1. Some Terminology experiment vs. correlational study IV vs. DV descriptive vs. inferential statistics sample vs. population statistic vs. parameter H 0 vs. H 1 (or H a ) (hypotheses) Type I vs. Type II error constructs and operational definitions reliability and validity continuous vs. categorical variables scales of measurement

2. 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.

3. Null Hypothesis Significance Testing ask whether observed relationships in sample reflect true population relationships, or mere natural sampling variability null hypothesis H 0 : default description of data relationships in population - can it be rejected on basis of sample? alternative hypothesis H 1 (or H a ): any data relationship in population other than what H 0 specifies Type I error - conclude H 0 false when it's true Type II error - conclude H 0 true when it's false "significance" - conventionally, "p<.05": less than 5% probability of observing this data if H 0 is true, which leads us to reject H 0

4. 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.)

5. 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. 60-72)]

6. 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

7. 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

8. Reliability and Validity

9. 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

10. 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"

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

12. 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

13. 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

14. 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

15. 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

16. Figure 2-11 (p. 50) Examples of different shapes for distributions.

17. Figure 3-14 (p. 96) Measures of central tendency for skewed distributions.

18. Figure 4-2 (p. 106) Population distributions of adult heights and adult weights.

19. Figure 4-6 (p. 116) The graphic representation of a population with a mean of µ = 40 and a standard deviation of  = 4.

20. Figure 4-7 (p. 117) The population of adult heights forms a normal distribution. If you select a sample from this population, you are most likely to obtain individuals who are near average in height. As a result, the scores n the sample will be less variable (spread out) than the scores in the population.