Psychology
chady5
Example1discussion.docxExample 1
Methodological Design: Phenomenology
Research Question: What is the experience of “burnout” among direct support professionals (DSPs)?
Data Analysis Elements: Qualitative analysis is driven by guidance but does not have a strict formula regarding how data is synthesized into results (Patton, 2015). Vast amounts of data, like what would be collected by asking questions of 15 DSPs needs to be appropriately considered and filtered into what is significant to identify patterns (Patton, 2015). This process does not have a way to test its validity, instead the responsibility is on the researcher to fairly and thoroughly consider the data they have collected (Patton, 2015). This is especially important in phenomenology as the purpose is to come to commonalities while maintaining separation from the views of the group (Creswell & Poth, 2018).
Data Analysis for this Study: Once my data is organized, I will use the data analysis spiral method to begin reading and memoing to become familiar with the details of the narratives (Creswell & Poth, 2018). Rapid reading is one way to review notes from a perspective other than the author's and to not dwell on a more intricate step like coding (Creswell & Poth, 2018). After reviewing the answers to the questions regarding burnout, I can develop a coding system to identify themes and categories that I will be compiling across responses (Creswell & Poth, 2018). Beginning with a shortlist of codes (beginning with around 6 and expanding to no more than 20 or 30) makes thematic generation clearer later on (Creswell & Poth, 2018). Moustakas recommends for phenomenological research that these significant statements should be non-overlapping and used to craft descriptions of “what” and “how” (Creswell & Poth, 2018). The codebook and results then lead to interpretation, creation of a table, and in the case of phenomenology the generation of the final composite description of the experience that is being studied (Creswell & Poth, 2018).
Strengths and Limitations: Strong qualitative analyses include the collection of data that is organized and purpose-driven (Patton, 2015). It is important to begin an analysis of patterns while data is being collected to understand how many more cases may need to be collected to test the theme (Patton, 2015). One concern that I could see regarding this method could be researcher bias affecting how data is collected. The researcher would need to work on establishing and maintaining bracketing of their own experience as to not sway the direction the study is moving in with their own outsider perspective (Creswell & Poth, 2018).
References
Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). Thousand Oaks, CA: Sage.
Patton, M. Q. (2015). Qualitative research & evaluation methods (4th ed.). Thousand Oaks, CA: Sage.
Professor feedback.
There are several alternatives with this method, Amanda. You can follow the method described by Giorgi in the attached document or Moustakas (the text is referenced in the syllabus). Another author is van Kaam. Within van Kaam's approach you need to consider all of the data so that it is apparent that you weren't biased in your selection of what was important within the experience (van Kaam, 1959). The next step is to separate the data into meaning units by marking where shifts in meaning occur. Look at each meaning unit to determine if it provides a description of the experience being explored. If it does, decide whether or not this meaning unit can be labeled in a way that does not alter but in fact preserves what is being communicated. If this meaning unit is relevant and can be labeled, it is retained and coded. Once the entire data set is coded, concepts (labels) that overlap or repeat can be explored to identify the expression (label/code) that is the most descriptive of this experience. Several meaning units may be identified with this expression (label/code). Ex. The code ‘feeling inadequate’ could be applied to several segments of text or meaning units. Related expressions, expressions that represent the same aspect of the experience, are then grouped together (clustered) and labeled with an abstract concept (expression framed in psychological terms) that represents that group. Several clusters may fall underneath this expression especially if each represents a portion of that particular aspect of the experience. Adrian L. van Kaam (1959) refers to these as moments within the experience. Several moments may be grouped together that describe one aspect of the experience. After all of the data are analyzed, the researcher has to make sure that the expressions identified as exemplifying this experience are communicated directly or indirectly within each data set and apply to the overall experience expressed by each individual.
Van Kaam, A (1959). Phenomenal Analysis: Exemplified by a Study of the Experience of “Really Feeling Understood”. Journal of Individual Psychology, 13, 66-72.
Example 2
Topic: Experience of adjusting to motherhood as a working BCBA. Research Question and Methodological Design My proposed research topic is on the experience of adjusting to motherhood as a working BCBA (Board Certified Behavior Analyst). This topic is relevant most to a Phenomenological study, as I wish to explore a specific population of interest and their personal experiences. The Phenomenological approach aims to collect data on the unique personal experiences of the target population to develop a broader understanding of the specific phenomena of that group in their adjustment to motherhood (Moustakas, 1994). Per recommendations by Creswell & Poth (2018), data collection would be primarily derived from an open-ended one on one interview and sampled at n = 1 of BCBA’s interviewed to provide an appropriate number of successful participant data collected. Data Analysis for Methodology Data collected will be prepared and then put through a process of phenomenological reduction. This would be done in conjunction with the five stages of analysis (Percy et al., 2015). As per Capella guidelines, an empirical Phenomenological approach would be chosen. Collected interviews would be transcribed to documents and analyzed for criteria that would be categorized into corresponding codes/themes identified. After this, interpretation and visual graphing would be designed to transfer the collected data to an observable and visual representation of the data (Creswell & Poth, 2018). The data will then be divided into meaning units and examined to determine the meaning of the experiences (Giorgi, 1997). Effectiveness and Limitations of Data Analysis Strategy Limitations of an empirical data analysis strategy are that it cannot utilize the ability to experiment with meanings or themes multi-directionally. Also, any discrimination between the textural or structural description of the interviews would not be possible. Finally, we are working with qualitative data, which is subjective in many ways. It could not be tested in a lab and replicated as such. The known effective traits of this data analysis strategy are that the data is categorized and analyzed in a systematic and inductive process. Natasha Bouchillon References: Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). Thousand Oaks, CA: Sage. Giorgi, A. (1997). The theory, practice, and evaluation of the phenomenological method as a qualitative research procedure. Journal of Phenomenological Psychology, 28(2), 235-281. Moustakas, C.E., & SAGE Publications. (1994). Phenomenological research methods. Thousand Oaks Calif: Sage. Percy, W.H., Kostere, K., & Kostere, S. (2015). Qualitative research approaches in psychology. Retrieved from http://assets.capella.edu/campus/doctoral-programs/PsychologyQualitativeResearchApproaches.pdf
Professor feedback
Van Kaam, A (1959). Phenomenal Analysis: Exemplified by a Study of the Experience of “Really Feeling Understood”. Journal of Individual Psychology, 13, 66-72.
