PR2 Module 1.docx

  **TARGET** *This part enumerates the objectives of the module. This part shall give you an idea of the skills or competencies you are expected t...

  **TARGET** *This part enumerates the objectives of the module. This part shall give you an idea of the skills or competencies you are expected to acquire upon completion of this module.* ---------------------------------------------------------------------------------------------------------------------------- ---------------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- **TESTER** *This part includes an activity that aims to check what you already know about the lesson to take.*  **TEACHING** *In this portion, a new lesson will be introduced. This section provides a discussion of the lesson. This section aims to help you explore and understand new concepts and skills.* Icon Description automatically generated **TAKE AWAY** *This involves the reflective part of the module. It contains various activities that will allow you to reflect and state what you have learned.*  **TOUCHSTONE** *This is the assessment presented at the end of each module. This section aims to check how much have you learned from the module. This will also evaluate your level of mastery in achieving the learning competency.* **OBJECTIVES** At the end of the lesson, you are expected to be able to: 1. infer and explain patterns and themes from data **(CS\_RS11IVd-f-2)** 2. relate the findings with pertinent literature **(CS\_RS11IVd-f-3)** **CONTENT** **LECTURES** "Data analysis is central to credible qualitative research. The qualitative researcher is often described as the research instrument as his or her ability to understand, describe and interpret experiences and perceptions is the key to uncovering meaning in particular circumstances and contexts." (Maguire and Delahunt, 2017) **DRAWING THEMES AND PATTERNS** - **Themes** are features of participants' accounts characterising particular perceptions and/or experiences that the researcher sees as relevant to the research question. Themes come both from the data (an inductive approach) and from the investigator's prior theoretical understanding of the phenomenon under study (an a priori approach). A priori themes come from the characteristics of the phenomenon being studied; from already agreed on professional definitions found in literature reviews; from local, common sense constructs; and from researchers' values, theoretical orientations, and personal experiences (Bulmer1979;Strauss1987;Maxwell1996 as cited by Ryan & Bernard, 2003). - **Coding** is the process of identifying themes in accounts and attaching labels (codes) to index them. **Thematic Analysis** **Thematic analysis** is the process of identifying patterns or themes within qualitative data. Braun & Clarke (2006) suggest that it is the first qualitative method that should be learned as '..it provides core skills that will be useful for conducting many other kinds of analysis' (p.78) (cited by Maguire and Delahunt, 2017). The goal of a thematic analysis is to identify themes, i.e. patterns in the data that are important or interesting, and use these themes to address the research or say something about an issue. This is much more than simply summarising the data; a good thematic analysis interprets and makes sense of it. A common pitfall is to use the main interview questions as the themes (Clarke & Braun, 2013). **Braun & Clarke (2006) six-phase guide for conducting thematic analysis** 1. **Become familiar with the data.** The first step in any qualitative analysis is reading, and re-reading the transcripts. You should be very familiar with your entire body of data or data corpus (i.e. all the interviews and any other data you may be using) before you go any further. At this stage, it is useful to make notes and jot down early impressions. 2. **Generate initial codes.** Start to organise your data in a meaningful and systematic way. 3. **Search for themes.** As defined earlier, a theme is a pattern that captures something significant or interesting about the data and/or research question. As Braun & Clarke (2006) explain, there are no hard and fast rules about what makes a theme. A theme is characterised by its significance. 4. **Review themes.** Modify and develop the preliminary themes that were identified. Do they make sense? It is useful to gather together all the data that is relevant to each theme. 5. **Define themes.** 6. **Writing up.** **How to identify themes (Ryan and Bernard, 2003, Field Methods)** A. **Observational Techniques** 1. **Repetitions** 2. **Indigenous Typologies or Categories** 3. **Metaphors and Analogies** 4. **Transitions** 5. **Similarities and Differences** 6. **Linguistic Connectors** 7. **Missing Data** The next scrutiny-based approach works in reverse from typical theme identification techniques. Instead of asking, What is here?, you can ask, What is missing? Researchers have long recognized that much can be learned from qualitative data by what is not mentioned. Bogdan and Taylor (1975) suggested being "alert to topics that your subjects either intentionally or unintentionally avoid" (p. 82). Themes that are discovered in this manner need to be carefully scrutinized to ensure that investigators are not finding only what they are looking for or what they are only expecting to find. 8. **Theory-related Material** Researchers can also look for statements that are related to or supports a theory. However, over-focusing on this can also draw the focus away from crucial more obvious themes. That is why, again, this should be done after "obvious" themes were already identified. B. **Manipulative Techniques** 1. **Cutting and Sorting** After the initial pawing and marking of text, cutting and sorting involves identifying quotes or expressions that seem somehow important and then arranging the quotes/ expressions into piles of things that go together (those with common implications). Make sure that the quotes or expressions that were chosen are properly labeled or coded (who said it, in what part of the transcript was it taken etc.) 2. **Word Lists and Key Words in Context (KWIC)** Word lists and the KWIC technique draw on a simple observation: If you want to understand what people are talking about, look closely at the words they use. To generate word lists, researchers first identify all the unique words in a text and then count the number of times each occurs. Computer programs perform this task effortlessly. 3. **Word Co-occurrence** This approach, also known as collocation, comes from linguistics and semantic network analysis and is based on the idea that a word's meaning is related to the concepts to which it is connected. 4. **Metacoding** Metacoding examines the relationship among a priori themes to discover potentially new themes and overlapping themes. The technique requires a fixed set of data units (paragraphs, wholetexts, pictures, etc.) and a fixed set of a priori themes. Fo reach data unit, the investigator asks which themes are present and, possibly, the direction and valence of each theme. The data are recorded in a unit-by-theme matrix. This matrix can then be analyzed statistically. Themes like these are often not readily apparent, even after a careful and exhaustive scrutinizing of the text. Because metacoding involves analyzing fixed units of texts for a set of a priori themes, it works best when applied to short, descriptive texts of one or two paragraphs. **\*\*** Some softwares that can be used for analysis of qualitative data include ATLAS.ti, Nvivo, Provalis Research Text Analytics Software, FreeQDA, QDA Miner Lite and others. **What to consider when choosing appropriate techniques in identifying themes** 1. **Kind of Data** 2. **Expertise** Not all techniques are available to all researchers. One needs to be truly fluent in the language of the text to use techniques that rely on metaphors, linguistic connectors, and indigenous typologies or that requires spotting subtle nuances such as missing data. Researchers who are not fluent in the language should rely on cutting and sorting and on the search for repetitions, transitions, similarities and differences, and etic categories (theory-related material). Word lists and co-occurrences, as well as metacoding, also require less language competence and so are easier to apply. However, co-occurrences and metacoding require skills in manipulating matrices. 3. **Labor and Time** Today, computers have made counting words and co-occurrences of words much easier. Software also has made it easier to analyze larger corpora of texts. Still, some of the scrutiny-based techniques (searching for repetitions, indigenous typologies, metaphors, transitions, and linguistic connectors) are best done by manually by human eye, and this can be quite time consuming. 4. **Number and Kinds of Themes** In theme discovery, more is better. It is not that all themes are equally important. Investigators must eventually decide which themes are most salient and how themes are related to each other. But unless themes are first discovered, none of this additional analysis can take place. 5. **Validity and Reliability** Theme identification does not produce a unique solution. As Dey (1993) noted, "there is no single set of categories \[themes\] waiting to be discovered. There are as many ways of 'seeing' the data as one can invent" (pp.110--11). So, how can we tell if the themes we've identified are valid? The answer is that there is no ultimate demonstration of validity. The validity of a concept depends on the utility of the device that measures it and on the collective judgment of the scientific community that a construct and its measure are valid (Bernard, 2006:60; Denzin, 1970:106). **CORROBORATION** The aim behind corroboration in qualitative research is to enhance validity, reliability, authenticity, replicability, and accuracy of the research. The researcher uses several tools to achieve corroboration and to reduce faulty observations, biased analysis, and inaccurate conclusions. Corroboration is necessary to maintain standards in conducting surveys, data analysis, and interpretation. "The purpose of corroboration is not to confirm whether people's perceptions are accurate or true reflections of a situation but rather to ensure that the research findings accurately reflect people's perceptions, whatever they may be. The purpose of corroboration is to help researchers increase their understanding of the probability that their findings will be seen as credible or worthy of consideration by others." (Stainback & Stainback, 1988). **Approaches to corroboration in qualitative research** 1. **Supporting documents/proofs** The researcher might ask the respondents to provide supporting documents or proofs where necessary. For example, medical records sometimes help in corroborating the information received from the respondents. The use of supporting documents or other proofs in corroborating data gives the reader an idea about the authenticity of research. 2. **Other data sources** Other data sources can be books, government records, and other form of recorded data, or reports that can be used to corroborate the findings of the interview or questionnaire. The researcher often knows about other sources that help him corroborate the data through the interview. The interviewee can help the researcher know about the sources and where to locate them. Corroborating data with other sources makes a research worthy of considerations as it has been verified using different sources. 3. **Consistency check** Consistency check is a very useful tool that researchers use in interviews and questionnaires. The researcher asks two similar questions from the respondents with different wordings and phrasing. The aim is to know whether the answers are consistent or not. The researchers usually use this technique for important questions. They also use these techniques for questions that are sensitive or have personal meanings. The use of consistency check should be done with caution if the respondent knows that there are questions like that they might get offended or they might not show the real self in the answers. 4. **Comparing results to similar studies** There might be similar studies conducted by other researcher if you are not sure about the validity and accuracy of the data and its results you can compare the results with other such studies. The researchers can provide references of the useful and relevant sources in their own research. These references add more weight to the research outcomes. Source: **REFERENCES** **Books** Avilla, R.A.(2016). Practical Research 1. DIWA Senior High School Series. DIWA Learning Systems. pp. 104-105 Melegrito, M.L., Mendoza, D., & Mactal, R. (2016). Applied Research: An Introduction to Qualitative Research Methods and Report Writing. Phoenix Publishing House, Inc. pp. 47-54, 85-96 **Journals** Tanaka C., Tuliao, M.T., Tanaka, E., Yamashita, T., & Matsuo, H. (2018). A qualitative study on the stigma experienced by people with mental health problems and epilepsy in the Philippines. *BMC Psychiatry (2018)* 18:325 Wang, Q. & Su, M. (2020) A preliminary assessment of the impact of COVID-19 on environment -- A case study of China. *Science of the Total Environment 728 (2020)* 138915. **Websites** Achieving Corroboration in Qualitative Research (2019) ResearchArticles.com  Themes and Codes (n.d.) **E-book** Maguire, M., & Delahunt, B.(2017) Doing a Thematic Analysis: A Practical, Step-by-Step Guide for Learning and Teaching Scholars. *All Ireland Journal for Teaching and Learning in Higher Education. Volume* (Autumn 2017) pp. 3351-33512 Ryan, G., & Bernard, H. (2003) Techniques to Identifying themes. *ResearchGate* pp. 85-102