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RESEARCH > research lecture 5 > Flashcards

research lecture 5 Flashcards

1
Q

what are parametric statistics used for

A

to analyze QUANTITATIVE DATA

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2
Q

what are examples of parametric Statistics that are used to analyze quantitative data

A

t-test, ANOVA, Pearson correlation, linear regression

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3
Q

parametric stats are based on which distrcutions so the data needs to be normalized

A

t-distribution, F-distribution, chi-
square distribution

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4
Q

non parametric statistics are used to analyze what kind of data

A

qualitative

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5
Q

t Spearman rho, Mann-Whitney U, Friedman’s ANOVA, Wilcoxon-signed ranks .. these are examples of what

A

non parametric statistics that are used to analyze qualitative data

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6
Q

what is our options when we have =violated assumptions or have nominal or ordinal data

A

non parametric statistics

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7
Q

what is a statistical way to look at relationship among variables and it is based on a linear models

A

regression

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8
Q

when is a linear regression significant

A

when the slop is not equal to 0

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9
Q

what is a T test

A

see the difference between 2 means and it is significant if the slop does not equal 0

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10
Q

what are teh parametric assumptions for t-test or one way ANOVA

A
  1. I/R data
  2. Normality
  3. Homogeneity of Variance
  4. Free of Extreme outliers
  5. Independence of observations
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11
Q

what is normality

A

real concern in smaller studies with a n < 30 (bc of the central limit theory)

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12
Q

what are 3 way to assess normality

A

-check histogram
-skewness/kurtosis (if >2 or < -2 there is a problem)
- shapiro wilk test (want this to be greater then .05 to be significant)

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13
Q

what happens to skewness as the sample size increases

A

it improves

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14
Q

what is homogeneity of variance looking for and what should be the same

A

looking for difference , the variances of the outcome variable should be able the same in each group

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15
Q

how to assess for homogeneity of variance

A

Levene’s test

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16
Q

what do u want the levenes test to show

A

want it not to be significant , u want them to show “no difference”

set alpha at .05

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17
Q

data must be ___ of observations .. scored must not follow a pattern over time also scores from one participants can’t influence another participants score

A

independent

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18
Q

when u are looking at a graph how do u know if there is a problem with independence

A

if some of the scores are the same or follow a pattern there is a problem , it should all be reandiozned

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19
Q

what are 3 regression assumptions

A
  1. linearity
  2. homoscedasticity
  3. outlier testing in regression
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20
Q

what is the difference between homogeneity of variance and homoscedasticity

A

HOV is the difference in stats and homoscedasticity is the relationship between stats

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21
Q

(T/F) In correlational/relationship analyses (ex regression), the variance of the outcome variable must be about the same at all levels of the predictor variable

A

T

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22
Q

if the variance is no evenly distributed what is it called

A

having hereoscedasticity

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23
Q

when looking at a graph how can u tell the difference between homoscedasticity and heterosscedasticity

A

Homoscedasticity the dots are along the line of regression and for the other one the dots kinda look like a funnel it does from skinny to wide

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24
Q

how are the data points arranged in linearity

A

in a linear pattern

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25
Q

what is the easiest way to check for linearity

A

creat a scatterplot

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26
Q

what kind of residual does the outlier have

A

a large one

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27
Q

how do u find residual on a linear graph

A

its the distance from the dot to the actual line

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28
Q

what are scatterplots also used for

A

to check homoscedasticity and outliers

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29
Q

Scatterplots are also used to check for homoscedasticity and outliers. To do this…we must do what

A

figure out every participants residual score

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30
Q

to have homoscedasticity all of the ___ should be about the same

A

residuals

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31
Q

when you violate an assumption what are 2 ways that is good to do to fix it

A
  1. analyze with bootstrapping in SPSS
  2. use non parametric statistics
32
Q

what are 3 bad things to do if u violate the assumption and u want to fix it

A
  1. trim the data
  2. windsorizing
  3. transform the data
33
Q

what is a hypothesis test

A

a statical method that uses the sample data to evaluate a hypothesis about a population

34
Q

what is the general goal of a hypothesis test

A

rul out chance (sampling error) as a plausible explanation for the results from a research study

35
Q

what are teh steps in a hypothesis testing

A
  1. bathing evidence . make a case for ur study
  2. state hypothesis
  3. select test statistic
  4. calculate test statistic
  5. statistical descion
    • reject the HO or do no reject the HO , if reject then alternative HO is true if do not reject then null is true
36
Q

the purpose of the hypothesis test is to decide between 2 exmplnations

A
  1. the difference between the pre/post samples CNA BE EXPLAINED BY THE SAMPLING ERROR (fail o reject the null
  2. he difference between the pre/post samples is too large to be explained by sampling error. (there does appear to be a treatment effect).
    - reject the null
37
Q

what hypothesis states there is no change int he population before and after and intervention

A

null

38
Q

in the context of an experiment, H0 predicts that the independent variable had no effect on the dependent variable…. what states this

A

the null

39
Q

what hypothesis states there is a change in the population following an intervention

A

alternative hypothesis

40
Q

what type of error fixes the alpha level determine

A

the risk for type 1 error

41
Q

what is it called on a graph where it consists of outcomes that are very unlikely to occur if the null hypothesis is true

A

critical region

42
Q

what is calculated as the difference between he pre and post means / the amount of diffference one would expect without any treatment effect

A

test statistic

43
Q

if the test statistic is int he critical region not he graph what can we conclude and what do we reject

A

the difference is significant or that the treatment has a significant effect and we reject the null

44
Q

 If the test statistic is not in the critical region, what can we conclude

A

that the evidence from the sample is not sufficient and the descision is fail to reject the null

45
Q

what is the actual probability that the results occurred just because of sampling error.

A

the p value

46
Q

when is the p value significant

A

when it is smaller (or equal) to the alpha level that is set in advance

47
Q

if the p value is within the small end of the tails then it is ___

A

significant

48
Q

The hypothesis test is influenced not only by the size of the treatment effect and the variability of the sample but also the size of the ____ .

A

sample

49
Q

what do we use as a standaridized measure of effect size

A

cohens D

50
Q

what does cohens d measure

A

the size of the mean difference in terms of the standard deviation

51
Q

whar are 2 ways to increase the ES

A
  1. increase the mean difference
  2. decrease the SD
52
Q

if r (correlation analyses) =.1 , d=.2 what kind of effect size does it have

A

small

53
Q

if r (correlation analyses) =.3 , d=.5 what kind of effect size does it have and what does r^2 mean

A

medium
the effect explains 9% of the total variance between the 2 samples

54
Q

if r (correlation analyses) =.5 , d=.8 what kind of effect size does it have and what does r^2 mean

A

large
the effect explains 25% of the total variance between the two

55
Q

what does the larger the cohens D mean

A

the bigger the effect size and power increases

56
Q

what is the power of the hypothesis test

A

the probability that the statistical test will reject the null hypothesis when the treatment does not have an effect

57
Q

how do we increase our power of a hypothesis test

A

1- increase the ES (increase the difference between the groups or decrease the variability)
2. increase the a sample size
3. increase the alpha (.01 to .05)
4. use a 1-tail test

58
Q

what has more power
More difference between groups or Less difference
More variability within groups or Less variability Larger sample size or Smaller sample size
Alpha = .01 or Alpha = .05
1 tail test or 2 tail test

A

more
less
larger
.05
1

59
Q

what is used to do power before and after research

A

g power

60
Q

if u find a significant result then u had enough ___

A

power

61
Q

what are the 2 types of t est

A

independent test and dependent test

62
Q

what T test Compares two means based on independent data
 Ex/ Data from different groups of people

A

independent

63
Q

what t-test Compares two means based on related data
 Ex/ Data from the same people measured
at different times (pre-post testing)
 Ex/ Data from ‘matched’ samples or twins

A

dependent

64
Q

independent t-tests have one ____ variable and one —— variable

A

idepdnet and dependent

65
Q

skewness and kurtosis is okay is it is between what

A

+2 and -2

66
Q

do we want the shapiro wilk for testing normality to be above or below .05

A

greater bc we do not want it to be significant

67
Q

if our critical t value is 2.191 what does the t value need to be in order to be significant

A

greater then 2.191 ir smaller then -2.191

68
Q

what do we want th number for levenes test for equality of variances to be

A

larger then .05 for assumptions to be met

69
Q

what is smaller if the HOV is not met

A

DF is smaller.. less likely to reject the null

70
Q

when do we use repeated measure TTest

A
  • comparing twins or matched pairs
  • studies that measure the same participants twice (ex. pre/post)
71
Q

when will repeated measures T-test be more powerful

A

if the degress of freedom (number of participants) for the 2 t-test are about the same

72
Q

what is a independent T-test for a non parametric

A

mann - whitney U test

73
Q

what is a repeated measures T-test for a non parametric

A

wilcoxon signed ranks test

74
Q

the indepdent t test and dependent t test for non parametric are done on the ___ ___

A

mean ranking

75
Q

if you are repeatedly testing the same dataset , you are more likely to commit what type of error

A

type 1

76
Q

what is the bonferroni corrections

A

divides the alpha by # of tests u plan to run

RESEARCH (11 decks)

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