Sampling & Experimental Control Psych 231: Research Methods in Psychology

Sampling Why do we do we use sampling methods? –Typically don’t have the resources to test everybody, so we test a subset –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

Sampling Sample Population everybody that the research is targeted to be about the subset of the population that actually participates in the research

Sampling Sample Population Inferential statistics used to generalize back Sampling to make data collection manageable

Sampling Methods Probability sampling –Use some form of random sampling Non-probability sampling –Don’t use random sampling –These are typically not considered as good

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

Systematic sampling Selecting every n th person

Stratified sampling Step 1: Identify groups (strata) 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

Experimental Control Our goal: –to test the possibility of a relationship between the variability in our IV and how that affects our DV. Control is used to minimize excessive variability. To reduce the potential of confoundings.

Sources of variability (noise) Sources of Total (T) Variability: T = NonRandom exp + NonRandom other + Random Nonrandom (NR) Variability - systematic variation A. (NR exp )manipulated independent variables (IV) i.our hypothesis is that changes in the IV will result in changes in the DV

Sources of variability (noise) Sources of Total (T) Variability: T = NonRandom exp + NonRandom other + Random Nonrandom (NR) Variability - systematic variation B. (NR other )extraneous variables (EV) which covary with IV i.other variables that also vary along with the changes in the IV, which may in turn influence changes in the DV (Condfounds)

Sources of variability (noise) Sources of Total (T) Variability: T = NonRandom exp + NonRandom other + Random Non-systematic variation C. Random (R) Variability imprecision in manipulation (IV) and/or measurement (DV) randomly varying extraneous variables (EV)

Sources of variability (noise) Sources of Total (T) Variability: T = NR exp + NR other +R –Goal: to reduce R and NR other so that we can detect NR exp. That is, so we can see the changes in the DV that are due to the changes in the independent variable(s).

Weight analogy Imagine the different sources of variability as weights R NR exp NR other R NR other Treatment groupcontrol group

Weight analogy If NR other and R are large relative to NR exp then detecting a difference may be difficult R NR exp NR other R NR other

Weight analogy But if we reduce the size of NR other and R relative to NR exp then detecting gets easier R NR other R NR exp NR other

Using control to reduce problems Potential Problems –Excessive random variability –Confounding –Dissimulation

Potential Problems Excessive random variability –If control procedures are not applied, then R component of data will be excessively large, and may make NR undetectable –So try to minimize this by using good measures of DV, good manipulations of IV, etc.

Excessive random variability R NR exp NR other NR other R Hard to detect the effect of NR exp

Potential Problems Confounding –If relevant EV co-varies with IV, then NR component of data will be "significantly" large, and may lead to misattribution of effect to IV IV DV EV Co-vary together

Confounding R NR exp NR other Hard to detect the effect of NR exp because the effect looks like it could be from NR exp but is really (mostly) due to the NR other R

Potential Problems Potential problem caused by experimental control –Dissimulation If EV which interacts with IV is held constant, then effect of IV is known only for that level of EV, and may lead to overgeneralization of IV effect This is a potential problem that affects the external validity

Methods of Controlling Variability Comparison Production Constancy/Randomization

Methods of Controlling Variability Comparison –An experiment always makes a comparison, so it must have at least two groups Sometimes there are control groups –This is typically the absence of the treatment »Without control groups if is harder to see what is really happening in the experiment »it is easier to be swayed by plausibility or inappropriate comparisons Sometimes there are just a range of values of the IV

Methods of Controlling Variability Production –The experimenter selects the specific values of the Independent Variables (as opposed to allowing the levels to freely vary as in observational studies) –Need to do this carefully Suppose that you don’t find a difference in the DV across your different groups –Is this because the IV and DV aren’t related? –Or is it because your levels of IV weren’t different enough

Methods of Controlling Variability Constancy/Randomization –If there is a variable that may be related to the DV that you can’t (or don’t want to) manipulate you should either hold it constant (control variable) let it vary randomly across all of the experimental conditions (random variable) –But beware confounds, variables that are related to both the IV and DV but aren’t controlled

Poorly designed experiments Example: Does standing close to somebody cause them to move? –So you stand closely to people and see how long before they move –Problem: no control group to establish the comparison group (this design is sometimes called “one-shot case study design”)

Poorly designed experiments Does a relaxation program decrease the urge to smoke? –One group pretest-posttest design –Pretest desire level – give relaxation program – posttest desire to smoke

Poorly designed experiments One group pretest-posttest design –Problems include: history, maturation, testing, instrument decay, statistical regression, and more participantsPre-test Training group Post-test Measure Independent Variable Dependent Variable

Poorly designed experiments Example: Smoking example again, but with two groups. The subjects get to choose which group (relaxation or no program) to be in Non-equivalent control groups –Problem: selection bias for the two groups, need to do random assignment to groups

Poorly designed experiments Non-equivalent control groups participants Training group No training (Control) group Measure Self Assignment Independent Variable Dependent Variable

“Well designed” experiments Post-test only designs participants Experimental group Control group Measure Random Assignment Independent Variable Dependent Variable

“Well designed” experiments Pretest-posttest design participants Experimental group Control group Measure Random Assignment Independent Variable Dependent Variable Measure Dependent Variable

Next time Read: Chpt 8