Module 4

Question 1

Extraneous / Cofounded Variables

Answer 1

Variables that are irrelevant to the independent or predictor variables but that also vary systematically from one group to another in a study, create a problem for researchers.

Question 2

Blocking Variable & Randomized Blocking Design

Answer 2

Example: Researchers divide participants into two groups, a “block or blocking variable” based on sex and then randomly assign an equal number of participants of each sex to each of the treatments. This then becomes a randomized blocking design. (helps prevent possible critical confounds when the sample is small)

Question 3

Homogenous (restricted) Sampling

Answer 3

Keeping sample characteristic narrow by restricting it to a homogeneous group. When the pool is so restricted , members of the experimental and control groups resemble each other on these individual variables and the experimenters succeed in controlling all of the potential confounds.

DownSIDE - the generalizability of the study can be challenged. Researchers cannot use data to make assertions about the treatment of the general population

Question 4

Matching Participants (peron-for-person matching)

Answer 4

It is related to blocking but here matches are done on a case by case basis. The procedure use for matching people who are assigned to two different groups is to go into the pool of participants and identify pairs of individuals who match each other on one or more key variables.

Question 5

Within-Subject Design

Answer 5

When researchers choose to avoid sampling error by using the same participants under two or more different conditions.

Question 6

Role Selection

Answer 6

A set of behavioral prescriptions adopted by individuals to create a particular impression

Question 7

Unobtrusive measures and Nonreactive measures

Answer 7

measures taken that do not intrude on the natural flow of behavior, are constructed with an eye to controlling role selection and response sets.

Question 8

Non-interactional experimenter effects

Answer 8

Observer and interpreter effects. Also called the “personal equation”. Systematic differences in observations and variations in the recording of time and other measurements

Question 9

Interactional Experimenter effects

Answer 9

Selective ways (confounding influences that are different from one group to another) that experimenters and participants influence each other’s behavior during the research

Question 10

Biosocial Experimenter Effects

Answer 10

Parallel to what we are referred to as the demographic characteristics of the participants. Personal characteristics of data collectors who come in direct contact with participants can have an unplanned effect.

Question 11

Psychosocial Experimenter Effects

Answer 11

Parallel the participant’s personal characteristics, habits and traits. Was there anything about the experimenter’s personality, demeanor, behavior, manner or appearance that could of affected the way the participants responded?

Question 12

Self-fulfilling Prophecy

Answer 12

The effect of the researchers’ expectancies on the participants’ responses. Did the experimenters’ wittingly or not, exert any unplanned influence over the participants behavior’s?

Question 13

Selection (Threats to Internal Validity)

Answer 13

Systematic differences in the characteristics of participants in the different conditions of an experiment. These differences typically exist before the experimental manipulation occurs, and they arise because a mechanism other than random assignment is used to place people into groups. RANDOM ASSIGNMENT PREVENTS THESE DIFFERENCES FROM HAPPENING.

Question 14

History (Threats to Internal Validity)

Answer 14

Extraneous events that take place during an experiment and that have an effect on the participants

Question 15

Maturation (Threats to Internal Validity)

Answer 15

Changes within the participants during the study that would happen anyway with the passage of time. These can be long term changes (reaching puberty) or short term changes (tired or hungry)

Question 16

(Threats to Internal Validity)

Answer 16

• Selection
• History (local and general)
• Maturation
• Testing (practice effects)
• Multiple treatment interference
• Instrumentation (raters)
• Regression to the mean
• Attrition

Question 17

Testing/Practice Effects (Threats to Internal Validity)

Answer 17

Simply taking a test can have an effect of its own People react to psychological and physiological tests. Some learn from the initial experience and do better next time. Others do worse because difficulty with the test increases their anxiety the second time they take it.

Question 18

Multiple Treatment Interference (Threats to Internal Validity)

Answer 18

When experimenters require participants to receive more than one experimental treatment. The first treatment can influence the second treatment to an extent that makes it difficult to draw any conclusions about its effects.

Question 19

Instrumentation (raters) - (Threats to Internal Validity)

Answer 19

When the dependent variable comes from raters (not the subjects themselves), it is essential for the raters to be trained, motivated, conscientious, and able to give ratings that are consistently reliable and valid.

Question 20

Regression to the Mean (Threats to Internal Validity)

Answer 20

When subjects are selected into an experiment on the basis of the extremity of their scores. You need to be most carefully on the lookout for possible regression toward the mean when a treatment under study is meant to improve participants’ scores. Experimenters may be prone to choose participants who have the lowest or highest scores because these are the people who need the most help.

Question 21

Attrition (Threats to Internal Validity)

Answer 21

The loss of subjects from an experiment after it begins

Question 22

Pre-experimental Research Design

Answer 22

Designs that have no control group. (makes it impossible for researchers to even think about drawing causal inferences from their study)

INCLUDES:

• Descriptive Research
• One Group, PostTest Only Research (rarely seen in \psychology)

Question 23

Quasi-Experimental Design

Answer 23

In this design there is an identifiable manipulation or treatment, but the subjects have not been randomly assigned to conditions

INCLUDES:

• One Group, Pretest and PostTest
• Two Groups, Posttest only
• Two groups, pretest and postest
• Several others (see text page 109)

Question 24

True Experimental Designs

Answer 24

• Random Assignment with treatment and control
• Random Assignment, two experimental groups
• Random Assignment, three groups
• Random Assignment with pretest and postest
• Solomon four-group design
• Random Assignment, two groups, extended repeated measures
• Crossover designs and counterbalancing
• Designs with multiple independent variables

Question 25

Single-Case Designs

Answer 25

Experiments in that the researchers exercise control over the experimental conditions, especially their introduction and withdrawal, and attempt to control all the artifacts that could explain the results. If they are successful, the researchers are able to make causal inferences about this one case or this single group.

Single-case designs are within-subject designs that feature a series of repeat measures for a single individual or a single group.

Question 26

Mediating Variable

Answer 26

Mediated by….explained by. Asking the questions why does that relationship exist, how does it exist? What other variable may explain the relationship that I am seeing.

Question 27

Moderating Variable

Answer 27

For whom does this relationship apply? When does it exist? Under what conditions? Or might change? Moderating variable talks about how a relationship between an IV and DV variable changes because of different groups, or when certain things happen, or under certain conditions.

In the causal relationship between the IV and DV, a moderating variable increases or decreases the effect of the IV on the DV.

Question 28

Third Variable Problem

Answer 28

Says that there is something else out there in the environment that explains the variation in both the variables. Serves to uncover the relationship between the IV and DV.