Validity, Sampling & Experimental Control

Psych 231: Research Methods in Psychology

Announcements

The required articles for the class experiment paper are already on-line at the Milner course reserves page

Class Experiment

Collect the forms (consent forms and data summary sheets) - pass to the front

Brief discussion:– So how did it go? – What happened? – Any thing unusual/unexpected?– Any problems?

External Validity

Are experiments “real life” behavioral situations, or does the process of control put too much limitation on the “way things really work?”

Will the same basic conclusions be supported with different operational definitions, different participants, different research settings?

External Validity 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)– how do the characteristics of the research setting

compare with the “real world”

Internal Validity

The precision of the results Did the change result from the changes in the

DV or does it come from something else?

Threats to internal validity History – an event happens during the experiment Maturation – participants get older (and other changes) Selection – nonrandom selection may lead to biases Mortality – participants drop out or can’t continue Testing – being in the study actually influences how the

participants respond Statistical regression – regression towards the mean, if you

select participants based on high (or low) scores (e.g., IQ, SAT, etc.) their scores later tend to move towards the mean.

“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.

Sampling

Why do we do we use sampling methods?– Typically don’t have the resources to test

everybody

Population - everybody that the research results are targeted

Sample - the subset of the population that actually participates in the research

Sampling

Goals:– 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 Methods Probability sampling

– Simple random sampling– Systematic sampling– Stratified sampling

Non-probability sampling– Convenience sampling– Quota sampling

Simple random sampling Every individual has a equal and

independent chance of being selected from the population

Systematic sampling Selecting every nth 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.

• if there are other variables that influence our DV, how do we know that the observed differences are due to our IV and not some other variable

Sources of variability (noise)

Sources of Total (T) Variability:

T = NonRandomexp + NonRandomother +Random

Sources of variability (noise)

I. Nonrandom (NR) Variability – systematic variation

A. (NRexp)manipulated independent variables (IV) i. our hypothesis is that changes in the IV will result in

changes in the DV

B. (NRother)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

Sources of variability (noise)

II. Random (R) Variability A. imprecision in manipulation (IV) and/or

measurement (DV)

B. randomly varying extraneous variables (EV)

Sources of variability (noise)

Sources of Total (T) Variability:

T = NRexp + NRother +R

Our goal is to reduce R and NRother so that we can detect NRexp.

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 variabilility as weights

RNRexp

NRother

RNRother

Treatment group control group

Weight analogy If NRother and R are large relative to NRexp

then detecting a difference may be difficult

RNRexp

NRother

RNRother

Weight analogy But if we reduce the size of NRother and R

relative to NRexp then detecting gets easier

RNRother

RNRexpNR

other

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 baseline, or 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– Then you should either hold it constant, or let it

vary randomly across all of the experimental conditions

Potential Problems of Experimental Control Excessive random variability:

– If control procedures are not applied, then R component of data will be excessively large, and may make NR undetectable

Confounding: – If relevant EV covaries with IV, then NR component of data

will be "significantly" large, and may lead to misattribution of effect to IV

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

Next time

Read: Chpt 8