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Comm. 88 Final Exam Review Guide - Mullin Spring 2016


Comm. 88 Final Exam Review Guide - Mullin Spring 2016 Comm. 88

Marketplace > University of California Santa Barbara > Communication Studies > Comm. 88 > Comm 88 Final Exam Review Guide Mullin Spring 2016
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Extensive study guide with in-depth lecture notes and examples covering all likely material to be on the exam. Also includes comprehensive notes from relevant class textbook and GauchoSpace reading...
Communication Research Methods
Dolly Mullin
Study Guide
UCSB, communication, Comm88, communication88, Mullin, research methods
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This 26 page Study Guide was uploaded by on Sunday June 5, 2016. The Study Guide belongs to Comm. 88 at University of California Santa Barbara taught by Dolly Mullin in Spring 2016. Since its upload, it has received 168 views. For similar materials see Communication Research Methods in Communication Studies at University of California Santa Barbara.


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Date Created: 06/05/16
Friday, June 3, 2016 Comm. 88 Final Exam Review Guide Survey/Correlational Research - Purpose • Identify/describe attitudes or behaviors (in a given population) - to see who is in favor of what - Can examine your population at one point in time or track the population over time • Cross-sectional Surveys - One sample measured at one point in time • Longitudinal Surveys - Variables are measured at more than one point in time; testing whether attitudes change over time as subjects age - Panel Study: same people are used each time • Can be difficult to track down same subjects over time - Trend Survey: different people (random samples) from the same population over time • Example) Poll “Americans” every 5 years to study their church- attendance; track “likely voters” over the course of an election campaign - Cohort Sample: different sample of people every time, but of the same “cohort” • Cohort = people who have something in common at some point in time, entire sample is aging together - Example) Surveying the UCSB Class of 2015 every 5 years surveying their employment since graduation; surveying people born during The Depression; people who lived in NYC during 9/11 - Types of Surveys • Self-Administered Questionnaires - Mail surveys; online or emailed questionnaires; handouts; diaries 1 Friday, June 3, 2016 • Relatively easy to administer and inexpensive • No interviewer influence • Increased privacy/anonymity • BUT: - Must be self-explanatory; cannot be too complicated • Example) Media Use Diary: confusing chart that is difficult to understand/ follow - Very low response rate! ~20% of people respond to self-administered questionnaires • Ways to increase response rate: - Have inducements/incentives • A chance to win money • Make it easy to complete • Include a persuasive cover letter and/or do advance mailing • Send follow-up mailings to the people who don’t send in/complete the surveys; extend deadline • Interview Surveys - Personal/face-to-face • More flexible (can probe for more depth in answers and can clarify things) Higher response rate • • BUT: - More potential for interviewer influence - Higher costs - Telephone (includes robo-calls) • Quickest results • Reduced costs, more efficiency • More privacy than face-to-face interviews 2 Friday, June 3, 2016 • More detail possible, better response rate compared to self-administered • What about call screening and cell phones? - People choose not to answer • Experience Sampling - Send texts to participants, the link via phone to survey line • Also can use survey apps Participants answer questions about their experiences/feelings “in the moment" • - Can improve accuracy of self-reports since it is about the current moment - Allows for longitudinal/panel data - Question wording and order is important - Survey Data Analysis • Analyzing depends on hypothesis/research question and how variables are measured • Is hypothesis/research question a difference statement? - Testing for a difference between two things (categories) • Example) Women will disclose more than men? - Comparing categories of gender • Or is it a continuous statement? - Looking at a variable on a continuum - Example) The more people talk about their problems, the worse their problems seem • Continuum of their problems talking and perception • Testing for Differences - IV is categorical (nominal/discrete) • Example) Comparing men to female, Republicans to Democrats, heavy vs. light talkers, like/dislike tinder - If DV is categorical: 3 Friday, June 3, 2016 • All you can do is compare percentages in different categories - If DV is continuous (interval/ratio): • DV uses likert or semantic differential • Compare mean DV scores for different IV categories • Comparing Means - Ex. RQ: Does political ideology (IV) predict support for marijuana legalization? IV: ideology • - Categorical/nominal variable - “I consider myself: [ ] Liberal [ ] Moderate [ ] Conservative • Or if IV originally measured as continuous variable, then collapsed into categorical variable - “I consider myself: very liberal 1 2 3 4 5 very conservative - Divide participants into categories based on scores • Above median = conservative • Below median = liberal • DV: Support for legalization - “Recreational use of marijuana should be legal” • strongly disagree 1 2 3 4 5 6 7 strongly agree - Compute a mean score on the DV for participants in each IV category • Ex) Conservatives: M = 3.3 ; Liberals: M = 6.1 - Liberals are (significantly) more in favor of legalization • Testing Continuous Relationships - Both IV and DV are continuous (interval/ratio) - Testing for correlation • Statistical value that shows relationship between 2 or more continuous variables 4 Friday, June 3, 2016 - Compute an “r” value (Pearson r) • R is for correlation • Tells you type of relationship (positive vs. negative) • Magnitude (strength) of relationship - Type of relationship • Positive r: as X increases, Y increases Negative r: as X increases, Y decreases • - Magnitude of relationship • R gets from 0-1 - -1.0 <— 0 —> 1 • Further from 0 = stronger relationships - What can you conclude from survey/correlational data? • Variables ARE related/associated • But CAN NOT see causal relationships - To establish causality, variables must be related (X correlated with Y) - Because we didn’t establish a time order • IV must happen before the DV - Outside factors could be affecting DV • Must rule out other explanations/causes - So, survey research has 2 Causality Problems: • Causal Direction Problem (time order) - Does X cause Y or does Y cause X? • Third Variable Problem (other possible explanations) - Does some “3rd” variable explain the X/Y relationship? • Solving Causality Problems - To solve Third Variable Problem: 5 Friday, June 3, 2016 • “Partial correlation” - Measure potential 3rd variables - Statistically “partial out” (control for) effects of those 3rd variables - Then see if X/Y relationship still holds • Partial Correlation (Ven Diagram) - GPA TIME STUDYING INTEREST IN SCHOOL (3rd variable) • If X/Y relationship still holds, can rule out the 3rd variable as the cause • What happens when Interest in School is highly correlational to GPA and Time Studying? (Lowest circle moves higher up, overlaps more with other two variables) - If X/Y relationship completely disappears (or is reduced substantially), then the 4rd variable explanation matters - To solve Causal Direction Problem: • Need a longitudinal study • “Cross-lagged panel design” (same people are studied over time) - Time 1: Measure X & Y variables - Time 2: Measure X & Y variables again later for the same people - Compute r’s for X & Y, but across the times measured • Example recent panel study: IM and adolescents’ friendships - IV: IM use - DV: friendship quality • Correlation between the two: +r 6 Friday, June 3, 2016 - IM use IM use 
 Time 1 Time 2 - Quality of friendships Quality of friendships Time 1 Time 2 (6 months later) —> Correlate IM use from Time 1 with Quality of friendships from Time 2 (X———> Y, Sig + r) —> Correlation between Quality of friendships from Time 1 with IM use from Time 2 did not have correlation (X —/—> Y, r = ø) - SO, measure both variables for same people at different times; then see which “cross” relationship holds - Mediating variable: intimate self-disclosure (in IM) - Reading: Chapter 10 Notes • Survey Applications; can measure attitudes & retrospective behaviors (past behaviors) - Polls • One of the most common and readily available uses of surveys • Used frequently for political purposes; political polls - Provide a detailed account of who is leading whom in the run for office; often conducted over the phone • Key to polls: research method strategies used, quality of sampling and sample size, response rate, and types of questions asked - Evaluation Research • Research that is designed to assess the effectiveness of programs or products during development or after their completion - Allows practitioners and managers to develop more effective communication programs • 1) Summative Evaluation Research 7 Friday, June 3, 2016 - Takes place after the program has run its course or the campaign is ending - Benefit: Can identify ways the future program/campaign can be refined and improved • 2) Formative Evaluation Research - Evaluation research that helps the campaign or program manager develop a campaign or program, or evaluate it while it is ongoing - Benefit: Can identify ways the current program/campaign can be refined and improved • 3) Needs Analysis - Mechanism for identifying problems experienced by a group of people by comparing what exists with what study participants want • Example: Organizational employees might fill out a needs assessment survey to help management develop a new training program comparing what the current programs offer vs. what employees want/need in the training program • 4) Organizational Feedback - Organizational members are asked to report on practices within the organization, and are then used for organizational improvement (in communication practices, information quality, preferred methods of communication, etc.) - Market Research • Research that is designed to study consumer behavior - If you can determine what people consume, how often, and why, then you can predict future consumption • Evaluates how satisfied consumers are with products and services, as well as explores persuasive strategies for advertising, product pricing, or packaging • Question Strategies - Note: 8 Friday, June 3, 2016 • Recent and important events are remembered more easily (asking to report on the previous day or week’s behavior will produce more accurate results than making about the distant past • People can recall past serious issues (September 11, 2001), but not past minor issues • Certain unpleasant and ego-threatening incidents tend to be reported inaccurately (college students underreport unsafe sexual behavior) - Open-ended or Closed-ended questions • Closed-ended: suited for eliminating interviewing interpretation, eliminating coding and editing interpretations, making data recording easier as the interview is being conducted, and ensures consistency in interviewing - “Yes/No” question, “In which of the following age categories do you fit” • Open-ended: suited for measuring a participant’s level of knowledge, detailed explanation of an opinion, or a playback of something - “Why do you say that”, “What do you remember about…” • Require some amount of probing and clarifying - “What else do you remember?”, “What do you mean by that?” - 1) Questions should be clear • Use simple language, make wise word choices based on your audience, be aware that some words may have multiple meanings (be sure that the interpretation you are thinking of is clear to participants) - 2) Questions must be about only ONE issue • Avoid double-barreled questions - Example) “Do you like the education and social life at UCSB?” • Asks about two different factors/issues - Can not be certain what the participant intended when they answered the question - Affects the reliability and validity of the survey - 3) Questions should avoid biased wording 9 Friday, June 3, 2016 • Avoid “Wouldn’t you agree…” or “Isn’t is true…” • Also, avoid language that is emotionally charged; this can bias answers - Language used in the abortion debate; “pro-choice”, “anti-choice” - 4) Questions should avoid making assumptions • Ask participants questions that are relevant to them • Ask questions only when participants are informed about the topic - 5) Questions should avoid offending participants • Rather than asking, “How old are you?”, as “In which of the following age categories do you fit?” • Question Order/Format: 3 Commonly Used Formats - 1) Tunnel Format • Questions tend to be similar in breadth and depth throughout the questionnaire, organized by similarity and vary little in terms of depth - 2) Funnel Format • Questions begin with broad, often open-ended questions followed by narrow, more closed-ended questions. Moves from general —> specific - 3) Inverted Funnel Format • Begins with closed-ended, narrow questions moving throughout the questionnaire to the most open-ended and broad questions - Choosing the right format & format suggestions • Choose whichever structure is best suited to the research question and/or hypothesis - For a study on sexual risk taking in college dating couples, the inverted funnel design is best because the topic of sex can be taboo or very personal • Start with low-risk, closed- ended questions before moving on to more personal questions • Arrange questions about the same topic together with transition statement between topic areas 10 Friday, June 3, 2016 • Response Set: Tendency people have to start answering questions without much thought - Solution: Include additional items/questions to minimize the Response Set tendency Asking “I am very tense and nervous in conversations” AND “I am very • calm and relaxed in conversations” • Same idea as reversing the polarity of some items on scales Experimental Research Purpose: • - To test hypotheses of cause and effect (find ca • Goal is to establish internal validity (the ability to determine that X causes Y and there are no other explanations) Key Elements to a True Experiment • - Manipulation of IV(s)… • Divide into different “conditions” - Ex. IV) New painkiller drug • Half of PS (people in the study) get the drug, the other half don’t… while controlling all other variables - PS in each condition are treated the same way, etc. • Examine the effects on the Dependent Variable (DV) - Compare measures (mean scores) for PS in each condition and see if differences exist • Ex. DV) amount of perceived pain (e.g., 0-10 scale) - Random Assignment of participants (Ps) to conditions • Everyone must have an equal chance of ending up in either condition • Why is this important? - Makes group equal before manipulation 11 Friday, June 3, 2016 - Types of Experiments • Design notation: - X : IV manipulation (treatment/induction) - O : Observation (measure for DV) - R : Random assignment • True Experiments - Random assignment, one group gets treatment and the other doesn’t, compare average scores - Posttest only control group design • R X O1 (group 1) • R O2 (group 2) - Variations: more groups, several different treatments - Example: IV — different types of ad appeals • R X1 (personal cancer story) O1 (group 1) • R X2 (cancer statistics) O2 (group 2) • R X3 (attack tobacco industry) O3 (group 3) • Example: - R X (anti-smoking ad) O1 (beliefs about smoking) - R (no anti-smoking ad) O2 (beliefs about smoking) • This is the IV manipulation • This is the DV measure (compare O1 and O2). If you get difference between group means (O1 vs O2), the IV caused it! - Pretest-posttest control group design • R O1 X O2 (group 1) R O3 X O4 (group 2) • - Possible problem: differences on O2 and O4 might be result of interaction of manipulation with pretest 12 Friday, June 3, 2016 • Example: • R O1 (beliefs about smoking) X (anti-smoking ad) O2 (beliefs about smoking) • R O3 (beliefs about smoking) (no anti-smoking ad) O4 (beliefs about smoking) - This is the DV measure (before) - This is the IV manipulation - This is the DV measure (after) • Again, if you get a difference between group means (O2 vs O4), the IV caused it! - Solomon Four-Group Design: Used when you need to know whether pretest had effect • R O1 X O2 (group 1) • R O3 O4 (group 2) • R X O5 (group 3) • R O6 (group 4) - Comparison between O2 and O5 - see if pre-test had effect - Comparison between O4 and O6 - see if - Pretesting: Should you or shouldn’t you? Useful; to “check” on RA (random assignment), to get info on change • But; not necessary to establish causality • - Bad idea if treatment/pretest interaction is likely - Example Study: Music & Learning • Research Question: Does listening to music (while studying) hinder of enhance learning? • IV = music, DV = learning - Possible experiment: 13 Friday, June 3, 2016 • R X (music) O1 (test score) (group 1) M (mean score) = 67 • R (no music) O2 (test score) (group 2) M = 79 - Type of design? What can you conclude? - Now, what if we want to test for effects of ANOTHER IV?… • Quasi Experiments - When you can’t do random assignment, but try to make up for it - Nonequivalent Control Group Design • O1 X O2 (group 1) O3 O4 (group 2) - Use pretest scores to match groups before manipulation • If your testing the effect of something on first grade reading level, use a pretest to compare their scores • You find quasi equivalent groups - Time Series designs • Track many observations over time, before and after manipulation - O1 O2 O3 O4 X O5 O6 O7 O8 (group 1) - Single-group interrupted series design - Multiple time series design - O1 O2 O3 O4 X O5 O6 O7 O8 (group 1) - O9 O10 O11 O12 O13 O14 O15 O16 (group 2) • Give comparison group treatment and keep measuring - Factorial Designs • Purpose: To examine the effects of two or more IVs simultaneously - Main Effect: The effect of one independent variable by itself - Interaction Effect: The effect of two independent variables interacting with each other 14 Friday, June 3, 2016 - Threats to Internal Validity: - Not a true experiment - Not random assignment - Pre-experiments: some manipulation of IV, no RA, many threats to internal validity • One-shot case study - X O1(Group 1) • Observe one group with no control group and make assumptions - Ex: Effects of smiling on others - X(Smile at people) O1(# of smiles received) - Possible alternative explanations for data • One-group pre-test post-test design - O1(# of smiles) X(smile at people) O2 (# of smiles again) - Possible alternative explanations for data - O1(# of accidents) X(new stop sign) O2(# of accidents after) • Static Group Comparison - Post-test only with nonequivalent rout - X O1(group 1) - smile at some people O2(group 2) - no smiling • Alternative explanations - Selection Bias: some groups will just react differently to some stimuli - History Effect: outside influences on your study (i.e. ice cream truck shows up so kids are smiling more) - Reactivity Effects: people’s reaction to being in a study —> Hawthorne Effect: productivity rates with different lighting, but participants actually had higher productivity just because the 15 Friday, June 3, 2016 participants knew they were being watched. Productivity decreased when participants realized they weren’t being observed - Placebo Affect: people respond based on what they think the outcome is - Demand Characteristics - To remove these threats, conduct a true experiment - Threats related to pretesting (or measures over time) • Testing affect: people know they’re being tested so they change behavior for second test • Maturation: people mature/change/fluctuate opinions and ideas between pretest and posttest - Can be a seasonal fluctuation; people are happier during holidays and smile more • Statistical Regression - People at extreme hi/low end of spectrum will be less extreme the second time you test them • You get a low score on SAT and then improve • Instrumentation - If you change your scale from time 1 to time 2 • Mortality - People dropping out of experiment from time 1 to time 2 Experimenter effect/bias • - Experimenters behavior of attributes rather than treatment of IV, influences DV • How to control this? - Perform a true experiment - Use a double-blind: experimenter doesn’t know who groups are separated - Use a script for what you tell your participants 16 Friday, June 3, 2016 • Within-Subjects Designs: - Every subject is in every condition, rather than being assigned to different conditions like “between-subjects” designs - Doesn’t work for most kind of research - By exposing people to multiple conditions they might understand what the experiment is about - Ex: pilot’s reaction time to warning lights Post-test design: • R X(red) O1(reaction time) group 1 R X(green) O2(reaction time) group 2 R X(yellow) O2(reaction time) group 3 - Problems: small number of people per group, lots of random error - In Within-Subjects design each pilot gets a chance at each condition (color of light) 1st red light —> reaction • • 2nd green light —> reaction • 3rd yellow light —> reaction - Now there’s 18 subjects in each condition • Greater power • Less random error - Problem: carry-over/order effects • Practice effects: with each trial subjects get better • Fatigue effect: subjects reaction time slows down • First treatment/contrast effect: first treatment is contrasted with what you do next - Humorous ad shown before serious one and subjects now compare them - Solution: Counterbalance orders 17 Friday, June 3, 2016 • Let subjects do each condition in different order (randomly) - Lab vs. Field Experiments • Laboratory Experiments: - Bring subjects into highly controlled setting • Field Experiments: - Different than field research - Manipulate IVs in the real world • Ex: littering studies; are people more likely to litter if there’s a confederate that litters? • More natural setting/behavior leads to higher external validity • Less reactivity (or none) • Hard to maintain experimental control - Reading: Text pp. 188-190 Notes • Threats to Internal Validity - History • Something totally unrelated to the study happened at a particular time and may have affected the responses - Ex) Surveying men and women on DUI’s at separate times (men on a Monday, women on a Thursday). Between Monday and Thursday, a high- profile celebrity was seriously injured in an alcohol-related car crash, affecting the responses between men and women - Maturation • Subjects can change or mature over time, affecting their responses to the measures a researcher is interested in - Ex) Texting a new curriculum at UCSB regarding Public Speaking- college freshmen communication apprehension is tested before and after the class, but the entire course of the school year and maturing as a freshman (meeting strangers, dealing with roommates, etc.) will have also affected their communication apprehension 18 Friday, June 3, 2016 - Testing • If someone is more familiar or comfortable with a series of questions or items, they may respond differently to them; completing an instrument multiple times may affect the results - Ex) Creating alternate forms of the same exam- while the scores may have increased from the first time the exam was taken to the second time, but the subjects may have just become better versed in the material because they have taken a similar test before - Instrumentation Differences that are observed at two different time points in which different • instruments are used to assess the same construct - Ex) Exam situation again- the second created exam may just be easier than the first; the use of different instruments means that the responses might differ simply as a function of measurement - Hawthorne Effect • The tendency for participants to respond differently when they know they are being observed - Research participants change their behavior/responses because someone is watching • Evaluator Apprehension - When research participants become nervous or unsettled by the fact that they are in uncomfortable surrounding, limiting their ability to concentrate or causing them to respond differently • Attrition - In longitudinal research, you may lose participants during the course of the study simply because participants move away, decide to drop out of the study, or get bored - Reading: Chapter 12 Notes (Experiments) • Overlaps with lecture; focus on lecture notes & Experimental Designs Sheet on GauchoSpace 19 Friday, June 3, 2016 Content Analysis (Quantitative) - Content Analysis • Used to: - Describe how much/what kind of certain messages there are (e.g. sex on TV, types of tweets) - Assess “image” of particular groups in media (e.g. stereotypes of race, gender, age, political party, etc.) - Compare media content to “real world” - Examine message change over time - Provide background for research on media effects • Content Analysis is also a method for coding/analyzing open-ended data in surveys and experiments - Important Procedures: • Sampling - Define population of interest • Ex) Primetime TV shows, Facebook discussions - Identify “unit of analysis” for coding (textbook: “recording” and “context” units) • Ex) For TV shows: Code each episode? Scene? Character? • Ex) For Facebook: Code each entry? Thread? Word? - Select representative sample (ideally) • Closer sampling most common for CA • Coding: Transforming content into numerical categories - Manifest content (visible, surface content) • E.g., Number of guns in a TV program - Don’t have to think about it, you just see it - Latent content (underlying meanings) • Have to think about it 20 Friday, June 3, 2016 • Establish reliability! - Limitations: • Purely descriptive - Cannot explain why the content is that why - Cannot conclude anything about effects of the message • Very reductionistic - Reduces content to “code-able” concepts only - Reading: Chapter 11 Notes • Interaction Analysis: Uses techniques similar to those of tradition content analyses, but looks instead at recordings of individuals or groups and how they may behave and exchange information - Record the behaviors or interactions of participants and analyze the content - Often used in conjunction with experimental research • While experimental procedures examining interpersonal interactions can produce data reflecting feelings/attitudes/opinions of participants, having an unobtrusive measure of the nature of interaction may be very valuable to a researcher Qualitative Research - Qualitative Research Methods • Message Analysis (Contrast with Content Analysis) - Subjectively analyze Comm. messages - Rhetorical Criticism - looks at Greek traditions of rhetoric • Critique of from, language, imagery, delivery of speeches/pop culture • Ex) Use of metaphors in a President’s speech • Ex) Themes in student’s drinking stories on Facebook • Goal: greater understanding/appreciation (similar to literary criticism) - Critical Theory: cultural studies or ideological criticism 21 Friday, June 3, 2016 • Craft arguments about cultural implications of media (especially gender, race, class, etc.) • Ex) Marxist or feminist analysis of ads • Goal: social/political awareness and change • Qualitative Methods for Studying People - Field research on GS reading • Ethnography or interpretive research - Contrast with surveys and experiments - Goal: develop rich understanding of people’s subjective experience • Important Features (Field Research) - Natural Setting • For observation/interviewing - Non-representative sampling - Researcher subjectivity and reflexivity • Researcher accepts and comments on own influences on participants - Inductive theory-building • Often called grounded theory - Participant Observation • Subjects aware (overt) or not (covert) of being studied • Researcher participates to varying degrees in events/groups under study • Issues - Typically purposive types of sampling (individual case studies common) - Detailed field notes and records - Finished when you achieve “saturation” • New data doesn’t add anything to your study - Qualitative Interviewing 22 Friday, June 3, 2016 • Unstructured or semi-structured - Open-ended questions, free to change - Depth is key • Types of interviews - In-depth interviews - Ethnographic conversations Naturally occurring things • - Focus Groups • Groups discuss an issue led by a mediator • Open-ended questions - Leader shouldn’t facilitate or control • Popular technique in marketing and political research - GauchoSpace Reading Notes: • Process of Conducting Field Research - Selecting a setting or group, gaining access, establishing roles and relationships, deciding what to observe/whom to interview, gathering and analyzing data, leaving the field, and writing the report • This process is nonlinear; researchers may move back and forth between steps - Guiding Questions: Relatively brand research questions that guide the initial ages of qualitative research • Broader than research questions posed in surveys and experiments - Gatekeeper: Relevant authority whose permission is needed to gain access to a setting or group - Key Informant: A person from whom field researchers acquire information who is selected on the basis of knowledge, expertise, or status within the group - Field Jottings: Brief quotes, phrases, and key words that are recorded by field researchers while in the field 23 Friday, June 3, 2016 • Should be made as inconspicuously as possible (may require researcher to wait until participants are out of sight) - Field Notes: Detailed written accounts of field observations, which may also include a researcher’s reflections and preliminary analyses • Detailed written accounts of field observations written at the end of each day or as soon after the observations as possible • Intended to be descriptive of what the researcher observed; may also include what the researcher thought/felt at the time - Analytic Memo: An adjunct to field notes in field research that consists of recorded analyses that come to mind in going over notes and observations • Intended to help the researcher identify emergent empirical patterns and concepts in the data • Process of Conducting Interviews - Select and recruit interviewees, develop interview guide, gather data, and analyze data. • Process may be nonlinear, with the researcher moving back and forth between steps - Interview Guide: A list of topics and specific questions to be asked in a qualitative interview - Grand Tour Question: A broad opening question in in-depth interviews that asks for a general description of the people, processes, or events being studied (also may be called Tour Question) Ethics - Research Ethics • Treatment of subjects - Institutional Review Board • Must submit research proposal for approval from University’s IRB - At UCSB: Human Subjects Committee • Must complete Human Subjects Training 24 Friday, June 3, 2016 - Guidelines for Using Human Subjects • Participation must be voluntary • Must obtain informed consent - Explain to participants: • Purpose & procedures • Possible risks/discomforts Ability to withdraw without penalty • • Should protect subjects from harm - Shouldn’t diminish self-worth or cause stress, anxiety, or embarrassment • 2 ways to keep privacy: - Anonymity - Confidentiality • Should avoid deception - Both types • Outright deception - deliberately gives false information • Concealment - withholding key information - Deception must be justified by compelling scientific concerns - Subjects must be adequately debriefed • Treatment of Data (Ethics) - Don’t “fudge” data - Keep all data • Allow other scientists access • Reporting findings - Support proper peer review - Avoid conflicts of interest • Funding from sources that expect certain findings 25 Friday, June 3, 2016 • Should we distrust science? - Just watch for bad signs: • Scientists/science too closely mixing with a political agenda • Demonizing people with conflicting points of view - Remember proper research methods - Reading: Chapter 5 Notes Vulnerable Populations • - Persons with diminished autonomy; specifically children, people with cognitive impairments, older adults, people with severe health problems, employees, and students • Employees and students are susceptible to coercion by employers or professors to participate - Assent • Permission obtained from vulnerable individuals allowing themselves to be included as participants in research studies • Nonmaleficience - No harm should be done to participants • Beneficence - The outcome of research should be positive and beneficial • Justice - All classifications of people (race, ethnicity, gender, age, etc.) should be equally subjected to the risks and benefits of research, and people should be included or excluded only for reasons that have to do with the research question or hypothesis 26


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