PSYCH 61 Midterm Reviewer PDF

# PSYCH 61 - MIDTERM REVIEWER | For AB PSYCH 4 <3 ## Types and Pointers of Exam 1. **Identification**: Thematic Analysis 2. **Modified Essay:** - **Quantitative**: identify types of data, types of analysis, and statistics to be used for the research questions you will create based on the given...

# PSYCH 61 - MIDTERM REVIEWER | For AB PSYCH 4 <3 ## Types and Pointers of Exam 1. **Identification**: Thematic Analysis 2. **Modified Essay:** - **Quantitative**: identify types of data, types of analysis, and statistics to be used for the research questions you will create based on the given title. - **Qualitative**: organize the given codes to create themes. ## Thematic Analysis | Caulfield, 2023 ### Thematic Analysis A method of analyzing qualitative data. Usually applied to a set of texts, such as interview or transcripts. Data is closely examined by the researcher to identify common themes. A good approach when you're trying to find out something about people's views, opinions, knowledge, experiences, or values from a set of qualitative data. Allows flexibility in interpreting data & approach large data sets more easily by sorting them into broad themes. However; it involves the risk of missing nuances in the data as it is often subjective and relies on the researcher's judgement. ### Themes Topics, ideas, and patterns of meaning that come up repeatedly. #### Inductive Approach An approach to thematic analysis that involves allowing the data to determine your themes. "Am I planning to develop my own framework based on what I find?" #### Deductive Approach An approach to thematic analysis that involves coming to the data with some preconceived themes you expect to find reflected there, based on theory or existing knowledge. "Does my theoretical framework give me a strong idea of what kind of themes I expect to find in the data?" #### Semantic Approach An approach to thematic analysis that involves analyzing the explicit content of the data. Taken at face value; not looking for anything beyond what a participant has said or what has been written. "Am I interested in people's stated opinions?" #### Latent Approach An approach to thematic analysis that involves reading into the subtext and assumptions underlying the data. Underlying meanings; involves an element of interpretation where you theorize meanings and don't just take data at face value. "Am I interested in what their statements reveal about their assumptions and social context?" ## Braun and Clarke's Six-Step Thematic Approach | Virginia Braun & Victoria Clarke ### Step 1: Familiarization Get to know your data; getting a thorough overview of all the data collected before analyzing individual items. - Transcribing audio, reading through the text, taking initial notes, and generally looking through the data to get familiar with it. ### Step 2: Coding Coding is highlighting sections of our text, usually phrases or sentences, and coming up shorthand labels or "codes" to describe their content. * Going through the transcript of every interview and highlighting everything that is relevant or potentially interesting. * Collate the data into groups identified by code. **Example**: | Interview Extract | Codes | |---|---| | Personally, I'm not sure. I think the climate is changing, sure, but I don't know why or how. People say you should trust the experts, but who's to say they don't have their own reasons for pushing this narrative? I'm not saying they're wrong, I'm just saying there's reasons not to 100% trust them. The facts keep changing - it used to be called global warming. | * Uncertainty * Acknowledgement of climate change * Distrust of experts * Changing terminology | ### Step 3: Generating Themes Looking over the codes created, identify patterns among them, and come up with themes. * **Themes are broader than codes**: combining several codes into a single theme. * Some codes that are too vague or not relevant enough can be discarded. * Other codes may become themes. **Example**: - **Theme: Uncertainty** * Codes: Uncertainty, Leave it to the experts, Alternative explanations. - **Theme: Distrust of experts** * Codes: Changing terminology, Distrust of scientists, Resentment toward experts, Fear of government control. - **Theme: Misinformation** * Codes: Incorrect facts, Misunderstanding of science, Biased media sources. ### Step 4: Reviewing Themes Ensuring that the themes are useful and accurate representations of the data. * If we encounter problems with our themes, we might split them up, combine them, discard them or create new ones: whatever makes them more useful and accurate. **Example**: For example, we might decide upon looking through the data that "changing terminology" fits better under the "uncertainty" theme than under "distrust of experts," since the data labelled with this code involves confusion, not necessarily distrust. ### Step 5: Defining and Naming Themes Defining themes involves formulating exactly what we mean by each theme and figuring out how it helps us understand the data. Naming themes involves coming up with a succinct and easily understandable name for each theme. **Example**: For example, we might look at "distrust of experts" and determine exactly who we mean by "experts" in this theme. We might decide that a better name for the theme is "distrust of authority" or "conspiracy thinking". ### Step 6: Writing Up Writing up the analysis of the data. * Introduction to establish the research question, aims, and approach (Chapter 1). * Methodology section describing how the data was collected and how the thematic analysis was conducted (Chapter 2). * Results addresses each theme in turn; describe how often the themes come up and what they mean, including examples from the data as evidence (Chap 3). * Conclusion explains the main takeaways & how the analysis has answered our research question. **Example**: In our example, we might argue that conspiracy thinking about climate change is widespread among older conservative voters, point out the uncertainty with which many voters view the issue, and discuss the role of misinformation in respondents' perceptions. ## Quantitative analysis | Type of Data | Descriptive | Correlational | Comparative | Predictive/Associative | |---|---|---|---|---| | Nominal | Mode, Frequency Distribution, Percentage Distribution | Cramer's V, Phi Coefficient | Chi-square, Fisher Exact, McNemar Change| Nominal DV: Logistic Regression 1 IV: Simple >2: Multiple | | Ordinal | Mode, Frequency Distribution, Percentage Distribution, same w/ nominal-descriptive | Spearman rho, Kendall's Tau | Mann-Whitney U, Wilcoxon Signed Rank | Ordinal DV: Ordinal Regression 1 IV: Simple >2: Multiple | | Scale | Mean, Standard Deviation, Median | Pearson r | 2 means: Independent t-test, Dependent t-test 3 or more means: ANOVA | Scale DV: Linear Regression 1 IV: Simple >2: Multiple | ### Statement of the Problem (the usual schtick): 1. **Demographic Profile:** * "What is the profile of the respondents with regard to their ..." * "What is the demographic profile of the respondents in terms of: ..." 2. **SOP 2:** * Determining the levels * Determining the factors * Idk unsa pa mga usual 3. **Last SOP:** * Either significant relationship or significant difference * "Is there a significant chuchu between the ..." ## Qualitative Analysis **Theming Format:** | Code | Theme | Reject | Revise | Accept | |---|---|---|---|---| | Input the codes in this column | Merge codes to create themes | | | | **Example:** | Code | Theme | |---|---| | Feeling under pressure, Overwhelmed with obligations, Transition to work, Blending with colleagues, Workplace interactions | Dealing with Stress, Adjusting to Work Environment | This document provides a good overview of thematic analysis and is easy to understand with its clear headings and examples. The table format for the different types of data and analysis required for each one is also useful. However, it would be beneficial to revise the example in the "Writing Up" section to be more detailed and specific to a research topic.