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Research Skills > Week 7 > Flashcards

Week 7 Flashcards

1
Q

What are the three goals of science?

A

Describe, predict, explain

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2
Q

What are the two goals of correlational research?

A

Describe and predict.

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3
Q

What is correlational research?

A

A form of non-experimental research. It is any research in which variables are measured not manipulated.

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4
Q

Three examples of when correlational research is used in terms of the independent variable is when the manipulation of the independent variable is not:

A
  1. possible
  2. feasible
  3. ethical
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5
Q

Correlational research is used when the manipulation of the independent variable is not possible. Give an example of this.

A

Is verbal intelligence associated with mathematical intelligence?

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6
Q

Correlational research is used when the manipulation of the independent variable is not feasible. Give an example of this.

A

Do human-caused CO2 emissions create global warming?

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7
Q

Correlational research is used when the manipulation of the independent variable is not ethical. Give an example of this.

A

Does smoking cause cancer?

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8
Q

Correlational research is used to establish reliability(1) and validity(2). What are the three types of these where correlational research is used?

A
  1. test-retest reliability
  2. convergent validity
  3. discriminant validity
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9
Q

Correlational research is also used in exploratory research. Give an example of this in regards to the coronavirus.

A

What behaviours during COVID-19 lockdown predict positive mental health outcomes?

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10
Q

What is exploratory research?

A

Research used to investigate a problem that is not clearly defined.

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11
Q

Is research setting important in correlational research?

A

No.

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12
Q

Is the statistical method in correlational research relevant?

A

No.

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13
Q

Would a comparison of groups using t tests be correlational if there was no random assignment to groups?

A

Yes, because the variables are measured not manipulated.

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14
Q

When is research in a laboratory considered correlational?

A

When no variables are manipulated or controlled.

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15
Q

What two kind of variables can correlational research involve?

A

Continuous and/or categorical variables.

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16
Q

Give an example of correlational research using the categorical variable of a daily to do list in comparison with a continuous variable of stress levels.

A

Do people who make daily to do lists experience less stress than people who do not make such lists?

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17
Q

Does correlation automatically imply causation?

A

No

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18
Q

Is it ever possible that two correlated variables might be causally related?

A

yes

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19
Q

If two variables are causally related, they will also be what?

A

Correlated

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20
Q

Should we reject a possible causal relationship if we learn that a study was correlational?

A

No, knowing a study is correlational does not give license to reject causation.

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21
Q

What use is Pearson’s correlation?

A

Quantifies strength of correlation between two continuous variables.

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22
Q

What are three descriptions that can be drawn from Pearson’s correlation?

A
  1. Is there an association
  2. Is the association strong or weak
  3. Is the association positive or negative
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23
Q

What does ‘p’ stand for in Pearson’s correlation?

A

population

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24
Q

What does ‘r’ stand for in Pearson’s correlation?

A

sample

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25
Q

What is the range of Pearson’s correlation?

A

-1.00 to +1.00

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26
Q

In Pearson’s correlation, what indicates the strength of relationship?

A

the magnitude

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27
Q

In Pearson’s correlation, what indicates the direction of relationship?

A

The sign (- or +)

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28
Q

What does Pearson’s r refer to?

A

Pearson’s product moment correlation ?

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29
Q

What is Spearman’s rho?

A

Use when variables are ordinal

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30
Q

What is Kendall’s Tau-

A

when variables are ordinal

31
Q

What are scatterplots?

A

Visual presentation of the relationship between two variables.

32
Q

In scatter plots, what represents a single point?

A

Two measurements for each individual.

33
Q

In Pearson’s r, what does -1 represent?

A

the strongest possible negative relationship

34
Q

what does 0 mean in Pearson’s r?

A

indicates no relationship

35
Q

What does +1 indicate in Pearson’s r?

A

The strongest possible positive relationship

36
Q

What does correlation assess?

A

How variables are paired

37
Q

What do different pairings within the same data create?

A

Different correlations

38
Q

What are four interpretations of a strong correlation?

A
  1. Does not mean there is a causal relationship
  2. Third variable
  3. causality may be reversed
  4. might reflect influence of outliers
39
Q

What are four interpretations of a weak correlation?

A
  1. Does not mean there isn’t a causal relationship
  2. might reflect truncated range
  3. might reflected curvilinear relationship
  4. might reflect outliers
40
Q

In Pearson’s correlation, can outliers strongly influence r?

A

Yes

41
Q

When can data make a strong overall correlation appear weak?

A

When one variable has a restricted range

42
Q

Give one example of how correlation doesn’t imply causation with Nicolas cage

A

Number of films Nicolas Cage is in positive correlates with number of drownings by falling in a pool. Should Nicolas Cage films be banned? Should it be illegal to cast Cage in movies?

43
Q

What is the assumption for Pearson’s r regarding linearity?

A

If there is a relationship it must be linear (straight line) not curved

44
Q

What are the two assumptions for Pearson’s r regarding interval or ratio data?

A
  1. The data for the Pearson’s r correlation must be interval or ratio level data.
  2. If data are ordinal, use Kendall’s tau or Spearman’s rho.
45
Q

What are the two assumptions for Pearson’s r regarding univariate normality?

A
  1. Both variables should be normally distributed

2. No outliers

46
Q

What is the assumption for Pearson’s r regarding ini-variate normal?

A

Distribution of variables in combination should be normal

47
Q

Describe the four steps involved in Pearson product-moment correlation

A
  1. calculate covariance
  2. problem: magnitude of covariance depends on range of scale. For the same strength of association, large values have large covariance, small values have small covariance
  3. standardise by dividing covariance by product of standard deviations
  4. interpret: range of r (-1,1)
48
Q

What does a magnitude of +/-.50-1.00 indicate?

A

strong relationship

49
Q

What does a magnitude of +/-.30-.49 indicate?

A

A moderate relationship

50
Q

What does a magnitude of +/- .10-.29 indicate?

A

A weak correlation

51
Q

What does a magnitude of +/-.10-.29 indicate?

A

a trivial

52
Q

in the correlation coefficient r, what does a number greater than 0 indicate?

A

a positive correlation

53
Q

with the correlation coefficient of r, what does a number less than 0 indicate?

A

a negative relationship.

54
Q

What is a confidence interval?

A

A range within-which the true value (parameter) is likely to lie.

55
Q

What does the width of the confidence interval indicate?

A

How precise our estimate of the parameter is, which is really useful for interpretation.

56
Q

Are confidence intervals symmetrical?

A

Except when r is exactly 0, CIs around r are not symmetrical.

57
Q

As the sample size (N) increases, what happens to confidence intervals?

A

They become narrower or more precise.

58
Q

What does r have to be converted to, in order to calculate a CI?

A

has to be converted to z-scores

59
Q

Does jamovi provide a CI automatically?

A

Yes

60
Q

Can you still calculate the CI for r if you don’t have the raw data?

A

Yes, if you have r and N, effect size worksheet can calculate it.

61
Q

Does does the regression do?

A

Quantifies the strength of association among one or more predictor variables and one criterion or outcome variable.

62
Q

What is example of regression?

A

How strongly are (the predictors) education level and lifestyle factors (exercise, diet) associated with (the outcome variable) life satisfaction?

63
Q

What does regression allow?

A

The prediction of one variable, from one or more measured variables. (e.g I want to use a person’s education level and lifestyle score to predict life satisfaction).

64
Q

What three things does simple regression involve?

A
  1. One predictor variable and one outcome variable.
  2. Mathematical procedure that determines the straight line that best ‘fits’ the data
  3. Minimises the distance between the outcome variable (Y) the predicted value of the regression line for each predictor value (X)
65
Q

What is the equation for the line used for in simple regression?

A

To predict the value of the outcome variable from the predictor variable (can be used for cases NOT observed)

66
Q

What is the intercept (constant) in simple regression?

A

the value of outcome variable (Y) when value of the predictor variable (X) is 0

  • not always meaningful or interpretable
  • centring predictor makes the intercept equal to the mean of the outcome variable
67
Q

What is the B coefficient (slope) in simple regression?

A

How much the outcome variable (Y) changes for each unit change of the predictor variable (X)

68
Q

What is R-squared in simple regression?

A

How much of the variance in the outcome variable (Y) is accounted for by predictor variable (X)

69
Q

What does R-squared equal in simple regressions?

A

equals r^2, hence the name

70
Q

Does R-squared provide information about causation?

A

NO

71
Q

How does R-squared indicate how much information the predictor variable provides about the value of the outcome variable in simple regression?

A

By multiplying R-squared by 100 to convert to %.

72
Q

What does it mean if R-squared = 1?

A

If you are given the value of the predictor you can calculate the exact value of the outcome.

73
Q

What happens if R-squared equals 0?

A

Predictor is not helpful at all.

74
Q

What is a summary of how to calculate the simple regression with jamovi?

A

create two continuous variables
from the regression icon, select linear regression
move the outcome variable into the dependent variable slot
move the predictor variable into the covariates slot
test overall model fit

Research Skills (13 decks)

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