Exam 2 Outline
Exam 2 Outline PSYC-31574-003
Popular in Research Methods In Psychology
Popular in Psychlogy
This 4 page Study Guide was uploaded by Amy Turk on Wednesday April 13, 2016. The Study Guide belongs to PSYC-31574-003 at Kent State University taught by Dr. Tanjeem Azad in Spring 2016. Since its upload, it has received 43 views. For similar materials see Research Methods In Psychology in Psychlogy at Kent State University.
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Date Created: 04/13/16
Exam 2 Study Outline *In-Class Activity slides and handouts are included in this study outline as examples to accompany lecture content and foster comprehension of materials Chapter 5: Selecting Research Participants + Lecture notes + In-Class Activity on Sampling & Survey Methods Importance of sampling Sample vs. Population A Common Misconception Approaches to Sampling People Probability samples – better Everyone in population has equal probability of being in sample Representative Sample Generalize findings from sample to population Error of Estimation (margin of error) Function of: sample size, population size, and variance in the data Goals and Types of Probability sampling Simple random sampling Stratified random sampling Cluster sampling Threats to Representativeness Problem of Nonresponse Misgeneralization Nonprobability samples – easier, but probability of selecting person in population is not known Convenience sampling Quota sampling Purposive sampling Generalizability of sample assessed with replication to other samples How Many Participants? Economic sample (probability sampling) Power (affected by sample size, effect size) Chapter 6: Descriptive Research + Lecture notes + In-Class Activity on Sampling & Survey Methods Main type of descriptive research Survey Most common type Survey vs. Questionnaire Four survey designs Cross-sectional 1 group, 1 timepoint Successive Independent samples >1 group, >1 time point Longitudinal or panel 1 group, >1 time point Internet Advantages and Drawbacks Reporting Data Frequency distributions – summarizes raw data; can determine Median, Range, and Sample size from frequency distributions. Simple Grouped Measures of Central Tendency Mean – average score Median – middle score Mode – most frequent score Normal Distributions – most scores fall at or around the mean with a few outliers. *Know steps to determining what % of scores (68%, 95%, 99%) fall between score range Positively skewed distributions Negatively skewed distributions Z-scores (standard scores) – tells where a participant’s score falls in relation to the other participants. *Know formula and how to compute z-scores and how to manipulate formula Chapter 7: Correlational Research + Lecture Notes + In-Class Correlations Activities Purpose – to understand relationships Why is Correlation Research important? Correlation coefficient Pearson Correlation coefficient (r) Always between -1 and 1 Positive correlations - > 0.0 Negative correlations - < 0.0 No correlation = 0.0 *Know how to interpret and plot correlation Only tells about linear relationships Sign indicates direction Magnitude indicates strength 2 Coefficient of Determination (r ) Percentage of variance explained by the correlation Factors that distort correlation coefficient Restricted range Outliers Unreliable measures Correlation does NOT imply causation!!! Directionality and Third Variable Problem Chapter 9: Experimental Research + Lecture Notes + In-Class Experiments Activity Non-Experimental vs. Experimental Method – key difference is manipulation of IV Experiment Demo 3 Characteristics of Experiments Variables Independent Variables – variables that we manipulate, vary, change; the cause Dependent Variables – variables that we measure (i.e., how we are measuring our manipulation); the effect Independent Variables All experiments have at least one IV Must have at least 2 levels Can have more than one IV Can be discrete or continuous Types of IV manipulations Environmental manipulations Instructional manipulations Invasive manipulations Internal Validity Degree to which differences in performance between groups or conditions can be attributed to an effect of the IV Internal validity allows us to make causal claims Control over variables directly affects internal validity (an experiment is internally valid when it eliminates all potential sources of extraneous variables) Extraneous variables Variables that are NOT part of the research question but may affect behavior Want to control for extraneous variables When you cannot control for extraneous variables, they are a source of error Confounding variables Type of extraneous variable that unintentionally varies with the IV (are differences in conditions due to IV or due to confound?) Undermines internal validity of study *Know how to identify confounds Experimental Designs Between-subjects designs Simple random assignment Matched random assignment Within-subjects designs Advantages of within-subjects designs Eliminates need for random assignment Fewer participants needed More powerful Disadvantages of within-subjects designs Order effects Practice effects Fatigue Sensitization Carryover effects Counterbalancing Threats to Internal Validity Biased assignment to conditions Differential attrition Pretest sensitization Participant effect Experimenter effect Stimulus materials
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