Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

Psyc 344- Research Methods > Chapter 8: Bivariate Correlational Research > Flashcards

Chapter 8: Bivariate Correlational Research Flashcards

1
Q

What is the difference between experimental methods and correlation?

A
  • Experimental methods test if changes to IV causes changes to DV score. Ex. Does coffee make you anxious?
  • Correlations are descriptive methods. Look at behaviors as they occur. Ex. Is there a relationship between feeling anxiety (X) and coffee drinking (Y)?
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

What do correlations do?

A
  • Evalulate the strength of a relationship
  • The mathematical measure of an association between 2 variables
  • Do NOT establish cause
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

When would you use a correlation?

A
  • To make a prediction
  • For validity: If you made a new IQ test, to determine how good it is, you could compare results on the new test (X) to results on another valid test (Y)
  • For reliability: Measurements are stable if they produce similar results across situations. Ex. To see if your score today is correlated with your score next week
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

What are some association claims supported by correlational research? Why aren’t they causal statements?

A
  • Meaningful conversations linked to happier people
  • Couples who meet online have better marriages.
  • Tehse are not causal statements because there was no random assignment to different groups
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Bivariate correlation/association

A
  • Three kinds: pos, neg, zero
  • 2 variables (X and Y). Each participant will have a score on both variables. Asks: Is there a relationship between the way one variable changes and the way a second variable changes?
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

What does a scatterplot do?

A

Represents the score for each participant on both variables

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

Cacioppo et al, 2013

A
  • Found that ouples who meet online tend to have between marriages.
  • Emailed surveys asking “How did you meet?” and the Couples Satisfaction Index (CSI)
  • 19,000 respondents… people who met online tended to score slightly higher on CSI
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

What two pieces of information does the corrleation coefficient tell you?

A
  • Strength of the correlation
  • Direction of the correlation

Is a value between -1 and 1

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

How does the correlation coefficient (r) tell you the strenght of the correlation?

A
  • Closer to +/-1.0= strong relationship
  • Closer to +/- 0.01= weak relationship
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

How does the correlation coefficient (r) tell you the direction of the correlation?

A
  • Positive: values increase and decrease together
  • Negative: one value increases, the other decreases
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

How to visualize associations with categorical data?

A
  • Ex: Did you meet online? Categories are yes and no.
  • Scatter plots can still be useful, as you can seee a higher density of dots in online.
  • But bar graphs are usually more helpful for categorical variables
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

Do correlational studies establish internal validity?

A

NO

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

What is construct validity?

A
  • the degree to which a test or measurement tool accurately assesses the theoretical concept or “construct” it is intended to measure
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

How can you determine construct validity of association claims?

A
  • Because association claims describe the relationship between 2 variables, it is reasonable to ask about the construct validity of each variable.
  • Can ask: How well were each of the variables measured? Does the measure have good reliability? Is it measuring what it intends to measure? Face, discriminant, and convergent validity?
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

Using the CSI example, what questions should you ask to determine its’ construct validity?

A
  • Does it have good internal reliability?
  • Does it correlate with other measures of marital happiness?
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

What is face validity?

A
  • Compared to construct validity, which examines whether the tool truly captures the underlying theoretical construct, face validity is a more superficial assessment based on how the tool appears on the surface
  • Face validity is a subjective evaulation of whether a test “looks like” it measures what it intends to
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

What is discriminant validity?

A
  • The extent to which a test or measurement tool accurately differentiates between distinct constructs
  • Meaning, it should now show high correlations with tests designed to measure different, unrelated concepts
  • Demonstrates that a test is measuring what it intends to measure and not picking up on other unintended constructs
18
Q

What is convergent validity?

A
  • Measure of how closely a test or instrument correlates with other tests that measure the same or similar constructs
  • Established when there is a strong positive correlation between the results of a test and other tests that measure the same construct
19
Q

Questions to ask to determine statistical validity

A
  • How well do the data support the conclusion?
  • How strong is the relationship?
  • Effect size (the strength of a relationship between two or more variables)?
20
Q

Statistical validity and effect size

A
  • All else being equal, a larger effect size is better/more important
  • But a small effect size over multiple observations can also be important (Ex. taking 0.5 sceonds off an olympic athlete’s sprint time)
21
Q

Statistical Validity: What is aggregation and what are some examples

A
  • Aggregation: small effect sizes can compound over many observations
  • Ex. Baseball: MLB players have higher probability (r=0.05 of getting to a base on any given turn at bat compared to minor league baseball. This is a huge difference over 550 turns at bat.
  • Ex. Agreeableness: Agreeableness= better social interactions (r=0.07) in mid size uni first semester. But there are 100s of interactions in the first few weeks, so that is many ore positive interactions
22
Q

Aggregation: Many participants

A
  • Teens in US high schools with a growth mindset (can be smarter through effort and good learning strategies) have better grades (r=0.05)
  • If implemented at scale, this could be enough to prevent 79,000 teens from scoring in F and D range
23
Q

Statistical validity: How precise is the estimate?

A
  • How right/wrong could your estimate possible be?
  • 95% confidence interval= margin of error (range) of the estimate. Included true values that will be obtained 95% of the time
24
Q

Confidence Interval Example

A
  • Time spent sitting and medial temporal lobe thickness
  • R=-0.37 [CI=-0.07, -0.64]
  • Notice range does not include zero. When it does contain zero, it is possible there is no relationship

really big range bc data only represent 35 ppl

25
Q

Statistical Validity: Sample size and precision

A
  • When samples are small it is less stable
  • Outliers have a bigger CI
  • Smaller samples have bigger CIs
  • Bigger samples have smaller and more precise CIs
26
Q

Statistical validity: replication

A
  • Important to see if the study has been replicated.
  • Ex (Milek et al 2018) found there is a relationship between deep talk and overall wellbeing.
  • Ran repeatedly… 4/5 reflect a positive relationship
27
Q

Statistical Validity: outliers

A
  • Can the results be due to an extreme score?
  • Depending on the variable and the number of observations, a single score can skew the results
  • In association claims, outliers have a bigger impact when they are extreme on both variables (ex someone who is v tall and v heavy)
  • Outliers matter most when the sample is small
28
Q

Statistical Validity: Range of data

A
  • If there is not a fully represented range on one of the variables it can make the effect seem smaller
  • Can apply at any time one of the variables has little variance
  • SAT scores are predictive of performance in 1st year (r=0.33) but also only includes ppl w/ scores >1200. When you expand the range the correlation gets stronger
29
Q

Can we make causal inference from associations/What are the three criteria for causation?

A
  1. Covariance of cause and effect
  2. Temporal precendence- have a directionality problem
  3. Internal validity- problem w third variable
30
Q

Why does correlation not equal causation?

A
  • Correlations are descriptive, not experimental
  • Often there is another variable that is influencing X and Y
31
Q

Correlation does not equal causation example

A
  • Are disney princess movies related to agreement with female empowerment?
  • 3rd variable; boys who watch these likely have sisters
32
Q

What type of test is correlation?

A
  • Descriptive, set up to describe data
  • Not set up to ask “why” questions
  • Some very stange things are closely correlated
33
Q

Weird correlations

A
  • Ppl who drowned after falling out of a fishing boat and marriage rate in kentucky
  • Searches for JK rowling and coca cola stock price
34
Q

What is evaluative conditioning?

A

Pairing products with another thing people like

35
Q

Why would someone think correlation = causation?

A
  • This tendency reflects a bias of brains
  • The brain is a pattern seeker, and likes finding causal relationships even when none exist
  • The brain is neurologically tuned to notice when things occur together (Hebb Rule: neurons that fire together wire together)
  • We use statistics to overcome our biases
36
Q

Internal validity w association claims: the third variable problem

A
  • Height and hair length= negative correlation (but due to 3rd variable- gender)
  • But 3rd variable is not always a problem (height and weight- despite gender, generally taller women weigh more and taller men also weigh more)
37
Q

What is a spurious association?

A

The association is there but it is not “real”

38
Q

Association claims and external validity

A
  • Can an association be generalized? To other places, ppl, and times?
  • Size of sample matters less than how participants were selected. Random assignment is best
39
Q

What is a moderating variable?

A
  • When the level of a variable changes because of the level of another variable
  • A 3rd variable that influences strength or direction of the relationship between X and Y
  • Suggest that the relationship between 2 variables changes bc of the moderator
40
Q

Moderating variable: fandom example

A
  • The better the team does, the more tickets sold
  • But, residential mobility matters, as it increases fairweather fans
  • Moderating variable= residential mobility

Psyc 344- Research Methods (10 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions