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Research Methods 2
| Question | Answer |
|---|---|
| Types of descriptive research | survey, demographic, epidemiological |
| Demographic research | characteristics of a population |
| Epidemiological research | occurence of disease or death |
| Pearson's r applies to what kind of relationships? | linear |
| Range of Pearson's r | -1 to +1 |
| Curvilinear relationship | quadratic, correlation inappropriate |
| How many SDs from the mean are outliers? | 3 |
| Online outlier | artificially inflates |
| Offline outlier | artificially deflates |
| Partial correlation | remove shared variance of variable we are not interested in |
| True independent variable | a variable a researcher can manipulate |
| Types of true IVs | environmental, instructional, invasive |
| Environmental IV | change in physical or social environment |
| Instructional IV | verabal instructions given to participants |
| Invasive IV | sugery or drugs |
| How many levels for true IV? | must have 2 |
| Subject variable | stable characteristics of participants that can't be manipulated |
| Between-subjects study | 1 control group, 1 experimental group |
| Within-subjects study | control and experiment conditions applied to 1 group |
| Systematic variance | variance that is systematically different between groups |
| Treatment variance | variance that occurs due to IV |
| Confound variance | variance that occurs due to any variable other than IV (result could have been from something other than IV if both variables occured at once) |
| Internal validity | accuracy of conclusions |
| Demand characteristics | aspects that indicate to subjects what experimenters are looking for (leads to expectency bias) |
| Single-item measure | one item provides enough information |
| Multi-item measure | set of items required to provide information about a more complex construct (depression, personality) |
| Criteria for causality | covariation, temporal priority (one variable changes before the other), causation when extraneous variables are controlled |
| When do researchers use multi-level modeling? | when there are nested samples |
| Factor analysis | many factors summarized by one factor (Big 5 personality test) |
| Z-score | standard deviation from the mean |
| Regression | relationship between the means of two variables (prediction) |
| Regression equation | Y=mX + b |
| Regression constant | b |
| Regression coefficient | m |
| Multiple regression | more than one predictor, more accurately predicts outcome |
| Multiple regression equation | Y=mX + b + c + d + .... |
| Simultaneous multiple regression | all predictors in same equation |
| Step-wise multiple regression | one predictor added to equation at a time |
| What does variation in the predictor variable tell us | % systematic variance in the outcome variable |
| Order effects | the order in which participants are given questions affects their responses |
| How are order effects controlled? | counterbalancing |
| Counerbalancing | one part of sample completes conditions in a certain order and the other completes them in a different order |
Created by:
karadub03
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