaches as with a complex landscape of variable practice in which the inhabitants use a range of labels (‘ethnography’, ‘discourse analysis’, ‘life history work’, narrative study’, ……, and so on) in diverse and open-ended ways in order to characterize their orientation, and probably do this somewhat differently across audiences and occasions” (p. 293).
This concept of having no clear boundaries in methods when designing a QD study should enable researchers to obtain rich data and produce a comprehensive summary of data through various data collection and analysis approaches to answer their research questions. For example, using an ethnographical approach (e.g., participant observation) in data collection for a QD study may facilitate an in-depth description of participants’ nonverbal expressions and interactions with others and their environment as well as situations or events in which researchers are interested ( Kawulich, 2005 ). One example found in our review is that Adams et al. (2014) explored family members’ responses to nursing communication strategies for patients in intensive care units (ICUs). In this study, researchers conducted interviews with family members, observed interactions between healthcare providers, patients, and family members in ICUs, attended ICU rounds and family meetings, and took field notes about their observations and reflections. Accordingly, the variability in methods provided Adams and colleagues (2014) with many different aspects of data that were then used to complement participants’ interviews (i.e., data triangulation). Moreover, by using a constant comparison technique in addition to qualitative content analysis or thematic analysis in QD studies, researchers compare each case with others looking for similarities and differences as well as reasoning why differences exist, to generate more general understanding of phenomena of interest ( Thorne, 2000 ). In fact, this constant comparison analysis is compatible with qualitative content analysis and thematic analysis and we found several examples of using this approach in studies we reviewed ( Asemani et al., 2014 ; DeBruyn et al., 2014 ; Holland et al., 2014 ; Johansson et al., 2014 ; Li et al., 2014 ; Oosterveld-Vlug et al., 2014 ).
However, this flexibility or variability in methods of QD studies may cause readers’ as well as researchers’ confusion in designing and often labeling qualitative studies ( Neergaard et al., 2009 ). Especially, it could be difficult for scholars unfamiliar with qualitative studies to differentiate QD studies with “hues, tones, and textures” of qualitative traditions ( Sandelowski, 2000 , p. 337) from grounded theory, phenomenological, and ethnographical research. In fact, the major difference is in the presentation of the findings (or outcomes of qualitative research) ( Neergaard et al., 2009 ; Sandelowski, 2000 ). The final products of grounded theory, phenomenological, and ethnographical research are a generation of a theory, a description of the meaning or essence of people’s lived experience, and an in-depth, narrative description about certain culture, respectively, through researchers’ intensive/deep interpretations, reflections, and/or transformation of data ( Streubert & Carpenter, 2011 ). In contrast, QD studies result in “a rich, straight description” of experiences, perceptions, or events using language from the collected data ( Neergaard et al., 2009 ) through low-inference (or data-near) interpretations during data analysis ( Sandelowski, 2000 , 2010 ). This feature is consistent with our finding regarding presentation of findings: in all QD articles included in this systematic review, the researchers presented focused or comprehensive, descriptive summaries to their research questions.
Finally, an explanation or justification of why a QD approach was chosen or appropriate for the study aims was not found in more than half of studies in the sample. While other qualitative approaches, including grounded theory, phenomenology, ethnography, and narrative analysis, are used to better understand people’s thoughts, behaviors, and situations regarding certain phenomena ( Sullivan-Bolyai et al., 2005 ), as noted above, the results will likely read differently than those for a QD study ( Carter & Little, 2007 ). Therefore, it is important that researchers accurately label and justify their choices of approach, particularly for studies focused on participants’ experiences, which could be addressed with other qualitative traditions. Justifying one’s research epistemology, methodology, and methods allows readers to evaluate these choices for internal consistency, provides context to assist in understanding the findings, and contributes to the transparency of choices, all of which enhance the rigor of the study ( Carter & Little, 2007 ; Wu, Thompson, Aroian, McQuaid, & Deatrick, 2016 ).
Use of the CASP tool drew our attention to the credibility and usefulness of the findings of the QD studies included in this review. Although justification for study design and methods was lacking in many articles, most authors reported techniques of recruitment, data collection, and analysis that appeared. Internal consistencies among study objectives, methods, and findings were achieved in most studies, increasing readers’ confidence that the findings of these studies are credible and useful in understanding under-explored phenomenon of interest.
In summary, our findings support the notion that many scholars employ QD and include a variety of commonly observed characteristics in their study design and subsequent publications. Based on our review, we found that QD as a scholarly approach allows flexibility as research questions and study findings emerge. We encourage authors to provide as many details as possible regarding how QD was chosen for a particular study as well as details regarding methods to facilitate readers’ understanding and evaluation of the study design and rigor. We acknowledge the challenge of strict word limitation with submissions to print journals; potential solutions include collaboration with journal editors and staff to consider creative use of charts or tables, or using more citations and less text in background sections so that methods sections are robust.
Several limitations of this review deserve mention. First, only articles where researchers explicitly stated in the main body of the article that a QD design was employed were included. In contrast, articles labeled as QD in only the title or abstract, or without their research design named were not examined due to the lack of certainty that the researchers actually carried out a QD study. As a result, we may have excluded some studies where a QD design was followed. Second, only one database was searched and therefore we did not identify or describe potential studies following a QD approach that were published in non-PubMed databases. Third, our review is limited by reliance on what was included in the published version of a study. In some cases, this may have been a result of word limits or specific styles imposed by journals, or inconsistent reporting preferences of authors and may have limited our ability to appraise the general adequacy with the CASP tool and examine specific characteristics of these studies.
A systematic review was conducted by examining QD research articles focused on nursing-related phenomena and published in one calendar year. Current patterns include some characteristics of QD studies consistent with the previous observations described in the literature, a focus on the flexibility or variability of methods in QD studies, and a need for increased explanations of why QD was an appropriate label for a particular study. Based on these findings, recommendations include encouragement to authors to provide as many details as possible regarding the methods of their QD study. In this way, readers can thoroughly consider and examine if the methods used were effective and reasonable in producing credible and useful findings.
This work was supported in part by the John A. Hartford Foundation’s National Hartford Centers of Gerontological Nursing Excellence Award Program.
Hyejin Kim is a Ruth L. Kirschstein NRSA Predoctoral Fellow (F31NR015702) and 2013–2015 National Hartford Centers of Gerontological Nursing Excellence Patricia G. Archbold Scholar. Justine Sefcik is a Ruth L. Kirschstein Predoctoral Fellow (F31NR015693) through the National Institutes of Health, National Institute of Nursing Research.
Conflict of Interest Statement
The Authors declare that there is no conflict of interest.
Hyejin Kim, MSN, CRNP, Doctoral Candidate, University of Pennsylvania School of Nursing.
Justine S. Sefcik, MS, RN, Doctoral Candidate, University of Pennsylvania School of Nursing.
Christine Bradway, PhD, CRNP, FAAN, Associate Professor of Gerontological Nursing, University of Pennsylvania School of Nursing.
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Learning objectives.
There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions; to extensive, in-depth interviews; to well-controlled experiments.
The three main categories of psychological research are descriptive, correlational, and experimental research. Research studies that do not test specific relationships between variables are called descriptive, or qualitative, studies . These studies are used to describe general or specific behaviors and attributes that are observed and measured. In the early stages of research, it might be difficult to form a hypothesis, especially when there is not any existing literature in the area. In these situations designing an experiment would be premature, as the question of interest is not yet clearly defined as a hypothesis. Often a researcher will begin with a non-experimental approach, such as a descriptive study, to gather more information about the topic before designing an experiment or correlational study to address a specific hypothesis. Descriptive research is distinct from correlational research , in which psychologists formally test whether a relationship exists between two or more variables. Experimental research goes a step further beyond descriptive and correlational research and randomly assigns people to different conditions, using hypothesis testing to make inferences about how these conditions affect behavior. It aims to determine if one variable directly impacts and causes another. Correlational and experimental research both typically use hypothesis testing, whereas descriptive research does not.
Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While surveys allow results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data. Some researchers conduct archival research by using existing records. While existing records can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected.
Correlational research can find a relationship between two variables, but the only way a researcher can claim that the relationship between the variables is cause and effect is to perform an experiment. In experimental research, which will be discussed later, there is a tremendous amount of control over variables of interest. While performing an experiment is a powerful approach, experiments are often conducted in very artificial settings, which calls into question the validity of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns.
The three main types of descriptive studies are case studies, naturalistic observation, and surveys.
Psychologists can use a detailed description of one person or a small group based on careful observation. Case studies are intensive studies of individuals and have commonly been seen as a fruitful way to come up with hypotheses and generate theories. Case studies add descriptive richness. Case studies are also useful for formulating concepts, which are an important aspect of theory construction. Through fine-grained knowledge and description, case studies can fully specify the causal mechanisms in a way that may be harder in a large study.
Sigmund Freud developed many theories from case studies (Anna O., Little Hans, Wolf Man, Dora, etc.). F or example, he conducted a case study of a man, nicknamed “Rat Man,” in which he claimed that this patient had been cured by psychoanalysis. T he nickname derives from the fact that among the patient’s many compulsions, he had an obsession with nightmarish fantasies about rats.
Today, more commonly, case studies reflect an up-close, in-depth, and detailed examination of an individual’s course of treatment. Case studies typically include a complete history of the subject’s background and response to treatment. From the particular client’s experience in therapy, the therapist’s goal is to provide information that may help other therapists who treat similar clients.
Case studies are generally a single-case design, but can also be a multiple-case design, where replication instead of sampling is the criterion for inclusion. Like other research methodologies within psychology, the case study must produce valid and reliable results in order to be useful for the development of future research. Distinct advantages and disadvantages are associated with the case study in psychology.
A commonly described limit of case studies is that they do not lend themselves to generalizability . The other issue is that the case study is subject to the bias of the researcher in terms of how the case is written, and that cases are chosen because they are consistent with the researcher’s preconceived notions, resulting in biased research. Another common problem in case study research is that of reconciling conflicting interpretations of the same case history.
Despite these limitations, there are advantages to using case studies. One major advantage of the case study in psychology is the potential for the development of novel hypotheses of the cause of abnormal behavior for later testing. Second, the case study can provide detailed descriptions of specific and rare cases and help us study unusual conditions that occur too infrequently to study with large sample sizes. The major disadvantage is that case studies cannot be used to determine causation, as is the case in experimental research, where the factors or variables hypothesized to play a causal role are manipulated or controlled by the researcher.
Some well-known case studies that related to abnormal psychology include the following:
If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, but do you think hand washing after every trip to the restroom is really that universal?
This is very similar to the phenomenon mentioned earlier in this module: many individuals do not feel comfortable answering a question honestly. But if we are committed to finding out the facts about handwashing, we have other options available to us.
Suppose we send a researcher to a school playground to observe how aggressive or socially anxious children interact with peers. Will our observer blend into the playground environment by wearing a white lab coat, sitting with a clipboard, and staring at the swings? We want our researcher to be inconspicuous and unobtrusively positioned—perhaps pretending to be a school monitor while secretly recording the relevant information. This type of observational study is called naturalistic observation : observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).
It is critical that the observer be as unobtrusive and as inconspicuous as possible: when people know they are being watched, they are less likely to behave naturally. For example, psychologists have spent weeks observing the behavior of homeless people on the streets, in train stations, and bus terminals. They try to ensure that their naturalistic observations are unobtrusive, so as to minimize interference with the behavior they observe. Nevertheless, the presence of the observer may distort the behavior that is observed, and this must be taken into consideration (Figure 1).
The greatest benefit of naturalistic observation is the validity, or accuracy, of information collected unobtrusively in a natural setting. Having individuals behave as they normally would in a given situation means that we have a higher degree of ecological validity, or realism, than we might achieve with other research approaches. Therefore, our ability to generalize the findings of the research to real-world situations is enhanced. If done correctly, we need not worry about people modifying their behavior simply because they are being observed. Sometimes, people may assume that reality programs give us a glimpse into authentic human behavior. However, the principle of inconspicuous observation is violated as reality stars are followed by camera crews and are interviewed on camera for personal confessionals. Given that environment, we must doubt how natural and realistic their behaviors are.
The major downside of naturalistic observation is that they are often difficult to set up and control. Although something as simple as observation may seem like it would be a part of all research methods, participant observation is a distinct methodology that involves the researcher embedding themselves into a group in order to study its dynamics. For example, Festinger, Riecken, and Shacter (1956) were very interested in the psychology of a particular cult. However, this cult was very secretive and wouldn’t grant interviews to outside members. So, in order to study these people, Festinger and his colleagues pretended to be cult members, allowing them access to the behavior and psychology of the cult. Despite this example, it should be noted that the people being observed in a participant observation study usually know that the researcher is there to study them. [1]
Another potential problem in observational research is observer bias . Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability : a measure of reliability that assesses the consistency of observations by different observers.
Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally (Figure 3). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
Surveys allow researchers to gather data from larger samples than may be afforded by other research methods . A sample is a subset of individuals selected from a population , which is the overall group of individuals that the researchers are interested in. Researchers study the sample and seek to generalize their findings to the population.
There is both strength and weakness in surveys when compared to case studies. By using surveys, we can collect information from a larger sample of people. A larger sample is better able to reflect the actual diversity of the population, thus allowing better generalizability. Therefore, if our sample is sufficiently large and diverse, we can assume that the data we collect from the survey can be generalized to the larger population with more certainty than the information collected through a case study. However, given the greater number of people involved, we are not able to collect the same depth of information on each person that would be collected in a case study.
Another potential weakness of surveys is something we touched on earlier in this module: people do not always give accurate responses. They may lie, misremember, or answer questions in a way that they think makes them look good. For example, people may report drinking less alcohol than is actually the case.
Any number of research questions can be answered through the use of surveys. One real-world example is the research conducted by Jenkins, Ruppel, Kizer, Yehl, and Griffin (2012) about the backlash against the U.S. Arab-American community following the terrorist attacks of September 11, 2001. Jenkins and colleagues wanted to determine to what extent these negative attitudes toward Arab-Americans still existed nearly a decade after the attacks occurred. In one study, 140 research participants filled out a survey with 10 questions, including questions asking directly about the participant’s overt prejudicial attitudes toward people of various ethnicities. The survey also asked indirect questions about how likely the participant would be to interact with a person of a given ethnicity in a variety of settings (such as, “How likely do you think it is that you would introduce yourself to a person of Arab-American descent?”). The results of the research suggested that participants were unwilling to report prejudicial attitudes toward any ethnic group. However, there were significant differences between their pattern of responses to questions about social interaction with Arab-Americans compared to other ethnic groups: they indicated less willingness for social interaction with Arab-Americans compared to the other ethnic groups. This suggested that the participants harbored subtle forms of prejudice against Arab-Americans, despite their assertions that this was not the case (Jenkins et al., 2012).
Research has shown that parental depressive symptoms are linked to a number of negative child outcomes. A classmate of yours is interested in the associations between parental depressive symptoms and actual child behaviors in everyday life [2] because this associations remains largely unknown. After reading this section, what do you think is the best way to better understand such associations? Which method might result in the most valid data?
clinical or case study: observational research study focusing on one or a few people
correlational research: tests whether a relationship exists between two or more variables
descriptive research: research studies that do not test specific relationships between variables; they are used to describe general or specific behaviors and attributes that are observed and measured
experimental research: tests a hypothesis to determine cause-and-effect relationships
generalizability: inferring that the results for a sample apply to the larger population
inter-rater reliability: measure of agreement among observers on how they record and classify a particular event
naturalistic observation: observation of behavior in its natural setting
observer bias: when observations may be skewed to align with observer expectations
population: overall group of individuals that the researchers are interested in
sample: subset of individuals selected from the larger population
survey: list of questions to be answered by research participants—given as paper-and-pencil questionnaires, administered electronically, or conducted verbally—allowing researchers to collect data from a large number of people
Descriptive Research and Case Studies Copyright © by Meredith Palm is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.
Published on December 21, 2020 by Pritha Bhandari . Revised on January 17, 2024.
The results section of a quantitative research paper is where you summarize your data and report the findings of any relevant statistical analyses.
The APA manual provides rigorous guidelines for what to report in quantitative research papers in the fields of psychology, education, and other social sciences.
Use these standards to answer your research questions and report your data analyses in a complete and transparent way.
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What goes in your results section, introduce your data, summarize your data, report statistical results, presenting numbers effectively, what doesn’t belong in your results section, frequently asked questions about results in apa.
In APA style, the results section includes preliminary information about the participants and data, descriptive and inferential statistics, and the results of any exploratory analyses.
Include these in your results section:
Write up the results in the past tense because you’re describing the outcomes of a completed research study.
The AI-powered Citation Checker helps you avoid common mistakes such as:
Before diving into your research findings, first describe the flow of participants at every stage of your study and whether any data were excluded from the final analysis.
It’s necessary to report any attrition, which is the decline in participants at every sequential stage of a study. That’s because an uneven number of participants across groups sometimes threatens internal validity and makes it difficult to compare groups. Be sure to also state all reasons for attrition.
If your study has multiple stages (e.g., pre-test, intervention, and post-test) and groups (e.g., experimental and control groups), a flow chart is the best way to report the number of participants in each group per stage and reasons for attrition.
Also report the dates for when you recruited participants or performed follow-up sessions.
Another key issue is the completeness of your dataset. It’s necessary to report both the amount and reasons for data that was missing or excluded.
Data can become unusable due to equipment malfunctions, improper storage, unexpected events, participant ineligibility, and so on. For each case, state the reason why the data were unusable.
Some data points may be removed from the final analysis because they are outliers—but you must be able to justify how you decided what to exclude.
If you applied any techniques for overcoming or compensating for lost data, report those as well.
For clinical studies, report all events with serious consequences or any side effects that occured.
Descriptive statistics summarize your data for the reader. Present descriptive statistics for each primary, secondary, and subgroup analysis.
Don’t provide formulas or citations for commonly used statistics (e.g., standard deviation) – but do provide them for new or rare equations.
The exact descriptive statistics that you report depends on the types of data in your study. Categorical variables can be reported using proportions, while quantitative data can be reported using means and standard deviations . For a large set of numbers, a table is the most effective presentation format.
Include sample sizes (overall and for each group) as well as appropriate measures of central tendency and variability for the outcomes in your results section. For every point estimate , add a clearly labelled measure of variability as well.
Be sure to note how you combined data to come up with variables of interest. For every variable of interest, explain how you operationalized it.
According to APA journal standards, it’s necessary to report all relevant hypothesis tests performed, estimates of effect sizes, and confidence intervals.
When reporting statistical results, you should first address primary research questions before moving onto secondary research questions and any exploratory or subgroup analyses.
Present the results of tests in the order that you performed them—report the outcomes of main tests before post-hoc tests, for example. Don’t leave out any relevant results, even if they don’t support your hypothesis.
For each statistical test performed, first restate the hypothesis , then state whether your hypothesis was supported and provide the outcomes that led you to that conclusion.
Report the following for each hypothesis test:
When reporting complex data analyses, such as factor analysis or multivariate analysis, present the models estimated in detail, and state the statistical software used. Make sure to report any violations of statistical assumptions or problems with estimation.
For each hypothesis test performed, you should present confidence intervals and estimates of effect sizes .
Confidence intervals are useful for showing the variability around point estimates. They should be included whenever you report population parameter estimates.
Effect sizes indicate how impactful the outcomes of a study are. But since they are estimates, it’s recommended that you also provide confidence intervals of effect sizes.
Briefly report the results of any other planned or exploratory analyses you performed. These may include subgroup analyses as well.
Subgroup analyses come with a high chance of false positive results, because performing a large number of comparison or correlation tests increases the chances of finding significant results.
If you find significant results in these analyses, make sure to appropriately report them as exploratory (rather than confirmatory) results to avoid overstating their importance.
While these analyses can be reported in less detail in the main text, you can provide the full analyses in supplementary materials.
To effectively present numbers, use a mix of text, tables , and figures where appropriate:
Since these are general guidelines, use your own judgment and feedback from others for effective presentation of numbers.
Tables and figures should be numbered and have titles, along with relevant notes. Make sure to present data only once throughout the paper and refer to any tables and figures in the text.
It’s important to follow capitalization , italicization, and abbreviation rules when referring to statistics in your paper. There are specific format guidelines for reporting statistics in APA , as well as general rules about writing numbers .
If you are unsure of how to present specific symbols, look up the detailed APA guidelines or other papers in your field.
It’s important to provide a complete picture of your data analyses and outcomes in a concise way. For that reason, raw data and any interpretations of your results are not included in the results section.
It’s rarely appropriate to include raw data in your results section. Instead, you should always save the raw data securely and make them available and accessible to any other researchers who request them.
Making scientific research available to others is a key part of academic integrity and open science.
This belongs in your discussion section. Your results section is where you objectively report all relevant findings and leave them open for interpretation by readers.
While you should state whether the findings of statistical tests lend support to your hypotheses, refrain from forming conclusions to your research questions in the results section.
For the sake of concise writing, you can safely assume that readers of your paper have professional knowledge of how statistical inferences work.
In an APA results section , you should generally report the following:
According to the APA guidelines, you should report enough detail on inferential statistics so that your readers understand your analyses.
You should also present confidence intervals and estimates of effect sizes where relevant.
In APA style, statistics can be presented in the main text or as tables or figures . To decide how to present numbers, you can follow APA guidelines:
Results are usually written in the past tense , because they are describing the outcome of completed actions.
The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.
In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.
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Descriptive research methods. Descriptive research is usually defined as a type of quantitative research, though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable.. Surveys. Survey research allows you to gather large volumes of data that can be analyzed for frequencies, averages ...
Health research: Descriptive research design is useful in health research to describe the prevalence and distribution of a disease or health condition in a population. ... Relies on self-report data: Descriptive research design often relies on self-report data, such as surveys or questionnaires. This type of data may be subject to biases, such ...
Descriptive research is a methodological approach that seeks to depict the characteristics of a phenomenon or subject under investigation. In scientific inquiry, it serves as a foundational tool for researchers aiming to observe, record, and analyze the intricate details of a particular topic. This method provides a rich and detailed account ...
INTRODUCTION. In our previous article in this series, [1] we introduced the concept of "study designs"- as "the set of methods and procedures used to collect and analyze data on variables specified in a particular research question.". Study designs are primarily of two types - observational and interventional, with the former being ...
Descriptive research is a research method describing the characteristics of the population or phenomenon studied. This descriptive methodology focuses more on the "what" of the research subject than the "why" of the research subject. The method primarily focuses on describing the nature of a demographic segment without focusing on ...
Descriptive research may identify areas in need of additional research and relationships between variables that require future study. Descriptive research is often referred to as "hypothesis generating research." Depending on the data collection method used, descriptive studies can generate rich datasets on large and diverse samples. Limitations:
Definition: As its name says, descriptive research describes the characteristics of the problem, phenomenon, situation, or group under study. So the goal of all descriptive studies is to explore the background, details, and existing patterns in the problem to fully understand it. In other words, preliminary research.
Descriptive research is an exploratory research method.It enables researchers to precisely and methodically describe a population, circumstance, or phenomenon.. As the name suggests, descriptive research describes the characteristics of the group, situation, or phenomenon being studied without manipulating variables or testing hypotheses.This can be reported using surveys, observational ...
Descriptive research methods. Descriptive research is usually defined as a type of quantitative research, though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable.. Surveys. Survey research allows you to gather large volumes of data that can be analysed for frequencies, averages ...
As such, descriptive design is great for¹: Case reports and surveys: Descriptive research is a valuable tool for in-depth examination of uncommon diseases and other unique occurrences. In the context of surveys, it can help researchers meticulously analyse extensive datasets. A survey conducted to measure the changes in the levels of customer ...
Benefits of Descriptive Research: Limitations of Descriptive Research: Rich Data: Provides a comprehensive and detailed profile of the subject or issue through rich data, offering a thorough understanding (Gresham, 2016). Lack of Control: Cannot control variables or external factors, potentially influencing the accuracy and reliability of the data. Basis for Further Research: Helps to identify ...
Definition of descriptive research. Descriptive research is defined as a research method that observes and describes the characteristics of a particular group, situation, or phenomenon. The goal is not to establish cause and effect relationships but rather to provide a detailed account of the situation.
Box 1. Descriptive Analysis Is a Critical Component of Research Box 2. Examples of Using Descriptive Analyses to Diagnose Need and Target Intervention on the Topic of "Summer Melt" Box 3. An Example of Using Descriptive Analysis to Evaluate Plausible Causes and Generate Hypotheses Box 4.
A descriptive correlation study was a research method that observes and characterizes the behavior of participants from a scientific standpoint in relation to factors in a setting. It seeks to ...
Descriptive Research vs. Exploratory Research. Descriptive research is a research method that focuses on providing a detailed and accurate account of a specific situation, group, or phenomenon. This type of research describes the characteristics, behaviors, or relationships within the given context without looking for an underlying cause.
Types of descriptive research. Observational method. Case studies. Surveys. Recap. Descriptive research methods are used to define the who, what, and where of human behavior and other ...
Abstract. Descriptive research is a study of status and is widely used in education, nutrition, epidemiology, and the behavioral sciences. Its value is based on the premise that problems can be solved and practices improved through observation, analysis, and description. The most common descriptive research method is the survey, which includes ...
Descriptive research design. Descriptive research design uses a range of both qualitative research and quantitative data (although quantitative research is the primary research method) to gather information to make accurate predictions about a particular problem or hypothesis. As a survey method, descriptive research designs will help ...
1. Purpose. The primary purpose of descriptive research is to describe the characteristics, behaviors, and attributes of a particular population or phenomenon. 2. Participants and Sampling. Descriptive research studies a particular population or sample that is representative of the larger population being studied.
Therefore, we talk about "generic" or "descriptive-interpretive" approaches to qualitative research that share in common an effort to describe, summarize, and classify what is present in the data, which always, as we explain in Chapter 4, involves a degree of interpretation.
Qualitative description (QD) is a label used in qualitative research for studies which are descriptive in nature, particularly for examining health care and nursing-related phenomena (Polit & Beck, 2009, 2014).QD is a widely cited research tradition and has been identified as important and appropriate for research questions focused on discovering the who, what, and where of events or ...
Surveys. Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally (Figure 3). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect ...
Present descriptive statistics for each primary, secondary, and subgroup analysis. Don't provide formulas or citations for commonly used statistics (e.g., standard deviation) - but do provide them for new or rare equations. Descriptive statistics. The exact descriptive statistics that you report depends on the types of data in your study.
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