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FALL 2022 > Psych Stats Exam #4 > Flashcards

Psych Stats Exam #4 Flashcards

1
Q

How is correlational research different from experimental?

A

1) no manipulation of the IV
2) no random assignment
3) at least 2 DV’s measured

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2
Q

Purpose of correlational research

A

to explore association between variables

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3
Q

Correlation definition

A

the linear association between variables

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4
Q

What does the correlation coefficient provide?

A

An indicator of a linear relationship

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5
Q

Visualizing correlation

A

scatterplots: each point represents two measurements of the same person

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6
Q

Things to look for in a scatterplot

A
  • direction
  • scatter/dispersal
  • shape
    -outliers
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7
Q

Negative correlation

A

subjects with high scores on one variables tend to have low scores on the other variable

“when a score of X if above the mean of X, scores of Y will tend to be below the mean of Y” (and vice versa)

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8
Q

Positive Correlation

A

subjects with high scores on one variable tend to have high scores on the other variables (or low/low)

“when a score of X is above the mean of X, scores of Y will tend to be above the mean of Y” (and below/below)

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9
Q

Correlation coefficient definition (r)

A

statistic that quantifies the linear relationship between two variables
“ a measure of the tendency for paired scores to vary systematically”

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10
Q

What does the sign of r tell us?

A
  • direction NOT magnitude
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11
Q

R value ranges

A

positive +1 to negative -1
- tells us magnitude

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12
Q

Perfect linear relationship

A

+1 or -1 (usually don’t exist in nature)

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13
Q

R effect size guidelines

A

small: 0.1
medium: 0.3
large: 0.5

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14
Q

R as a descriptive statistic

A

describes effect size

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15
Q

R as an inferential statistic

A

you can compare it to a critical value to find the rejection region

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16
Q

Null hypothesis of correlation

A

there is not a linear relationship between A and B (r = 0)

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17
Q

r for a population

A

rho

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18
Q

degrees of freedom for correlation

A

df(r) = N-2
- N = number of pairs of observations (20 data points = 10 pairs of data sets)

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19
Q

Example of correlation write-up

A

“there is a statistically significant negative correlation - a negative linear relationship - between number of absences and exam score r(8) = -0.85, p<0.05. The more classes students miss, the worse they tend to perform on the exam.”

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20
Q

Correlation…

A

does not equal causation

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21
Q

Factors that influence r

A

1) truncated range
2) outliers
3) non linear relationships

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22
Q

Truncated range

A

zooming in on one group of people (ex: just high or low scores)
- can alter correlation: misrepresenting the true strength of the existing relationship by altering sample size

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23
Q

Outliers and small sample sizes

A

can mask or exaggerate a relationship between variables
- with a small sample size, outliers heavily affect results
- extremity of outlier: very extreme outliers have larger influences

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24
Q

Pearson’s correlation coefficient

A

for linear relationships only
used for parametric tests (scale DV)

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25
Q

Examples of nonparametric inferential tests

A
  • chi-squared tests
  • Mann-Whitney U test
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26
Q

Spearman’s correlation

A

used in nonparametric tests

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27
Q

When do we use nonparametric tests?

A

1) When assumptions of parametric tests are not met (population skewed or non linear)
2) small sample sizes (usually under 30)
3) DV is not scale (ordinal and nominal)

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28
Q

Disadvantages of nonparametric tests

A

1) tend to have low statistical power (higher probability of type II error)

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29
Q

Chi-squared

A
  • used when we only have a nominal variable
    “how different are the observed values from the expected values under the null hypothesis”
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30
Q

What is “O”

A

observed value

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31
Q

What is “E”

A

expected value (under the null hypothesis)

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32
Q

What is Σ

A

sigma: summation

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33
Q

what is χ2

A

chi-squared: test statistic

34
Q

Types of Chi-Squred tests:

A

1) chi-squared test for goodness of fit: one nominal variable, 2+ categories
- df = number of categories - 1
2) chi-squared test for independence: 2 nominal variables

35
Q

Misuse of NHST parts

A

1) failure to control for bias
2) low statistical power
3) poor quality control
4) p-hacking
5) publication bias

36
Q

What is replication crisis?

A

ongoing methodological crisis to replicate and reproduce psychological findings

37
Q

reproducibility

A

obtaining consistent results using the same original data, methodology, and analysis

38
Q

Replicability

A

obtaining consistent results across several studies that aim to answer the same question with different data

39
Q

Open science collaboration

A
  • attempted to reproduce the findings of 100 journal articles
  • 270 scientists
  • only 39% replicated
40
Q

Power posing

A
  • only self-reported feelings replicated, no physiological impact
41
Q

Smiling make you happier

A

did not hold up

42
Q

P value definition

A

the probability of your observed results (or results more extreme) occurring if the null is true

43
Q

Why reliance on P-value can be misleading

A

1) can result in binary thinking: 0.049 is significant but 0.5 is not
2) statistical significance is not necessarily meaningful (need to look at effect size)

44
Q

Tools to use besides P-values

A

1) confidence intervals: more precise and accurate measure of the sample mean as an estimate of the true population
- small interval = better precision

45
Q

Significant result but small effect size

A

something may be there but not meaningful

46
Q

Not significant result but large effect size

A

might indicate you missed something (type II error) - might indicate low power

47
Q

P-hacking (ways to increase power)

A

1) use a higher alpha
2) use a one-tailed hypothesis instead of two
3) increase sample size
4) somehow reduce variability
5) somehow make the difference between populations means bigger

48
Q

P-hacking (definition)

A

the misuse of data analysis to find and report statistically significant effects
- data dredging, data snooping, significance chasing

49
Q

Ways to P-hack

A

1) trimming data sets (get rid of outliers, zooming in)
2) adjusting values in the data set (what you think participants “mean”)
3) significance chasing: adding a few more participants at a time until the result becomes statistically significant
4) selective reporting: running many analyses but only reporting the ones that showed the desired effect

50
Q

Debunking published research

A

very hard - once we see reported evidence, it is hard to change our perceptions

51
Q

Publication bias

A

journals tend to publish significant results - may lead researchers to engage in shady research practices
- biased in incomplete understanding: important to know what is NOT different as well
- “file drawer problem”

52
Q

Best Practices

A
  • publish what you plan to collect and analyze to you don’t adjust
  • people held accountable
53
Q

Simple regression

A

use data to produce an equation for a straight line that captures the trend of the data
- used to make predictions about Y given a particular X score

54
Q

Multiple Regression

A

use data to produce an equation for a line including MANY variables
- multiple predictor variables
- can compare strength of different variables on how they jointly affect Y

55
Q

IV in regression

A

predictor variable

56
Q

DV in regression

A

outcome/criterion variable

57
Q

Line of best fit

A

captures the best trend of the data

58
Q

Simple linear regression equation

A

ŷ = a + bX
y = predicted score on outcome
a = intercept
bX = slope of regression line (predicts change in Y for an increase of 1 unit in X)
b = unstandardized regression coefficient
- can not flip variables and get same regression

59
Q

Ordinary least square (OLS) estimation

A

used to draw a line minimizing error/residuals

60
Q

standardized beta

A

a 1 standard deviation increase in (IV) is related to (beta value) standard deviation increase in (DV)
- used in multiple regressions

61
Q

Write up for multiple regression (beta)

A

“Controlling for all other measures variables (TV exposure, age, lower grades, parent education and education aspirations) exposure to sexual content on TV is still a significant predictor of pregnancy”

62
Q

Intercorrelated

A

all variables relate to one another

63
Q

Regression can not:

A

1) establish temporal precedence: do not know what came first (can not determine cause and effect)
2) control for variables that aren’t measured (can not measure all the variables in the world)

64
Q

How is regression different from correlation?

A

Correlation: association between 2 variables Regression: prediction of DV using IV

65
Q

When to use Mann-Whitney U

A

test for significant difference between two independent samples (two levels of IV, ordinal/nominal DV)
- parametric partner: independent samples t-test

66
Q

When to use Wilcoxon signed-rank T-test

A

Test for significant difference between two paired samples (two levels of IV, nominal/ordinal DV)
-parametric partner: paired samples t-test

67
Q

When to use Wilcoxon-Wilcox comparison test

A

Test for significant differences among all pairs of independent samples (three levels of IV, and ordinal/nominal DV)
- parametric partner: one-way ANOVA, tukey HSD tests

68
Q

When to use Spearman correlation coefficient

A

Describe the degree of correlation between two variables (nominal/ordinal DV)
- parametric partner: Pearson coefficient (r)

69
Q

When is the mean larger than the median?

A

Negative skewed data

70
Q

When is mean smaller than the median?

A

Positive skewed data

71
Q

Descriptive Statistics

A

Summarizing a distribution of data with a single number - conclusions you draw from numbers

72
Q

Sample size and rejecting the null

A

Sample size increase: easier to reject the null

73
Q

Parameters

A

number describing the population
- muew: mean
- s-hat = standed deviation

74
Q

Statistics

A

number describing sample
- mean = M
- S = standard deviation

75
Q

Practical use of power

A
  • can be used to determine the sample size required to detect an effect size
76
Q

Type I error

A

False alarm: you said yes but there is no effect

77
Q

Type II error

A

Miss: you missed an effect that was actually there

78
Q

Statistical Power definition

A

the probability that we will correctly reject the null when we should

79
Q

What is NHST?

A

Null-hypothesis significance testing
Testing against a null hypothesis (no significant difference) to see how odd your results are

80
Q

Robust parametric tests

A

When an assumption of a parametric test is violated, but the test still operates (mostly) as intended
- The tests we’ve covered this semester are robust against the assumption of normality

81
Q

Spearman vs Pearson Correlation

A

Pearson:
- parametric: scale DV
Spearman:
-non parametric: nominal/ordinal DV

82
Q

If assumptions of a parametric test are met and you use a nonparametric test you are more likely to…

A

make a Type II error

FALL 2022 (11 decks)

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