Unit 2: Research Methods In Psychology (Chapter 2)

Question 1

Theory

Answer 1

An integrated set of related principles that explains and generates predictions about some phenomenon in the world.

Ex: Schachter-Singer theory of emotion (uses environmental cues to label unexplained or ambiguous signs of arousal).

Question 2

Putting common sense to the test

Answer 2

Ex: Late fee daycare study

Question 3

Hypothesis

Answer 3

A testable prediction about what will happen under specific circumstances if the theory is correct.

Ex: Subjects who encounter the lady on the dangerous bridge (vs. safe bridge) are more likely to call her and include erotic content in their written responses.

Question 4

Data

Answer 4

A set of observations that are gathered to evaluate the hypothesis.

Question 5

Replication study

Answer 5

Repetition of the study with a new group of participants. Direct replications attempt to recreate the original experiment exactly. Conceptual replications try to recapture the original finding using different methods or measures.

Question 6

Open science movement

Answer 6

Initiative to make scientific research, data, and methods openly accessible and transparent, with the goal of increasing reproducibility of research.

Question 7

Meta-analysis

Answer 7

Combination of the results of multiple studies (since there is only so much confidence one can obtain from a single study).

Question 8

Peer review

Answer 8

Critical evaluation of the study’s quality by trained psychological scientists.
Just because a research paper has been peer reviewed does not mean it’s free of limitations.

Question 9

Variable

Answer 9

Anything (typically something of interest) that can take on different values.

Ex (person): age, gender, ethnicity, mood, dance floor confidence level.
Ex (content, condition or situation): time of day, temperature, ambient noise, stressfulness.

Question 10

Manipulated variable

Answer 10

Variable intentionally changed by the researcher. Not all variables can be manipulated.

Ex: participants assigned to low vs. high stress condition.

Question 11

Measured variable

Answer 11

A variable whose values are simply recorded. Used in every study, as all variables can be measured.

Ex: # of life stress events a participant has experienced within the past year.

Question 12

Operational variable

Answer 12

Specific description of how a variable will be measured or manipulated in a study. (From abstract to concrete, quantifiable, specific.)
Creating an operational definition = operationalizing a variable.

Ex: Operationalizing positive emotionality = smiles in high school yearbook photos (using systematic method to describe and analyze facial movement).

Question 13

Fixed response questionnaires (surveys)

Answer 13

Specific set of questions and possible responses predetermined by the researchers.

Ex: Beck Depression Inventory.

Question 14

Open-ended (self-report) questions

Answer 14

Participant gives any answer that comes to mind. Helpful when studying something we don’t know much about yet. A way of gathering information to generate more specific questions later on.

Question 15

Self-report

Answer 15

People describe themselves and/or their behaviour.

Ex: Asking participants how many hours they spend on social media per week.

Question 16

Self-report advantages

Answer 16

• Allows us to “get inside people’s heads”.
• Easy, relatively inexpensive (in the case of surveys).
• Allows us to collect data from more participants, which will make our study stronger.

Question 17

Self-report limitations

Answer 17

• Social desirability bias.
• May be difficult to identify and verbalize experience (ex: how one feels).
• Not always aware of why we do the things we do. [Often relies on retrospective report -memories may be inaccurate or coloured (biased) by current experience. This can be mitigated by using methods where participants are asked to report their experience soon after it happens (ex: immediately after, at the end of each day).]

Question 18

Social desirability bias

Answer 18

Tendency to answer questions in a manner that will be viewed favourably by others. Includes impression management (“faking good”), which can be mitigated with anonymous participation, and self-deceptive enhancement (honestly held but unrealistic self-views).

Question 19

Behavioural observation

Answer 19

Direct observation: Researchers observe and record the occurrence of behaviour. Can take place in a lab or the field and use technology.

Ex lab research: Stage emergency to examine factors promoting or inhibiting helping behaviour. Ex naturalistic research: Jane Goodall’s observation of intergroup warfare among Gombe chimps.

Question 20

Behavioural observation advantages

Answer 20

• More objective than self-report (if done right).
• Observe real-world behaviour (or at least a close approximation).
• Source of nuanced, rich information.
• Possible to capture behaviours in their natural context.

Question 21

Behavioural observation limitations

Answer 21

• More time and resource-intensive. (Requires extensive training to achieve consistency and minimize bias. May not be able to recruit as many participants.)
• Reactivity
  (May use unobtrusive observation/recording, but this raises ethical issues.)

Question 22

Reactivity

Answer 22

A change in behaviour caused by the knowledge one is being observed.

Question 23

Indirect measures

Answer 23

Designed to avoid reactivity and social desirability. Ex: reaction time = the time it takes to respond to a stimulus on a screen. Can be used to assess implicit attitudes (the automatic tendency to associate a given stimulus with positive or negative feelings).

Question 24

Indirect measures pros

Answer 24

• Avoid social desirability & reactivity problems (could be particularly useful for sensitive topics).

Question 25

Indirect measures cons

Answer 25

• Big gap between construct of interest and operationalization (can we be sure that we are studying what we think we are studying?).

Question 26

Physiological responses

Answer 26

Body’s reaction to various experiences/stimuli.

Ex: autonomic nervous system activity, hormone changes, immune system changes, brain activity.

Question 27

Physiological responses pros

Answer 27

• Interesting in their own right (ex: understanding link between relationships and health).
• Outside participants’ control (not susceptible to social desirability bias, etc.).

Question 28

Physiological responses cons

Answer 28

• Very expensive = smaller sample size.
• Ambiguity in interpretation.
• Could be more invasive (depending on the measure).

Question 29

Population of interest

Answer 29

The full set of cases the researcher is interested in.

Question 30

Sample

Answer 30

The group who participated in research, and who belong to the larger group (the population of interest) that the researcher is interested in understanding. Sample size matters (bigger sample = better estimate)!

Ex: Jelly beans in jar

Question 31

Random sample

Answer 31

Every person in the population of interest has equal chance of inclusion.
You cannot obtain a random sample of the world population.

Question 32

Case study

Answer 32

Researchers study one or two individuals in depth, often those who have a unique condition. Do not generalize to the larger population, but may offer theoretical insights and research inspirations.

Question 33

Correlational research

Answer 33

A type of study that measures two (or more) variables in the same sample of people, and then observes the relationship between them.

Ex: Social media x well-being

Question 34

Scatterplot

Answer 34

A figure used to represent a correlation. The x-axis represents values for one variable, and the y-axis represents values for the other variable.
- Each individual in the study is represented by a dot placed between the axes according that individual’s variable values.
- Relationships can be positive (sloping upwards) or negative (sloping downwards); strong (dots clustered tightly together) or weak (dots spread out).

Question 35

Correlation coefficient / Pearson’s r / r

Answer 35

Statistic showing the direction and the strength of a relationship. Ranges from -1.0 to +1.0. Direction of the relationship is indicated by - or +. Strength of relationship is indicated by the value:
- The closer it is to 0, the weaker the relationship.
- The closer it is to -1 or +1, the stronger the relationship.

Question 36

Three criteria that must be met to establish causality

Answer 36

1) Two variables must be correlated.
2) One variable must precede the other.
3) There must be no reasonable alternative explanations for the pattern of correlation.
An experiment is the ideal way to establish causality.

Question 37

Experimental research

Answer 37

A study in which one variable is manipulated, and the other is measured (while all other variables are kept constant).

Question 38

Independent variable

Answer 38

The manipulated variable in an experiment.

Question 39

Dependent variable

Answer 39

The measured variable in an experiment. This variable depends on the level (or version) of the independent variable.

Question 40

Random assignment

Answer 40

Participants are as likely to be assigned to one condition as to another so that everything averages out to be the same in both groups.

Question 41

Control group

Answer 41

A condition comparable to the experimental condition in every way except that it lacks the one “ingredient” hypothesized to produce the expected effect on the dependent variable.

Question 42

Moderator variable

Answer 42

The effect of the independent variable on the dependant variable is conditional on value of the moderator.
The independent variable does not cause the moderator.

Ex: Maybe social media is only detrimental for younger individuals (i.e., age is moderator).

Question 43

Mediator variable

Answer 43

The independent variable exerts its effect on dependant variable through some other variable.

Ex: Social media use increases upward social comparisons, which leads to depression (upward social comparison is the mediator).

Question 44

Measurement validity / Construct validity

Answer 44

Are you measuring what you think you are measuring? The measure should make sense, be grounded in theory, be associated with theoretically similar measures, and have a predictive value.

Question 45

Reliability

Answer 45

Do you get the same results every time you administer the measure?
- Test-retest reliability = Measures at more than one time point don’t fluctuate.
- Inter-rater reliability. = Do other researchers score about the same?
A measure can be reliable, but not valid.

Question 46

Internal validity

Answer 46

Can we rule out alternative explanations in an experiment? Internal vaildity is threatened by the presence of confounds.

Question 47

How to avoid threats to internal validity

Answer 47

• Keep experimental conditions constant for all variables (except for the variable you want to study).
• Use random assignment
• Standardize study scripts
• Do not reveal hypotheses
• Make the study double-blind (if possible)

Question 48

Confound

Answer 48

An alternative explanation for a relationship between two variables; “muddies up” results. Occurs when two experimental groups accidentally differ on more than just the independent variable.

Ex: Rating a professor’s competency based on a profile – is the effect due to gender or age?

Question 49

Placebo effect

Answer 49

May experience improvement after receiving inert substances or inactive treatments. Without this condition, we would not know whether the improvement we see can be attributed to our treatment. Takeaway: Expectations are really powerful.

Ex: Knee surgery video.

Question 50

Double-blind procedures

Answer 50

Neither the experimenters nor the participants know who is in the experimental group or control group. This reduces the influence of bias and expectations.

Question 51

Observer expectancy effect

Answer 51

• The experimenter may, consciously or unconsciously, cue the participant in a way that confirms their expectations, ask leading questions, or interpret the participant’s behaviour differently (confirmation bias)
• The subtle cues (demand characteristics) from the experimenter may give the participant a sense of what is expected of them. The participant may try to “help” by acting in a way that conforms to the experimenter’s expectations.

Ex: The curious case of “Clever Hans”.

Question 52

Differential attrition

Answer 52

Participants drop out from experimental and control groups at different rates; the participants that drop out are not random. This is a common threat to internal validity.

Question 53

External validity

Answer 53

1) Can our results be generalized to other samples?
2) Can our results be generalized to other situations?
Difficult to establish high external and internal validity in the same study. Solution = Collect more data.

Question 54

Statistic

Answer 54

Numerical value derived from dataset that can help us describe the dataset or evaluate our research hypothesis.

Question 55

Descriptive statistics

Answer 55

Summarize sets of data.

Ex: mean, median, mode, standard deviation.

Question 56

Effect size

Answer 56

Values describing the strength of an association or magnitude of the effect.

Ex: r coefficient.

Question 57

Inferential statistics

Answer 57

Help us assess whether there is sufficient evidence to support a claim or hypothesis. Allow us to make inferences about the population from our
sample using rules of probability.

Ex: Hypothesis testing.

Question 58

Cohen’s d

Answer 58

Difference between groups (e.g., experimental & control) expressed in terms of standard deviation. Determined not just by absolute difference between groups, but also their spread.

Ex: Tallians vs Shortians.

Question 59

Hypothesis testing

Answer 59

An inferential technique; how do we know that the difference we observed between our two groups did not occur by chance alone?

Question 60

Null hypothesis

Answer 60

A type of statistical hypothesis that proposes that no statistical significance exists in a set of given observations

Ex: There is no effect of quitting social media on depression.

Question 61

P-values

Answer 61

The probability of getting a result as extreme as the one we observed if there really was no difference between the two groups (or no relationship between two variables). I.e. how likely the obtained results are under the null hypothesis.
- Takes on values between 0 and 1.
- P-value < 0.5 = reject the null.
- P-value > 0.5 = do not reject the null.

Question 62

Factors affecting the size of P-values

Answer 62

• The size of the observed effect. All else being equal, larger effects are more likely to be statistically significant.
• The number of participants in our study. All else being equal, results are more likely to be significant when we have more participants. With very large samples, even small effects may be statistically significant.
  Statistical significance ≠ practical significance.

Ex: Flip a coin for a cup of coffee. How many times does one have to win for the other to accuse them of cheating?

Question 63

What the P-value does not mean

Answer 63

• Statistical significance (low p value) does not mean the hypothesis is “true”.
• Statistical insignificance (high p value) does not mean the hypothesis is “false”.
• A statistically significant p-vale does not mean that the finding is important from a practical point of view. With a big enough sample size, even a trivial difference could be statisticallly significant.

Question 64

Institutional Review Board (IRB)

Answer 64

Panel made up of researchers, community members tasked with evaluating whether research study meets ethical standards:
- Autonomy;
- Beneficence;
- Justice.

Question 65

Autonomy

Answer 65

Each participant must have the right, without intimidation or coercion, to decide whether to participate in a study.

Question 66

Informed consent

Answer 66

Researcher must fully explain study procedures, including risks and potential benefits, prior to participation.

Deception in research may be required to maintin the integrity of a study. Here, the board would weigh harm to participant against benefits.

Question 67

Beneficience

Answer 67

• Obligation to promote well-being & minimize harm.
• Benefits of the study must outweigh the risks of harm. (What is the risk to participants? Can it be minimized?What are the benefits to society? Is there a risk of not doing this research?)
• Low risk with high benefit is most preferred for approval.

Question 68

Justice

Answer 68

Fairness in distribution of benefits and burdens of research without discrimination or favouritism. Participants must be representative of the population that will benefit from the study.

Question 69

Examples of unethical experiments

Answer 69

• Tuskegee syphilis experiments.
• Concentration camp experiments.
• MK Ultra mind control experiments.

Question 70

Guideline principles for psychologists testing on non-human animal subjects

Answer 70

• Replacement: Find alternatives for animals when possible.
• Refinement: Modify procedures to minimize animal diseases. Provide humane housing conditions.
• Reduction: Use the fewest animal subjects as possible.

Question 71

Theory-data cycle

Answer 71

The process of the scientific method, in which scientists collect data and can either confirm or disconfirm a theory.

Question 72

Scientific method

Answer 72

The process of basing one’s beliefs on systematic, objective observations of the world, usually by setting up research studies to test ideas.

Question 73

Descriptive research

Answer 73

A type of study in which researchers measure one variable at a time, with the goal of describing what is typical.

Question 74

Third-variable problem

Answer 74

When a correlation observed between two variables can actually be explained by some third variable.

Question 75

Descriptive statistics

Answer 75

Graphs or computsations that describe the characterstics of a batch of scores, such as its distribution, central tendancy, or variability.

Question 76

Frequency distribution

Answer 76

A bar graph in which the possible scores on a variable are listed on the x-axis from the lowest to highest, and the total number of people who got each score is plotted on the y-axis.

Question 77

Mean

Answer 77

Arithmetic average of a group of scores.

Question 78

Median

Answer 78

A measure of central tendancy that is the middlemost score.

Question 79

Mode

Answer 79

The most common score in the batch.

Question 80

Standard deviation

Answer 80

A variability statistic that caluclates how much, on average, a batch of scores varies around its mean.

Question 81

Open science

Answer 81

The practice of sharing one’s data, materials, analysis plans, and published articles freely so others can collaborate, use, verify, and learn about the results.

Question 82

Preregistration

Answer 82

When researchers make their hypotheses, methods, and planned statistical analyses public before they carry out a study.

Question 83

Experiment

Answer 83

A conducted study that investigates the direct effect of an independent variable on a dependent variable.

Question 84

Difference between set average score and variability

Answer 84

While the central tendency, or average, tells you where most of your points lie, variability summarizes how far apart they are.