1. 9 research designs likely for PSYC 2100
1) 1 factor, 2 levels, 1 group (one group gets both treatment levels) related samples t-test (compare means of 2 levels only)
2) 1 factor, 2 levels, 2 groups (one group for each treatment level) independent measures t-test (compare means of 2 levels only)
3) 1 factor, 3+ levels, 1 group (one group gets all treatment levels) repeated measures ANOVA (compare means of any number of levels)
4) 1 factor, 3+ levels, 3+ groups (one group for each treatment level) independent measures ANOVA (compare means of any number of levels)
2 factors, 2+ levels, 1 group repeated measures factorial ANOVA (2 factors require ANOVA even if factors have only 2 levels)
2 factors, 2+ levels, 4+ groups, independent measures factorial ANOVA (2 factors w/ minimum 2 levels each, so minimum 4 possible combinations, each group gets one combination)
7) 2 factors, 2+ levels, 2+ groups mixed factorial ANOVA (one independent measures factor, one repeated measures factor; number of groups = number of levels in independent measures factor)
correlational design correlation coefficient (r)
9) count / frequency / proportion in categories chi-square (2)

2. 9 research designs (format from class lecture)
1) 1 group, 1 factor, 2 levels related samples t-test
2) 2 groups, 1 factor, 2 levels independent measures t-test
3) 1 group, 1 factor, 3+ levels repeated measures ANOVA
4) 3+ groups, 1 factor, 3+ levels independent measures ANOVA
5) 1 group, 2 factors, 2+ levels repeated measures factorial ANOVA
6) 4+ groups, 2 factors, 2+ levels independent measures factorial ANOVA
7) 2+ groups, 2 factors, 2+ levels mixed factorial ANOVA (one independent groups factor, one repeated measures factor)
correlational design: r
9) count / frequency / proportion data: chi-square

3. SCIENCE naturalistic explanation
all explanation is of nature, in terms of nature
allows no entities that have powers beyond other entities, that are not subject to determinism, that are uncaused or otherwise unique
our view of nature: scientific materialism
only matter in motion exists
view of nature resulting from 17th century science

4. SCIENCE AS METHOD systematic empirical observation
guided by theory to reveal something about world
theory is set of testable propositions
has implications for observation
organizes past observations
guides future observations
focuses on solvable problems
publicly observable data
replication by others using method info
peer review in journals

5. DETERMINISM

6. Is Science the Only “True” Way of Knowing the World?
Sometimes, we are all guided by authority figures.
Sometimes, we just use common sense to get around in the world
Sometimes, we accept truths on the basis of belief or faith alone
BUT, science is based on direct observation and empirical testing.

7. The science of Psychology seeks to
1. Describe behavior
2. Predict behavior
3. Understand behavior
4. Change behavior
How does it accomplish these aims?
--by using the Scientific Method
From Zechmeister et al. text

8. Goals of Science Scientific Method Theory Prediction Revision
Describe
Predict
Understand
Control
Scientific Method
(deductive
thinking)
world of
concepts
Theory
Prediction
Revision
(inductive
thinking)
“real”
world
Observation
Verification

9. Where do Methods & Statistics Fit?
“real”
world of
concepts
Observation
(inductive
thinking)
Theory
Prediction
(deductive
Verification
Revision
Methods
Correlational
Experimental
Statistics
Hypothesis
Testing

10. Steps of the Scientific Method
1. Develop a research question
2. Generate a research hypothesis
3. Form operational definitions
4. Choose a research design
5. Evaluate the ethics
6. Collect data
7. Analyze data and form conclusions
8. Report research results
These steps are used in both basic and applied research

11. Cyclical Process Report the Results Define the Question
Analyze the Data
Design the Study
Collect the Data

12. Some Terminology experiment vs. correlational study IV vs. DV
descriptive vs. inferential statistics
sample vs. population
statistic vs. parameter
H0 vs. H1 (or Ha) (hypotheses)
Type I vs. Type II error
constructs and operational definitions
reliability and validity
continuous vs. categorical variables
scales of measurement

13. Experiment -- involves random assignment of participants and control over the research situation to minimize the influence of other variables and reveal the causal effect of the manipulation.
Correlational Study -- examines direction and strength of relationship between variables; no cause implied.
Independent variable -- the one manipulated by the experimenter (cause).
Dependent variable -- the one measured by the experimenter (effect).
Descriptive Statistics -- statistics and methods for organizing and summarizing data.
Inferential Statistics -- techniques to permit inferences or generalizations from samples to the populations from which they were drawn.
Statistic is to sample as Parameter is to population.

14. Null Hypothesis Significance Testing
ask whether observed relationships in sample reflect true population relationships, or mere natural sampling variability
null hypothesis H0: default description of data relationships in population - can it be rejected on basis of sample?
alternative hypothesis H1 (or Ha): any data relationship in population other than what H0 specifies
Type I error - conclude H0 false when it's true Type II error - conclude H0 true when it's false
"significance" - conventionally, "p<.05": less than 5% probability of observing this data if H0 is true, which leads us to reject H0

15. two major problems in psychological research
measurement problem: relation between constructs and operational definitions is not as tight as in other natural sciences, making construct validity an important issue
noise problem: inherent variability among individuals, and within individuals from occasion to occasion, makes it impossible to attain exact group equivalence or replication and obscures effects of independent variables of interest; makes internal validity issues especially important (e.g., random assignment, ruling out confounds, etc.)

16. Reliability The consistency or repeatability of a measure
The degree to which a measure would give you the same result over and over, assuming the phenomenon being measured is not changing
Cannot be calculated, only estimated
[Based on true score theory of measurement (Trochim pp )]

17. three types of validity (there are many others)
construct validity (addresses measurement problem) - relation between constructs and operational definitions; consider exams, SATs, behavioral vs MRI measures of cognitive processing; includes "face validity" or how good the measure SEEMS to reflect the construct on the surface
internal validity (addresses noise problem, among others) - use of random assignment and other aspects of experimental method to ensure legitimate conclusions
external validity (concerned with applying experiment's conclusions to real world) - use of random selection of participants so they represent the population accurately; includes "ecological validity" or similarity of processes in lab setting to the real world processes being investigated

18. Construct Validity
Construct validity is the approximate truth of the conclusion that your operationalization accurately reflects its constructs. Central questions to ask are:
“Is your operationalization an accurate translation of the construct?”
“Does your program/treatment accurately reflect what you intended?”
“Does your sample accurately represent your idea of the population of interest?”
“Are you measuring what you intended to measure?”

19. Internal Validity
“The approximate truth about inferences regarding cause-effect (causal) relationships” (Cook & Campbell,1979)
The primary consideration in establishing cause and effect
Key question: Can observed changes (effect) be attributed to the program or intervention (cause) and not some other possible (alternative) cause?
Only relevant to the specific study in question (i.e., is not concerned with generalizability)

20. Random Selection and Random Assignment
Random selection is how you draw the sample of people for your study from a population—impacts external validity.
Helps insure that the sample is representative of the population (and hence, findings are more generalizable)
Random assignment is how you assign the sample to different groups or treatments in your study—impacts internal validity.
Helps insure that groups are comparable at the beginning of the study

21. Reliability and Validity

22. types of research design: correlational vs. experimental
correlational design
typically examines how 2 variables go together in a single group
no casuality implied because no control is assumed, and confounds and spurious or coincidental relationships are probably present

23. types of research design: correlational vs. experimental
experimental design
typically compares mean DV scores of 2 or more groups
intent is to change one thing between the groups and then attribute group differences on the dependent variable to the difference in treatments (independent variable)
"change ONE thing" (manipulation) implies "keep everything else the same" (control)
when random assignment and other appropriate controls are in place, the manipulation of the IV allows causal conclusions to be drawn
when participants are not randomly assigned to treatments, the method is only superficially experimental and is called "quasi-experimental"

24. experimental control
physical control (for environmental variables, not participant variables): temperature, lighting conditions, time of day, noise levels
control by experimental design...

25. experimental control: control by experimental design
hold constant (for environmental variables, some subject variables): temperature, lighting; age, sex; not really IQ (even if measurement were accurate, you wouldn't choose only people with IQ = 126); definitely not anxiety or authoritarianism or depression
matching (for environmental variables and explicitly measured participant variables): have corresponding subjects (e.g., similar IQ) in each treatment group so groups are equal on average (equated at individual or group level); groups may still differ on unsuspected variables
random assignment (for all variables): all characteristics, known or unknown, are randomly spread across all groups so they're the same on average

26. nuisance variability (nuisance variables): factors affecting scores on the DV other than the factor you're interested in
unsystematic nuisance variability doesn't affect one group more than another or bias scores or correlations to be higher or lower - just adds to variability (noise) you're trying to see through
systematic nuisance variability does affect one group more than another or bias scores or correlations to be higher or lower - confound: don't know which factor to attribute DV differences to
random assignment converts systematic nuisance variability into unsystematic by distributing it randomly among all groups

27. Scales of Measurement nominal: assign labels to categories
ordinal: assign order to categories
interval: ordinal, and includes equal distances
ratio: interval, and includes an absolute zero

28. Scales of Measurement nominal: car color, sex, religion, ethnicity
ordinal: reading grade level; exam finishing order
interval: Fahrenheit temperature; IQ, SAT (?)
ratio: Kelvin temperature; height; reaction time