Chapter #3: Research and Assessment

Case Study

closely study a particular event or person or group to find out as much as possible

Experimental Study

differences between groups to determine if the difference is larger than would be expected by chance

Correlational

research technique establishing relationship between two variables by measuring both variables as they occur naturally in a sample of participants

Correlational Coefficient

reflects strengths and direction of the relationship where r=1.0 is the strongest

Positive Correlation

as one variable increases, so does the other and vice versa with decreasing

Negative Correlation

as one variable increase, the other decreases; inverse relationship

Statistical Significance

result that would only occur by chance (<5%)

Null-Hypothesis Significance Testing (NHST)

Determines the chance of getting the result if nothing were really going on

Type I Error

false positive incorrectly rejecting a true null hypothesis

Type II Error

false negative failing to reject the null hypothesis

P-level

probability of obtaining a result if there is no difference between groups or no relationship between variables

Effect Size

index of the magnitudes or strength of the relationship between variables

Correlation Coefficient

the most commonly used measure of effect size

Variance

sum of squared deviations from the mean; squaring is a computational convenience but has no other rationale

Replication

finding the same result repeatedly, with different participants and in different labs

Publication Bias

 studies with strong results are more likely to be published

p-Hacking

hacking around in one’s data until one finds the necessary degree of statistical significance that allows one’s findings to be published; could be deleting unusually responses or adjusting results to influence extremes

Open Science

set of practices intended to move research closer to the ideals on which science was founded

WEIRD Samples

Western, Educates, Industrial, Rich, and Democratic