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Psych > Week 2 - Studying Sex and Gender > Flashcards

Week 2 - Studying Sex and Gender Flashcards

1
Q

What is the importance of studying gender systematically?

A

It can decrease stereotypes and misconceptions relating to gender

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2
Q

Maximalist research approach

A

Emphasizes differences between different sex groups

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3
Q

Cons of a maximalist approach

A

Can promote gender stereotypes, Ignores the overlap between male and female distributions

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4
Q

Minimalist research approach

A

Emphasizes similarities between different sex groups

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5
Q

Cons of a minimalist approach

A

Could ignore potentially important sex differences, can fail to acknowledge that some differences do exist

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6
Q

How can some researchers show bias in the way they portray their findings?

A

By truncating the y-axis. Instead of showcasing the full range of possible values they shrink it to exaggerate differences.

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7
Q

The scientific method

A

A process for testing theory-driven hypotheses through systematic study.

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8
Q

Data collection methods

A

Primary, secondary

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9
Q

What is primary data collection?

A

Original data collection

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10
Q

What is secondary data collection?

A

Uses existing data

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11
Q

3 main types of research designs

A

Quantitative, qualitative, mixed-methods

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12
Q

What is the dominant research paradigm in psychology?

A

Quantitative studies

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13
Q

Quantitative Studies

A

Converting data into numbers that be statistically analyzed

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14
Q

True Experiments

A

Assigning participants randomly to conditions to ensure equal chance.

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15
Q

Random Assignment

A

Process of assigning participants to experimental conditions randomly, each person has an equal chance of ending up in each condition

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16
Q

Correlational Studies

A

Researchers are testing hypotheses about the strength and direction of relationships between variables

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17
Q

What is a weakness of correlational designs?

A

They cannot determine causality

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18
Q

What does stronger correlation mean

A

More accurate predictions

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19
Q

Reverse Causation

A

True cause-and-effect relationship between the variables are the reverse of what is initially assumed

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20
Q

Third variable problem

A

Possibility that a third unmeasured variable (z) is responsible for the relationship between x and y

For example: In the summer, both ice cream sales and crime rates increase, likely due to the third variable, hot weather

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21
Q

Longitudinal study

A

Researchers follow people over time and measure the variables at multiple points

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22
Q

What is a strength of longitudinal designs?

A

They reduce the ambiguity of cause-and-effect

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23
Q

Cross-sectional study

A

Researchers measure variables at one point in time

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24
Q

Qualitative methods

A

In-depth interpretations of situations, focusing on individuals experiences in context

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25
Examples of qualitative research methods
Case-studies, interviews, focus groups
26
Case-studies
In-depth investigation of a single entity, person, group or event, lack generalizability, interpretation varies between researchers
27
What are interviews in research?
Involve asking participants (individuals or groups) open-ended questions
28
What are the three types of interviews?
Unstructured, semi-structured and formal
29
Focus groups
Interviews conducted in group format and guided by a moderator to explore shared experiences
30
Which groups most benefit from focus groups?
They help to represent marginalized groups in research
31
Mixed methods studies
Combines quantitative and qualitative research methods, 3 kinds
32
What are the 3 kinds of mixed-methods studies?
Sequential explanatory, sequential exploratory and convergent parallel
33
Sequential explanatory design
Two-phase approach, quantitative data is collected and analyzed first followed by qualitative data to elaborate on the initial quantitative findings
34
Sequential exploratory design
Two-phase approach, qualitative data is collected and analyzed first followed by quantitative data to test and generalize the findings from the first phase
35
Convergent parallel
Collecting both quantitative and qualitative data simultaneously, analyzing it separately and then comparing findings to see if they converge or diverge
36
Secondary Data
Literature review, meta-analyses, critical review, systematic review, scoping review
37
Literature review
Examines existing published materials on a topic.
38
Meta-analyses
Combines results of multiple studies to analyze an effect.
39
Researcher bias
Researchers behaving in ways that influence the outcome of a study
40
Participant bias
Participants responses are influenced by what they think the researcher expects
41
Androcentrism
Tendency to view men as the norm and women as deviations
42
Female deficit model
Assumes sex differences stem from what women lack
43
Masculine generic
Using masculine terms to refer to all people
44
What are some things that can bias research?
Poor sampling methods, faulty measures and procedures
45
How can poor sampling create bias
Reduces the generalizability of the findings
46
What is the issue with lack of intersectionality in sampling
It ignores variables beyond sex, like race or class.
47
What is reflexivity in research?
Recognizing how researchers’ values influence their studies.
48
Scientific positivism
Belief that objective, value-free knowledge is possible through science.
49
Feminist critique of scientific positivism
It overlooks the influence of values and context in research
50
What does postpositivism recognize?
Acknowledges inherent biases in scientific research
51
What does social constructivism emphasize?
Understanding knowledge as shaped by social and cultural contexts
52
What is critical theory in gender research?
Examines power and inequality in research
53
Guidelines to conduct gender fair research
- Eliminate sex bias from sampling - Avoid using men as the standard - Use precise, non-gender-biased, non-evaluative terminology - Shouldn't exaggerate sex differences - Not imply that sex differences are due to biological causes when biological factors haven't been properly tested
54
How to measure bodily aspects of gender in research?
Include specific questions about physiological/bodily aspects
55
How to measure gender identity in research?
Include self defined gender identity as a free text response
56
How to measure legal gender in research?
Ask explicitly about legal gender and add a second question about assigned gender at birth if relevant
57
How to measure gender expression in research?
Ask participant specific questions about how feminine or masculine they see themselves and how others see them
58
Second wave of the women's movement
1970's, brought greater attention to women's issues
59
What is variance?
The measure of how far the scores in a distribution vary from the mean of the distribution.
60
What is a gender diagnosticity (GD) score?
Estimated probability that an individual is male or female depending on their interests
61
Why increase demographic diversity in samples?
To make research more representative and inclusive
62
What is the d statistic used for?
To qualify the magnitude and direction of a difference between groups
63
What is within-group variance?
How spread out the values are among people within the same group
64
What is between-group variance?
The difference between the average values for each group
65
What is a second-order meta-analysis?
A summary of multiple meta-analyses
66
What is an example of a very large sex differences?
Tendency to commit extreme violence (homicide, rape)
67
What is a quasi-experiment?
An experiment-like study without full control over the variables
68
What is an ex post facto design?
Compares groups based on pre-existing differences without random assignment Example: Do women smile more than men. (Researchers didn't assign people gender, they just compare existing groups
69
What is a person-by-treatment design?
Combines a measured participant variable with a manipulated variable Example: Do scary movies affect men and women differently? Person variable is men and women Treatment variable is everyone randomly assigned a scary or funny movie
70
What is an interaction effect?
When the effect of one variable depends on the level of another variable Example: Being watched impacting the amount that people smile
71
What is publication bias?
The tendency to publish studies with significant findings more often
72
What does falsifiable mean?
A theory that can be disproved through evidence
73
How can bias enter through data interpretation?
Through researchers expectations or participant assumptions
74
Why use full y-axis ranges in graphs?
To avoid exaggerating small differences
75
What does a medium effect size (d=0.50) imply?
67% overlap between two distributions
76
What size effect has the most overlap?
A small size effect (d=0.20, 85% overlap)
77
What is a large effect size (d=0.80)?
52% overlap between two distributions
78
Why is statistical significance not enough?
Doesn't tell us the size or practical importance of the effect

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