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Psychology 242 1 Introduction to Research Exam overview, Spring, 2011 Go over the summary notes here These are general guides – get into the actual slide sets to understand them During lectures I have noted when you can expect a term or concept to be on the exam. Go back to your lecture notes, identify when I said something was “exam bait”, and make sure you understand those terms Give yourself the lectures!! Go through the slides and study the areas where you cannot easily describe the material to yourself Practice writing key terms Major focus: 2 statistics sections, within-subjects, and complex experiments. Psychology 242, Dr. McKirnan Week 12-13, quasi-experimental designs. Home Back Next Page Psychology 242 2 Introduction to Research Introduction to science, 1 How do we know something? What does science do? The core features of a research study. Overall Research approaches. Psychology 242, Dr. McKirnan Weeks 1 & 2; Introduction to science. Home Back Next Page Psychology 242 Introduction Four basic sources of knowledge or information: 3 to Research Authority: “I believe what they tell me to” Credible / powerful people Important social institutions Simple tradition Intuition: “I believe my Gut feelings” Emotionality or a “hunch” “Emotional IQ” Empiricism: “I believe what I can see” Simple sensation or perception Direct observation; data Rationalism: “I believe what makes sense.” Logical coherence Articulation with other ideas Psychology 242, Dr. McKirnan Weeks 1 & 2; Introduction to science. Home Back Next Page Psychology 242 Introduction 4 to Research Authority: Intuition: Empiricism: Most central to Science Rationalism: Psychology 242, Dr. McKirnan Week 12-13, quasi-experimental designs. Home Back Next Page Psychology 242 5 Introduction to Research Basic features of a research study Basic features of research; be able to define these. Theory Hypothetical construct Hypothesis Replication Operational definition Internal & external validity Confound Independent v. Dependent variables Which is the “cause” & which is the “effect”? Which is measured & which is manipulated? Measurement v. experimental studies Psychology 242, Dr. McKirnan Weeks 1 & 2; Introduction to science. Home Back Next Page Psychology 242 6 Introduction to Research Core features of a research study: Theory Hypothetical constructs In important relationship More specific variables Hypothesis Falsifiable prediction Operational definition Methods Internal & external validity Data & Numerical representation Normal distribution Analysis Probability Descriptive: Empirical question or exploration Results Hypothesis: Statistical significance Discussion Meaning of these results for the theory Limitations of methods: sample, setting, variables Psychology 242, Dr. McKirnan Weeks 1 & 2; Introduction to science. Home Back Next Page Psychology 242 7 Introduction to Research Section 1 study guide Core elements of the research flow Each component of the research flow corresponds to a later component… Psychology 242, Dr. McKirnan Weeks 1 & 2; Introduction to science. Home Back Next Page Psychology 242 8 Introduction to Research Overall research strategies: Drug use Observation or Measurement Experiments Simple Description Correlational Quasi- “True” Studies experiments experiments Qualitative Quantitative Research Research Research Research Research Question: Question: Question: Question: Question: How does drug Who tends to use What social or ψ What brain Does one form of use actually drugs, how often, variables are drug treatment centers control occur? etc.? associated with “drug craving”? work better than (epidemiology of drug use? another? Methods: Methods: drug use). Methods: Experimental Methods: Methods: design: Direct observation hypothesis- Experimental-like Operationalize of “shooting Surveys, face- oriented surveys design comparing galleries” or to-face drug “craving” or interviews two treatment corner drug interviews, in rats (DV), (potentially with groups. markets, in-depth archival data Stimulate targeted samples: interviews with (e.g., drug arrests, people in rehab., Groups are non- specific brain drug users… ER visits..) etc.). equivalent (not areas (IV) to blind, not randomly map brain Block by Test ψ variables demographic assigned, self- structure onto (motivation, variables (age, emotions, selected…). craving / drug- ethnicity…) seeking. attitudes…) Psychology 242, Dr. McKirnan Weeks 1 & 2; Introduction to science. Home Back Next Page Psychology 242 9 Introduction to Research Overall Research strategies: Validity Observation or Measurement Experiments Simple Description Correlational Quasi- “True” Qualitative Quantitative Studies experiments experiments Explore the actual Describe a Relate measured Test hypotheses Test specific process of a behavioral or variables to each in naturally hypotheses via behavior. social trend. other to test occurring events controlled “lab” hypotheses. or field studies. conditions. External validity Internal validity Less control: More control: Observe / test phenomenon Isolate (or create) the under natural conditions. phenomenon in a controlled environment More accurate portrayal of how it works in nature Addresses specific questions or hypotheses Less able to interpret cause & effect More ability to interpret cause & effect Psychology 242, Dr. McKirnan Weeks 1 & 2; Introduction to science. Home Back Next Page 10 Research ethics Psychology 242 Introduction to Research The Common Rule: What can researchers “do” to get data? The Belmont Report and the Informed Consent document Psychology 242, Dr. McKirnan Research Ethics Home Back Next Page Psychology 242 11 The “Common Rule” criteria for Human Introduction to Research Subjects Protection Minimization of risks Reasonableness of risks (cost – benefit analysis) Equitable subject selection Informed consent Documentation of consent Data monitoring for safety Provisions to protect vulnerable participants & maintain confidentiality Psychology 242, Dr. McKirnan Weeks 1 & 2; Introduction to science. Home Back Next Page 12 Belmont Report Psychology 242 Introduction to Research 1. Respect For Persons Right to exercise autonomy & make informed choices. 2. Beneficence Minimization of risk + maximization of social/individual benefit How much information should participants get from a blinded, randomized trial? See ethics of clinical trials 3. Justice Research should not unduly involve groups unlikely to benefit from subsequent applications. Include participants of all races & both genders Members of target population on design & research team Research & researchers contribute to study population studied Communicate research results & develop programs/ interventions Psychology 242, Dr. McKirnan Research Ethics Home Back Next Page Psychology 242 13 Quasi-experimental designs Quasi-experiments Introduction to Research Experimental designs for “studies in nature”. Studying naturally occurring events Measurement studies Retrospective designs Evaluate existing groups or program Single shot survey or measure Non-equivalent groups Time series designs Psychology 242, Dr. McKirnan Research Ethics Home Back Next Page 14 Quasi-experiments Psychology 242 Introduction to Research 1. Study naturally occurring events that could not be brought into a lab or a true experiment. Measurement studies Retrospective designs 2. Evaluate existing groups or program(s) Simple survey of an intervention that already occurred Non-equivalent designs (due to…) Self-selection Non-random assignment Use of existing groups Participants not blind Time series designs, often with archival data Psychology 242, Dr. McKirnan Week 12-13, quasi-experimental designs. Home Back Next Page Psychology 242 15 Introduction to Research True v. quasi-experimental designs, 3 True experiments: Quasi-experiments: Emphasize Internal Validity Emphasize External Validity Assess cause & effect (in relatively Describe “real” / naturally artificial environment) occurring events Test clear, a priori hypotheses Clear or exploratory hypotheses Groups Equivalent at baseline Non-equivalent groups Random Assignment (or matching). Non-random assignment Participants & experimenter Blind to Existing groups assignment. Self-selection Participants not blind. Control study procedures Complete Control not Possible Create / manipulate the independent May not be able to manipulate the variable independent variable Control procedures & measures Partial control of procedures & measures Psychology 242, Dr. McKirnan Week 12-13, quasi-experimental designs. Home Back Next Page 16 Quasi-experiments that do not have a control group: Psychology 242 Introduction to Research Group Observe1 Intervention or event Observe2 Observe1 Confound Observe2 Threats to internal validity (confounds): Historical / cultural events occur between History baseline & follow-up. Individual maturation or growth occurs Maturation between baseline & follow-up. People respond to being measured or Reactive measures being a measured a second time. Extreme scores at baseline “regress” to a Statistical regression more moderate level over time. People leave the experiment non- Mortality / drop-out randomly (i.e., for reasons that may affect the results…). Psychology 242, Dr. McKirnan Week 12-13, quasi-experimental designs. Home Back Next Page Psychology 242 17 Introduction to Research Non-equivalent designs; pre- post- #2 Two Group Pre- Post- Design Group Observe1 Intervention or event Observe2 Group Observe1 Contrast group Observe2 Non-equivalent groups Intervention & Assessments often controlled by researcher in these Self-selection designs. Non-random assignment Similar to true Use of existing groups experimental Participants not blind design, except for non-equivalent groups Psychology 242, Dr. McKirnan Week 12-13, quasi-experimental designs. Home Back Next Page Psychology 242 Introduction 18 to Research Research Sampling. Define your target population Operationally define your criteria for population “membership” Define your sampling frame Probability sampling Non-probability sampling Psychology 242, Dr. McKirnan Week 3; Experimental designs Home Back Next Page 19 Sampling overview Psychology 242 Introduction to Research Who do you want to generalize to? Who is the target population? broad – external validity narrow – internal validity How do you decide who is a member? demographic / behavioral criteria? subjective / attitudinal? What do you know about the population already – what is the “sampling frame”. Is a Probability or random sample possible? “Hidden” population? Socially undesirable research topic? Easily available via telephone, door-to-door? Sampling frame adequate to choose selection method? Psychology 242, Dr. McKirnan Week 12-13, quasi-experimental designs. Home Back Next Page 20 Sampling overview, 2 Psychology 242 Introduction to Research Probability sampling Most externally valid simple Assumes: Clear sampling frame multi-stage Population is available cluster or stratified Less externally valid for hidden groups. Non-probability sampling Less externally valid targeted / multi-frame High “convenience” Best when: snowball No clear sampling frame quota, etc. Hidden / avoidant population. Psychology 242, Dr. McKirnan Week 12-13, quasi-experimental designs. Home Back Next Page 21 Descriptive research Psychology 242 Introduction to Research Qualitative or Quantitative Existing data Observational Describe an issue via Study behavior “in Use existing data for valid & reliable nature” (high new quantitative (or numerical measures ecological validity). qualitative) analyses Simple: frequency Qualitative Accretion counts of key In-depth interviews Study “remnants” of behavior Focus (or other) behavior Blocking” by other “ groups Wholly non-reactive variables Textual analysis Archival Correlational Qualitative Use existing data to research: “what relates quantitative test new hypothesis to what” Observational Typically non- Complex Direct reactive modeling Unobtrusive Psychology 242, Dr. McKirnan Descriptive Research. Home Back Next Page Statistics: an introduction 22 Psychology 242 Introduction to Research Using numbers in science Number scales & frequency distributions Central Tendency: Mode, Median, Mean Variance: Standard Deviation The Z score and the normal distribution Using Z scores to evaluate data Testing hypotheses: critical ratio. Home Back Next Page Psychology 242 Introduction Plato’s cave as a core allegory of scientific statistics (“run 23 to Research show” and mouse over different areas for text). Psychology 242, Dr. McKirnan Week 12-13, quasi-experimental designs. Home Back Next Page Psychology 242 24 Introduction to Research Core assumptions of the scientific approach We never observe “nature” directly, we only see its manifestations or images: We study hypothetical constructs; basic “operating principles” of nature; e.g., learning, motivation, “health”… We can only observe the effects of these basic principles, not the processes themselves. Rational analysis – theory – helps us deduce what the “form” of these processes must be, and how they work. We then test predictions from these theories. Theories are tested & variables are measured only with samples of people, places, and psychological states, not the entire population of people, places, or events. Psychology 242, Dr. McKirnan Exam #3 study guide Home Back Next Page Psychology 242 25 Introduction to Research External validity: summary Is the sample typical of the larger population? The research Is this Is the Sample: typical of outcome measure The The study “real world” represen- Dependent structure & The research settings tative, Variable context Setting: where the valid & phenomenon reliable? The occurs? Independent Variable Does the experimental manipulation (or measured predictor) actually create (validly assess…) the are phenomenon you sampling interested in? Psychology 242, Dr. McKirnan Experimental Design & Home Back Next Page 26 Types of numerical scales Psychology 242 Introduction to Research Ratio zero point grounded in physical property; “absolute” values continuous & equal intervals physical description: elapsed time, height Continuous Interval scales no zero point; scale values relative (scores on a continuous with equal interval continuum) behavioral research, e.g., attitude or rating scales. Ordinal rank order with non-equal intervals; no ‘0’ point Simple finish place, rank in organization... Categorical ‘values’ = categories only inherent categories: ethnic group, gender, zip code Central tendency used for: Mode (most common score) categorical variables Median (middle of distribution) categorical or continuous variables Mean (average score) continuous variables only Psychology 242, Dr. McKirnan Exam #3 study guide Home Back Next Page Psychology 242 27 Introduction to Research Distributions Mode Mean Normal distribution: mean = mode = Median median at center of the distribution Mean Median Bimodal distribution Mean & median are similar, at the center. Mode Skewed distribution: Extreme scores in Mode one direction make the median, and mean Median larger than the mode. Mean Psychology 242, Dr. McKirnan Exam #3 study guide Home Back Next Page Psychology 242 Introduction 28 to Research Measures of Dispersion or Variance Two estimates of Variance: 1. The Range of the highest to the lowest score. Similar to “average” amount each score deviates from the M. Simple & easy to compute. Provides simple idea of where scores fall Very sensitive to any extreme score(s) (“outliers”). 2. Standard deviation of scores around the Mean Similar to “average” amount each score deviates from the M. “Standardizes” scores to a normal curve, allowing basic statistics to be used. More accurate & detailed than range: Psychology 242, Dr. McKirnan Exam #3 study guide Home Back Next Page Psychology 242 29 Introduction to Research Introduction to normal distribution Properties of the normal distribution The normal distribution is a hypothetical distribution of cases in a sample It is segmented into standard deviation units. Each standard deviation unit (Z) has a fixed % of cases We use Z scores & associated % of the normal distribution to make statistical decisions about whether a score might occur by chance. Psychology 242, Dr. McKirnan Exam #3 study guide Home Back Next Page Psychology 242 Introduction 30 The Normal Distribution to Research 34.13% of scores from Z = 0 to Z = +1 Z = # of standard deviation and units from mean; from Z = 0 to Z = -1 M = 0, each standard deviation = 1 13.59% of scores Each segment of the curve (from Z=0 to Z=-1, and from 0 to +1) takes up a fixed 2.25% of scores percentage of area or % of cases. -3 -2 -1 0 +1 +2 +3 Z Scores (standard deviation units) Psychology 242, Dr. McKirnan Exam #3 study guide Home Back Next Page Psychology 242 31 Introduction to Research Normal distribution; Z scores Calculating a Z score to evaluate data To use the normal distribution to calculate how ‘good’ a score is… 1. Calculate how far the score (X) is from the mean (M); X–M. 2. “Adjust” X–M by how much variance there is in the sample; use the standard deviation (S). 3. Z = X–M / S How “good” is a score of ‘6' in two groups? A. The distance of the score from the M. B. The variance in the rest of the sample With low variance ‘6’ is higher (relative to other scores) then in a sample with higher variance. C. Criterion for “significantly good” score the guide What % of the sample must#3 study score be higher than… Exam Psychology 242, Dr. McKirnan Home Back Next Page Psychology 242 32 Introduction to Research Using the normal distribution, 2 A. The distance of the score from the M. The participant is 2 units above the mean in both tables. B. The variance in the rest of the sample: Table 1, high variance Table 2, low(er) variance Mean (M) = 4, Score (X) = 6 Mean (M) = 4, Score (X) = 6 Standard Deviation (S) = 2.4. Standard Deviation (S) = 1.15. (X-M = 6 - 4 = 2) (X-M = 6 - 4 = 2) Z (X-M/S) = 2/2.4 = 0.88 Z (X-M/S) = 2/1.15 = 1.74 About 70% of participants are About 90% of participants are below this Z score below this Z score Psychology 242, Dr. McKirnan Exam #3 study guide Home Back Next Page Psychology 242 33 Introduction to Research Evaluating scores using Z C. Criterion for a “significantly good” score X = 6, M = 4, S = 2.4, Z = .88 X = 6, M = 4, S = 1.15, Z = 1.74 70% of cases 90% of cases -3 -2 -1 0 +1 +2 +3 Z Scores (standard deviation units) Psychology 242, Dr. McKirnan Exam #3 study guide Home Back Next Page Psychology 242 34 Introduction to Research Core research questions Data Statistical Question One participant’s score Does this score differ from the M for the group by more than chance? Does this M differ from the M for the The mean for a group general population by more than chance? Means for 2 or more Is the difference between these Means more than we would expect by chance? groups -- more than the M difference between any 2 randomly selected groups? Scores on two Is the correlation (‘r’) between these measured variables variables more than we would expect by chance -- more than between any two randomly selected variables? Psychology 242, Dr. McKirnan Exam #3 study guide Home Back Next Page Psychology 242 35 Introduction to Research Summary Numbers are important for representing “reality” in science (and other fields). Different measures of central tendency are useful & accurate for different data; Mean is the most common. Median useful for skewed data Mode useful for simple categorical data Variance (around the mean) is key to characterizing a set of numbers. We understand a set of scores in terms of the: Central tendency – the average or Mean score The amount of variance in the scores, typically the Standard Deviation. Psychology 242, Dr. McKirnan Statistics introduction 1 Home Back Next Page Psychology 242 Introduction Summary 36 to Research Statistical decisions follow the critical ratio: Z is the prototype critical ratio: How far is your score (X) from the mean (M) X–M Z= How much variance is there among all the scores in the = sample [standard deviation (S)] S t is also a basic critical ratio used for comparing groups: How different are the two group Means M1 – M2 t= How much variance is there within each the two groups; = Variance grp1 Variance grp2 (“standard error of the mean”) n grp1 n grp2 Psychology 242, Dr. McKirnan Statistics introduction 1 Home Back Next Page Psychology 242 37 Revised 4/5/09 Introduction to Research Dr. McKirnan, Psychology 242 Introduction to statistics # 2 What can Plato’s Allegory of the Cave tell us about scientific reasoning? Was our hypothesis supported? The critical ratio and the logic of the t-test. The central limit theorem and "The Allegory of the Cave" by Allison Leigh Cassel sampling distributions Correlations and assessing shared variance Home Statistics Introduction 2. Back Next Page Psychology 242 38 Introduction to Research Plato’s Cave, 6 What does Plato’s Allegory of the Cave tell us about scientific reasoning? We cannot observe “nature” directly, we only see its manifestations or images: We are trapped in a world of immediate sensation; Our senses routinely deceive us (they have error). We cannot get outside our limited sensations to see the underlying “form” of nature Statistics Introduction 2. Home Back Next Page Psychology 242 39 Introduction to Research Plato’s Cave, 7 We study hypothetical constructs; basic “operating principles” of nature e.g., evolution, gravity, learning, motivation… Processes that we cannot “see” directly… …that underlie events that we can observe. We use rational analysis – theory – to deduce what the “form” of these processes must be, and how they work. Statistics Introduction 2. Home Back Next Page Psychology 242 Introduction 40 to Research Why can’t we just observe “nature” directly? 1. We can only observe the effects of hypothetical constructs, not the processes themselves. 2. Our theory helps us develop hypotheses about what we should observe if our theory is “correct”. 3. We test our hypotheses to infer how nature works. 4. Our inferences contain error: we must estimate the probability that our results are due to “real” effects versus chance. Statistics Introduction 2. Home Back Next Page Psychology 242 Introduction “Statistical significance” 41 to Research Using numbers to test Statistical Significance We assume a score with less than 5% probability of occurring has not occurred by chance alone (i.e., higher or lower than 95% of the other scores… p < .05) Z > +1.98 occurs < 95% of the time (p <.05). If Z > 1.98 we consider the score to be “significantly” different from the mean To test if an effect is “statistically significant” Compute a Z score for the effect Compare it to the critical value for p<.05; + 1.98 Psychology 242, Dr. McKirnan Statistics introduction 1 Home Back Next Page Psychology 242 42 Introduction to Research Statistical significance & Areas under the normal curve 95% of scores are between Z = -1.98 and Z = +1.98. Z = -1.98 Z = +1.98 2.4% of 2.4% of cases cases About 95% of cases -3 -2 -1 0 +1 +2 +3 Z Scores (standard deviation units) Psychology 242, Dr. McKirnan Exam #3 study guide Home Back Next Page Psychology 242 43 Introduction to Research Statistical significance & Areas under the normal curve With Z > +1.98 or < -1.98 In a hypothetical we reject the null distribution: hypothesis & assume 2.4% of cases are higher the results are not by than Z = +1.98 chance alone. 2.4% of cases are lower than Z = -1.98 34.13% 34.13% Thus, Z > +1.98 or < -1.98 Z = -1.98 of cases of cases Z = +1.98 will occur < 5% of the time 2.4% of by chance alone. 95% of cases 13.59% cases 13.59% of of 2.4% of cases cases cases 2.25% 2.25% of of cases cases -3 -2 -1 0 +1 +2 +3 Z Scores (standard deviation units) Psychology 242, Dr. McKirnan Statistics introduction 1 Home Back Next Page Psychology 242 44 Introduction to Research Critical ratio The strength of the results (our direct observation of nature) Critical ratio = Amount of error variance (the odds that our observation is due to chance) Difference between Ms for the two groups t= Variability within groups (error) Mgroup2 Mgroup1 Within-group Within-group variance, group2 variance, group1 control group experimental group Psychology 242, Dr. McKirnan Exam #3 study guide Home Back Next Page Psychology 242 45 Introduction to Research The Central Limit Theorem; large samples Central Limit Theorem True Population M With many scores (high n) Score Score “True” normal distribution “deviant” values are Score Score Score completely offset by other Score Score Score values. Score Score Score Score Score ScoreScore Score Score The distribution is Score Score Score Score Score Score normal, with low(er) Score Score Score Score variance. Score Score Score Score Score Score Score The sampling Score Score Score Score Score Score Score Score Score Score Score Score distribution well Score Score Score Score Score Score Score approximates the <-- smaller M larger ---> population distribution Psychology 242, Dr. McKirnan Exam #3 study guide Home Back Next Page Psychology 242 46 Introduction to Research Central limit theorem & evaluating t scores Central Limit Theorem The same logic applies with samples we used to test hypotheses. 1. If the groups in an experiment have small samples, it is more likely that high error variance, rather than an actual experimental effect, led to differences between the Ms. 2. Smaller samples (lower df) = more variance, so t must be larger for us to consider it statistically significant (< 5% likely to have occurred by chance alone). 3. Compare t to a sampling distribution based on df. 4. Critical value for t with p <.05 goes up or down depending upon sample size (df) Psychology 242, Dr. McKirnan Exam #3 study guide Home Back Next Page Psychology 242 Introduction Central Limit Theorem; variations in sampling 47 to Research distributions As samples sizes (df) go N > 120, t > + 1.98, p<.05 down, the estimated df = 18, t > + 2.10, p<.05. sampling distributions of t scores based on them have df = 8, t > + 2.30, p<.05. more variance, giving a more “flat” distribution. This makes the critical value for p<.05 increase. -2 -1 0 +1 +2 Z Score 2.4% of cases below this value (standard deviation units) 2.4% of cases above this value Psychology 242, Dr. McKirnan Exam #3 study guide Home Back Next Page Psychology 242 48 Introduction to Research Summary: Statistical tests t-test: Compare one group to another Experimental v. control (Experiment) Men v. women, etc. (Measurement) Calculate M for each group, compare them to determine how much variance is due to differences between groups. Calculate standard error to determine how much variance is due to individual differences within each group. Difference between groups Calculate the critical ratio (t): standard error of M Psychology 242, Dr. McKirnan Exam #3 study guide Home Back Next Page Psychology 242 49 Introduction to Research Statistics summary: correlation Pearson Correlation: measures how similar the variance is between two variables (A.K.A. “shared variance”) within one group of participants. are people who are above or below the mean on one variable similarly above or below the M on the second variable. If everyone who is a certain amount over the M on one variable (say, Z = +1.5) is the same amount above M on the other variable (Z also = +1.5) the correlation would be +1.0. Assess shared variance by multiplying ZX * ZY the person’s Z scores for each of the r two variables / df: n1 Psychology 242, Dr. McKirnan Exam #3 study guide Home Back Next Page Psychology 242 50 Introduction to Research Inferential statistics: summary, Key terms Plato’s cave and the estimation of “reality” Inferences about our observations: Deductive v. Inductive link of theory / hypothetical constructs & data Generalizing results beyond the experiment Critical ratio (you will be asked to produce and describe this). Variance, variability in different distributions Degrees of Freedom [df] t-test, between versus within –group variance Sampling distribution, M of the sampling distribution Alpha (α), critical value t table, general logic of calculating a t-test “Shared variance”, positive / negative correlation General logic of calculating a correlation (mutual Z scores). Null hypothesis, Type I & Type II errors. Statistics Introduction 2. Home Back Next Page Psychology 242 Introduction 51 to Research Multiple independent variables 4/14/09 Testing hypotheses about > 1 independent variable Factorial Designs: Main effects, Additive Effects, Interactions Psychology 242, Dr. McKirnan Home Back Next Page Psychology 242 52 Introduction to Research Designs with > 1 independent variable 1. Including a ‘control’ variable as an I.V. e.g., gender, age, race, etc. test if I.V. has the same effect within both groups 2. Testing hypotheses re: 2 or more I.V.s A. test separate, ‘main effects’ of each I.V. (Do each of these variables significantly affect the outcome?) B. test ‘additive’ effects of > 1 I.V.s simultaneously (What is the combined effect of these variables?) C. test interaction of 2 or more I.V.s (Does the effect of one I.V. on the outcome depend upon a second variable...?) Psychology 242, Dr. McKirnan Exam #3 study guide Home Back Next Page Psychology 242 Introduction Interaction example: Genetics, stress and depression, 1 53 to Research Interaction is very strong in an analysis of childhood trauma and depression. There is a general (main) effect whereby more trauma leads to greater likelihood of adult depression Psychology 242, Dr. McKirnan Exam #3 study guide Home Back Next Page Psychology 242 54 Introduction to Research Interaction example: Genetics, stress and depression, 2 However … the effect of trauma interacts with genetics Childhood trauma has no effect in people who have no genetic vulnerability. With increasing vulnerability, increasing trauma increases the likelihood of depression Psychology 242, Dr. McKirnan Exam #3 study guide Home Back Next Page Psychology 242 Introduction Example of a 3-way interaction 55 to Research Figure 3 Mean ratings of subjective stimulation and sedation on the BAES under 0.65 g/kg alcohol and placebo in women and men. Alcohol (v. placebo) made Alcohol made women much men much more stimulated. more sedated Psychology 242, Dr. McKirnan Multiple independent variables Home Back Next Page Psychology 242 56 Alternate portrayal of 3-way mood interaction Introduction to Research Placebo conditions do not show The alcohol conditions show a much effect classic “cross-over” effect for gender & mood; 50 45 Men get aroused 40 M BAES subscale scores 35 30 Men, Alcohol Men, Placebo 25 Women, Alcohol 20 Women, Placebo 15 10 Women get sedated 5 0 Stimulation Sedation Psychology 242, Dr. McKirnan Multiple independent variables Home Back Next Page Psychology 242 57 Introduction to Research Multiple IVs; summary 1 Multiple Independent Variables / Predictors: Tell us much more than simple main effects Some variables may combine with others (additive effects…) to produce very strong effects Variables may interact with others, so that they only affect the outcome at one level of a second variable… Drug use during sex leads to risk, primarily among those who have strong expectations that drugs decrease stress. Stress strongly predicts depression, only among people who are genetically vulnerable. Psychology 242, Dr. McKirnan Multiple independent variables Home Back Next Page Psychology 242 58 Introduction to Research Multiple IVs; summary 2 Multiple Independent Variables / Predictors: Are critical to theory development and testing: Changing sexual risk reduction requires that we understand both peoples’ psychological dispositions and their drug use patterns. Stress or other environmental events can “switch on” genes that create psychological or other problems; genetic dispositions and environment are not separate processes. Establish key “boundary conditions” to theory: when and among whom does a basic psychological process operate? Alcohol makes it more difficult to inhibit behavior, but primarily among men. Psychology 242, Dr. McKirnan Multiple independent variables Home Back Next Page Psychology 242 59 Introduction to Research Summary Key terms: Main effect Additive effect Interaction Cross-over interaction Factorial design Repeated measure Psychology 242, Dr. McKirnan Multiple independent variables Home Back Next Page Psychology 242 Introduction 60 to Research Complex experiments: Within- subjects & blocking designs Own control Reversal designs Repeated measures & Randomized block designs Psychology 242, Dr. McKirnan Home Back Next Page Psychology 242 Introduction Basic forms of within-subjects designs, 1 61 to Research Basic forms of within subjects designs; 1. Own control Each participant in control and experimental group. Optimally, order is counter-balanced 2. Reversal designs 3. Repeated measures & Randomized block designs Psychology 242, Dr. McKirnan Exam #3 study guide Home Back Next Page Psychology 242 Introduction Basic forms of within-subjects designs, 3 62 to Research Basic forms of Within subjects designs; 1. Own control 2. Reversal designs Hypothesis: behavior controlled by clearly bounded condition Design: “A – B – A”; impose – withdraw – impose condition 3. Repeated measures & Randomized block designs Psychology 242, Dr. McKirnan Exam #3 study guide Home Back Next Page Psychology 242 Introduction Basic forms of within-subjects designs, 2 63 to Research Basic forms of Within subjects designs; 1. Own control 2. Reversal designs 3. Repeated measures Multiple treatment conditions: each participant gets each treatment. Longitudinal / time sampling: each participant assessed over multiple time periods Randomized block designs; Repeated measure combined with between-groups variable. Psychology 242, Dr. McKirnan Exam #3 study guide Home Back Next Page Psychology 242 64 Introduction to Research Within subjects designs; own control, 2 1. Own Control Design Single Control Observe1 Experimental Observe2 Group Condition Condition All participants get the Control All participants then get the Condition and measurement experimental intervention and measurement. Experimental manipulation potentially very sharp for participants if there are no carry-over effects. Hypothesis tested by differences between conditions (Observation1 v. Observation2) within group. Internal validity: eliminate possible confound of group differences at baseline, since there is only one group. Statistical power increased: requires fewer subjects. Psychology 242, Dr. McKirnan Exam #3 study guide Home Back Next Page Psychology 242 Introduction 65 to Research Reversal designs 2. “REVERSAL” DESIGNS Test at baseline in normal state, Test under temporary experimental condition Test again under normal state. Examples: Role of incentives in enhancing performance Impact of anti-depressant drug on mood Effect of self-awareness on following social norms Psychology 242, Dr. McKirnan Exam #3 study guide Home Back Next Page Psychology 242 66 Introduction to Research Examples of reversal designs Test effect of, e.g., modeling (observation of attractive experimental confederate) on alcohol consumption. If the model influences participant’s behavior: 50 Consumption will 45 40 increase when the 35 model’s does… 30 Rate goes back down 25 when model’s does. 20 15 Up again with model, etc.. 10 5 0 Lo Lo H H ig ig w w h h Psychology 242, Dr. McKirnan Model's drinking rate Exam #3 study guide Home Back Next Page Psychology 242 Introduction Basic forms of within-subjects designs, 4 67 to Research Basic forms of Within subjects designs; 1. Own control 2. Reversal designs 3. Repeated measures & Randomized block designs Combine blocking or grouping variable with repeated measure. Most common repeated / within-Ss design Biomedical research Behavioral intervention evaluations Psychology 242, Dr. McKirnan Exam #3 study guide Home Back Next Page Psychology 242 68 Introduction to Research Randomized block designs Blocking Variable; between - subjects factor “Person” variable; age, gender, ethnicity, etc. not a “true” IV since people not randomly assigned; Or: Experimental condition; drug dose, treatment, etc. “True” IV with random assignment Repeated measure: within-subjects factor Multiple treatment conditions: each participant is observed in each treatment condition (e.g., high v. low drug dose, different instructions…) Or: Longitudinal / time sampling: measure D.V. over multiple time periods (Cohort studies) Psychology 242, Dr. McKirnan Exam #3 study guide Home Back Next Page Psychology 242 Introduction Within subjects designs; own control, 3 69 to Research Repeated measures / randomized block design Group 1 Baseline Control Measure2 M3 M4.. Measure Condition Group 2 Baseline Experimental Measure2 M3 M4.. Measure Condition Assignment Treatment. Primary Randomly or via Independent Variable. Control natural “blocks” group may receive Placebo. Baseline assessment Follow-up. Long-term prior to intervention or assessment of outcome or experimental condition. Dependent Variable. Time may represent 2nd Independent Variable. Psychology 242, Dr. McKirnan Exam #3 study guide Home Back Next Page Psychology 242 70 Introduction to Research Unprotected anal intercourse (UAI) with HIV positive and unknown sero-status partners among MSM, by study visit RUN THIS SLIDE TO SEE THE INFO. 60 60 Blocking 50 50 Blocking variable variable 40 40 PEP users PEP users 30 30 Participants reporting (%) Non users Non users 20 20 Non-users at the end Non-users safer All men get of PEP 10 10 of thetime are safest Repeated are study overgenerally safer (an additive effect) Measure 0 0 0 0 6 6 12 12 18 18 24 24 30 30 36 36 OR*(CI) p Month of study visit PEP 1.7(1.2-2.2) .001 Visit 0.97( .96-.99) .001 *Adjusted forDr. McKirnan site, drug use, Withineducation Psychology 242, age, study and Subjects Designs Home Back Next Page Psychology 242 71 Introduction to Research Summary Within subjects designs somewhat common in psychological research; Own control designs: create strong contrast for IV Eliminate problems in creating experimental v. control groups. Very common in bio-medical or public health studies; Most clinical studies are longitudinal; participants followed over time Intervention or experimental treatment is I.V. #1 (blocking or grouping variable). Stability over time is I.V. # 2 (repeated measure) Psychology 242, Dr. McKirnan Within Subjects Designs Home Back Next Page

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