Sampling & Experimental Control Psych 231: Research Methods in Psychology

Announcements This week: In lab turning in Methods, Appendix (stimuli), and IRB form for group projects Next week: Turning in first draft of class experiment write-up IN LABS Piloting group projects in labs next week, so need to have everything ready to go (the following week you are collecting the “real” data)

Variables Independent variables Dependent variables Measurement Scales of measurement Errors in measurement Extraneous variables Control variables Random variables Confound variables

VALIDITY CONSTRUCT CRITERION- ORIENTED DISCRIMINANT CONVERGENTPREDICTIVE CONCURRENT FACE INTERNALEXTERNAL Validity Does your measure really measure what it is supposed to measure?

VALIDITY CONSTRUCT CRITERION- ORIENTED DISCRIMINANT CONVERGENTPREDICTIVE CONCURRENT FACE INTERNALEXTERNAL Validity Does your measure really measure what it is supposed to measure?

External Validity Are experiments “real life” behavioral situations, or does the process of control put too much limitation on the “way things really work?” Variable representativeness Relevant variables for the behavior studied along which the sample may vary Subject representativeness Characteristics of sample and target population along these relevant variables Setting representativeness Ecological validity - are the properties of the research setting similar to those outside the lab

“Debugging your study” Pilot studies A trial run through Don’t plan to publish these results, just try out the methods Manipulation checks An attempt to directly measure whether the IV variable really affects the DV. Look for correlations with other measures of the desired effects.

Variables Independent variables Dependent variables Measurement Scales of measurement Errors in measurement Extraneous variables Control variables Random variables Confound variables

Sampling Population Everybody that the research is targeted to be about The subset of the population that actually participates in the research Sample Errors in measurement Sampling error

Sampling Sample Inferential statistics used to generalize back Sampling to make data collection manageable Population Allows us to quantify the Sampling error

Sampling Goals of “good” sampling: –Maximize Representativeness: –To what extent do the characteristics of those in the sample reflect those in the population –Reduce Bias: –A systematic difference between those in the sample and those in the population Key tool: Random selection

Sampling Methods Probability sampling Simple random sampling Systematic sampling Stratified sampling Non-probability sampling Convenience sampling Quota sampling Have some element of random selection Susceptible to biased selection

Simple random sampling Every individual has a equal and independent chance of being selected from the population

Systematic sampling Selecting every n th person

Cluster sampling Step 1: Identify groups (clusters) Step 2: randomly select from each group

Convenience sampling Use the participants who are easy to get

Quota sampling Step 1: identify the specific subgroups Step 2: take from each group until desired number of individuals

Variables Independent variables Dependent variables Measurement Scales of measurement Errors in measurement Extraneous variables Control variables Random variables Confound variables

Extraneous Variables Control variables Holding things constant Random variables Freely vary, to spread variability equally across all experimental conditions Confound variables Co-varys with both the dependent AND an independent variable

Colors and words Divide into two groups: men women Instructions: Read aloud the COLOR that the words are presented in. When done raise your hand. Women first. Men please close your eyes. Okay ready?

Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green List 1

Okay, now it is the men’s turn. Remember the instructions: Read aloud the COLOR that the words are presented in. When done raise your hand. Okay ready?

Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green List 2

Our results So why the difference between the results for men versus women? Is this support for a theory that proposes: “Women are good color identifiers, men are not” Why or why not? Let’s look at the two lists.

Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green List 2 Men Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green List 1 Women MatchedMis-Matched

What resulted in the performance difference? Our manipulated independent variable (men vs. women) The other variable match/mis-match? Because the two variables are perfectly correlated we can’t tell This is the problem with confounds Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green IV DV Confound Co-vary together

What DIDN’T result in the performance difference? Extraneous variables Control # of words on the list The actual words that were printed Random Age of the men and women in the groups These are not confounds, because they don’t co-vary with the IV Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green

Reminders This week: In lab turning in Methods, Appendix (stimuli), and IRB form for group projects Next week: Turning in first draft of class experiment write-up IN LABS Piloting group projects in labs next week, so need to have everything ready to go (the following week you are collecting the “real” data)