Midterm 1 - Lecture 3 (CH4)

Question 1

How do we measure?

Answer 1

Operational Definitions

Question 2

Operational Definitions

Answer 2

Operational Definitions: a concrete way to measure an abstract object

• We are defining the variables that we hope to study

Question 3

In operationalizing, we’re defining variables we hope to study…

Variable

Answer 3

Variable: any event, situation, behavior, or characteristic that varies (ie. Isn’t constant)
* Need an operational definition to study something - YOUR PLAN FOR MANIPULATING/MEASURING

Question 4

3 types of Variables:

Answer 4

• Participant
• Situational
• Response

Question 5

Participant Variable (Types of Variables)

Answer 5

• Characteristics that individuals bring with them
• Can be measured, not manipulated

Question 6

Situational Variable (Types of Variables)

Answer 6

Characteristics of the situation or environment

• Can be measured and/or manipulated (EX: observing vs. instructing)

Question 7

Response Variable (Types of Variables)

Answer 7

2 components:

• Responses – can be measured only: performance on tasks (e.g. memory, math, perseverance, attention), reaction time, physiology (sweating, HR, BP, pupil dilation), self-report
• Behaviour – can be measured only: helping others, tipping in restaurants, donating money, smiling

Question 8

What negative/impactful variable can also exist?

Answer 8

CONFOUNDING VARIABLES: Any variable that we are not interested in, that is intertwined with our variable of interest (its op def’n).

May impact the interpretation of our result

Question 9

Types of Studies

Correlational Designs (Types of Studies)

Answer 9

• A type of non-experiment (no manipulation)
• Draw a sample, then measure 2 or more different variables: See how well they “hang together”, How co- related are the variables?, How strongly is one associated with the other?
  (EX: Facebook use & lonelines)

Question 10

Correlation

Answer 10

• A statistic that indexes the degree of relationship between two variables.
• +/- = “direction” of relationship: Positive (both increase), negative (one increases, other decreases), or non-existent (flat line)
• Number = “strength” of the relationship
• How closely is one variable associated with the other one?

Question 11

Linear relationships can be summarized by a single
“number”:

Answer 11

• r (r is only useful for linear relationships)
• can range from -1 to +1
• -1 = perfect negative linear relationship
• +1 = perfect positive linear relationship
  0 = no relationship

Question 12

Correlation DOES NOT equal…

Answer 12

Causation; third variables only come out of correlations

Question 13

Types of Studies

Experimental Designs

Answer 13

• Language: When can we say differences in variable A caused changes in variable B?

a. A must precede (happen before) B
b. When A is present, B must follow
c. When A is absent, B should not follow
d. When A is the only thing changed that might affect B

Question 14

Internal Validity (Key Features of Experiments)

Answer 14

Internal Validity: the ability to infer that the IV causes changes in the DV

• Co-variation between two variables
• Temporal precedence
• Eliminate plausible alternative explanations

Question 15

How do we work toward achieving internal validity?
(Key Features of Experiments)

Answer 15

1. Experimental Control
2. Random Assignment of people to condition

Question 16

Experimental Control (Ways to Increase Internal Validity)

Answer 16

– Only the IV changes across conditions

– No “confounds” (a second kind)

• Confound (recap):

– A variable that is intertwined with the IV and may impact (co-varies with) the DV

– Could explain all or part of the result

Question 17

Random Assignment (Ways to Increase Internal Validity)

Answer 17

A method of assigning subjects to experimental
and control group so that each subject in the experiment has the same chance of being assigned to any of the conditions

• Allows the researcher to balance out influence of all other (participant/situational) variables except the IV…

– Works “on average”

• More effective as number of participants increases

Random assignment to condition balances out misc. effects on the DV

Question 18

Designing experiments that allow for casual inferences:

Answer 18

Experiments allow for casual inferences in part because they incorporate temporal precedence

• Also, attempting to eliminate the influence of all other variables except for the one that is being manipulated: Usually achieved by trying to ensure that every other feature of the control and experimental conditions is held constant, except for the IV
• In non-experimental, both variables measured at the same time - resulting in no info about what comes first

Question 19

IMPORTANT.

3 requirements for casual inference (Experiments)

Answer 19

• Temporal precedence
• Need for covariation
• Eliminate plausible alternative explanations - which involve the possibility that some confounding variable could be responsible for the observed relationship
• Experiments do this through random assignment & different forms of experimental control

Question 20

What’s a crucial part in achieving internal validity?

Answer 20

Keeping variables constant

Question 21

Further criteria for claiming cause:

Answer 21

To be sufficient, the cause must always produce the effect

Question 22

Non-Experimental Designs

Answer 22

Non-experimental designs: relationships are studied by measuring or observing the variables of interest

For any non-experimental method, both variables of interest are measured and none are manipulated

Allows us to detect covariation between variables, another term to denote some non-experiments is the correlational method: in which variables are observed but not manipulated, shouldn’t be confused with the correlation statistic

Question 23

Interpreting the results of non-experimental designs:

Answer 23

Cannot directly tell us whether two things are causally related
- EX: discover that two variables are related, but not if one has a casual impact on the other

Also introduces the third-variable problem: possible additional variable for an observed correlation between two variables

Question 24

Third Variable Problem:

Answer 24

Two variables aren’t directly related to each other

Since third variables offer an alternative explanation for the observed relationship, correlations alone can never be considered evidence of cause

Question 25

How are third variables different from confounding variables?

Answer 25

• Third variables: cause the apparent relationship between two other variables
• Confounding variables: intertwined with another variable in your study, so you cannot tell which is at work

Question 26

How can we determine the direction of cause & effect?

Answer 26

One key issue to figure out what causes what is…
- Temporal precedence: what comes first in time
- Helps figure out what directions the causal influence flows

Question 27

How do confounding variables come about?

Answer 27

• A lack of precision in our operationalizations
• To prevent, researchers must forecast possible confounds and measure them as well

Question 28

Mediating variable

Answer 28

Mediating variable: psychological process that helps to explain the relationship between two other variables

• Bridge in-between
• Allows us to have more knowledge to explore possible interventions to help inattentive children make friends

Question 29

Difference between confounding and mediating variables:

Answer 29

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

Question 30

How can we address concerns of generalizations, in terms of experimental artificiality?

Answer 30

Field experiment: the IV is manipulated in a natural setting out in the real world

• Researcher still controls extraneous variables via random assignment, experimental control
• Advantage: takes place in a natural context
• Disadvantage: researcher loses the ability to directly control many aspects of the situation