Research Methods Review Guide

***NOTE: This is the review guide for the entire course. Be sure to note in class the material that will be covered on each individual exam!

Be sure you understand and can explain each term or concept in THOROUGH AND EXTENSIVE DETAIL. This includes (but is not limited to) being able to spot examples and applications of each concept.

Material for Exam One:

1. skepticism, rationalism, empiricism, positivism, logical positivism
2. science, theory, pseudoscience, pseudotheory falsifiability
3. socialscience/behavioral science, behavioralism
4. anthropology, sociology, psychology, economics, political science
5. grand theory, paradigm, model
6. empirical/positive research, normative/post-positive research
7. quantitative research, qualitative research, post-modernism, methodological pluralism
8. research design, literature review, literature gap,
9. hypothesis, operationalization, data source, data collection, data coding
10. results/analysis, conclusions/implications
11. induction, deduction
12. research questions: descriptive, relational, causal
13. correlation/covariation v. causation
14. correlation types: positive, negative/inverse, nonlinear/curvilinear
15. accidental/random correlation, intervening variable, spurious correlation
16. unit of analysis, n, cases/observations
17. variable, constant
18. variable types: dependent, independent, control
19. variable characteristics: name, label, value, value labels
20. measure/indicator, surrogate
21. ***Be able to specifically critique a model, and create hypotheses, new operationalizations, and new variables along with their operationalizations for that model.
22. ***Be able to identify examples of all the concepts listed above. That is, if you are shown a real-world example of some particular research, be able to identify what concept the real-world example is illustrating.

Material for Exam Two:

1. pre-test, stimulus, post-test
2. hypothesis, operationalization, variable, constant
3. variables: independent, dependent, control
4. variable characteristics: name, label, value, value labels
5. unit of analysis, n, cases/observations
6. correlation types: postive, negative/inverse, nonlinear/curvilinear
7. accidental/random correlation, intervening correlation, spurious correlation,
8. validity, face validity, construct validity, internal validity
9. external validity, generalizaibility
10. threats to internal and external validity, Hawthorne Effect
11. reliability, test-retest reliability, inter-coder reliability
12. research design types: experimental, quasi-experimental, nonexperimental
13. pre-test, stimulus, post-test, feasibiliy, preliminary study
14. population, census, true/actual value, parameter,
15. sample, observed value, statistic, random, nonrandom/biased, pseudorandom
16. selection bias, self-selection/nonresponse bias, accessibility bias, affinity bias
17. RDD (Random Digit Dialing), Likert Scale, data set/database, codesheet, codebook
18. variable types: nominal (including dummy/dichotomous), ordinal, interval
19. descriptive statistics, univariate statistics, frequency distribution table
20. frequency, percent, cumulative percent, bar chart, pie chart
21. measures of central tendency: mode, mean, median,
22. distributions: uniform, bimodal, normal (bell curve)
23. measures of disperson: range, interquartile range, standard deviation, z-score
24. Bivariate statistics, contingency table, clustered bar chart, stacked bar chart
25. ***Be able to specifically critique a model, and create hypotheses, new operationalizations, and new variables along with their operationalizations for that model.
26. ***Be able to identify examples of all the concepts listed above. That is, if you are shown a real-world example of some particular research, be able to identify what concept the real-world example is illustrating.
27. ***Be able to calculate statistics and draw tables, charts, and graphs using the data from hypothetical research data.

Material for Exam Three (Final Exam):

1. hypothesis, operationalization, variable, constant
2. variables: independent, dependent, control
3. variable characteristics: name, label, value, value labels
4. variable types: nominal (including dummy/dichotomous), ordinal, interval
5. unit of analysis, n, cases/observations
6. correlation types: postive, negative/inverse, nonlinear/curvilinear
7. accidental/random correlation, intervening correlation, spurious correlation,
8. internal validity, external validity (generalizability)
9. research design types: experimental, quasi-experimental, nonexperimental
10. pre-test, stimulus, post-test, feasibiliy, preliminary study
11. population, census, true/actual value, parameter
12. sample, observed value, statistic, random, nonrandom/biased
13. frequency, percent, cumulative percent, bar chart, pie chart
14. measures of central tendency: mode, mean, median,
15. distributions: uniform, bimodal, normal (bell curve)
16. measures of disperson: range, interquartile range, standard deviation, z-score
17. Bivariate statistics, contingency table, clustered bar chart, stacked bar chart
18. measures of association, line graph, scatterplot/scattergram
19. Pearson Product-Moment Correlation Coefficient (Pearson's R), direction, strength
20. inferential statistics, statistical significance, sampling distribution
21. Central Limit Theorem, point estimate, interval estimate/confidence interval
22. margin of error, confidence level, research hypothesis, null hypothesis, p
23. level of significance, social science convention regarding statistical significance
24. regression/linear regression, best fit line, intercept, constant
25. regression equation y = a + bx, bivariate regression, slope,
26. slope coefficient/regression coefficient, coefficient, r-squared,
27. multivariate regression, partial slope coefficient,
28. interpretation of regression constants and coefficients
29. Chi-Square, cell, t-test, ANOVA, logit
30. ***Be able to specifically critique a model, and create hypotheses, new operationalizations, and new variables along with their operationalizations for that model.
31. ***Be able to identify examples of all the concepts listed above. That is, if you are shown a real-world example of some particular research, be able to identify what concept the real-world example is illustrating.
32. ***Be able to identify which different statistical test should be used on data.
33. ***Be able to specifically interpret/explain statistics given for hypothetical data, such as a computer printout of statistical data analysis, such as for correlations, chi-square, regression, etc.