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EBP 2 > Final > Flashcards

Final Flashcards

1
Q

What are the things that exist in the center of a normal curve?

A

Mean, median and mode

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2
Q

What does an inflection point on a normal curve mark?

A

A standard deviation from the mean

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3
Q

The distributions of most continuous random variables will follow the shape of the ____

A

The distributions of most continuous random variables will follow the shape of the normal curve

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4
Q

What does the empirical rule state?

A
  • 68% of all values fall within 1 standard deviation of the mean
  • 95% of all values fall within 2 standard deviation of the mean
  • 99.7% of all values fall within 3 standard deviations of the mean
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5
Q

What are the 3 major types of central tendency?

A

Mean, median, and mode

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6
Q

____ refers to the measure used to determine the center of a distribution of data.

A

Central tendency refers to the measure used to determine the center of a distribution of data.

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7
Q

What is central tendency used for?

A

It is used to find a single score that is most representative of an entire data set

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8
Q

What is a data set with 2 modes called?

A

Bi-modal

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9
Q

A data set with more than one mode can be described as ___

A

Multi-modal

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10
Q

____is mostly used to represent the central tendency, but sometimes outliers can interfere with its usage

A

Mean is mostly used to represent the central tendency, but sometimes outliers can interfere with its usage

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11
Q

What is an outlier?

A

A value that is very different from the other data in the data set

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12
Q

What is a variable?

A

A property that can take on many values

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13
Q

What are the two kind of variables?

A

Quantitative variables and qualitative/categorical variables

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14
Q

What is a quantitative variable and what kind you do with it?

A

Variables measured numerically. With quantitative variables, can do things like add and subtract, multiply and divide, and get a meaningful result

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15
Q

_____ allow for classification based on some characteristic

A

*Qualitative/ categorical variables allow for classification based on some characteristic

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16
Q

Whta is a discrete variable?

A

A quantitative variable with a finite number of values. Ex: the amount of even numbers on a dice

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17
Q

What is a continuous variable?

A

A quantitative variable with an infinite number of values Ex: temp

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18
Q

What is an independent variable?

A

Any variable that is being manipulated

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19
Q

What is a dependent variable?

A

Any variable that is being measured

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20
Q

What are the four data types of measured variables?

A
  • Nominal
  • Ordinal
  • Interval
  • Ratio
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21
Q

_____ data (also known as qualitative/categorical data) is data that is split into categories (dichotomous)

A

Nominal data (also known as qualitative/categorical data) is data that is split into categories (dichotomous)

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22
Q

____ data is data where order matters, but distance between values does not

A

Ordinal data is data where order matters, but distance between values does not

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23
Q

____ data is where order matters, and distances between values are qual and meaningful, but there is no natural zero present

A

Interval data is where order matters, and distances between values are qual and meaningful, but there is no natural zero present

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24
Q

____ data is data where order matters, distances between values are equal and meaningful, and a natural zero is present

A

Ratio data is data where order matters, distances between values are equal and meaningful, and a natural zero is present

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25
Q

___ is best for numeric symmetrically distributed data

A

Mean is best for numeric symmetrically distributed data

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26
Q

___ is best for numeric non-symmetrically distributed data

A

Median is best for numeric non-symmetrically distributed data

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27
Q

What level of measurement is dichotomous?

A

Nominal

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28
Q

Gender is a ___ level of measurement

A

Gender is a nominal level of measurement

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29
Q

Time is a ___ level of measurement

A

Time is a ratio level of measurement

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30
Q

Age is a ___ level of measurement

A

Age is a ratio level of measurement

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31
Q

What is the simple confidence interval?

A

A range of values that we are confident contains the population parameter

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32
Q

What is point estimate?

A

A single value that represents the best estimate of the population value

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33
Q

In a confidence interval, the width concerns the ___ of the estimate

A

In a confidence interval, the width concerns the precision of the estimate

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34
Q

The point estimate is always in the ___ of the confidence interval

A

The point estimate is always in the middle of the confidence interval

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35
Q

What is the formal definition of a confidence interval?

A

If we repeated sampling an infinite number of times, 95% of the intervals would overlap the true mean

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36
Q

Not every value in a CI, is equally as ___

A

Not every value in a CI, is equally as probable

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37
Q

A more narrow confidence interval means that it is ____ precise

A

A more narrow confidence interval means that it is more precise

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38
Q

What are the factors that can narrow/increase a confidence interval?

A
  1. Larger sample size
  2. Less variance
  3. Lower selected level of
    confidence (90% vs. 95%)
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39
Q

The null hypothesis is ___. And it states that _____

A

The null hypothesis is a sampling error. And it states that the population means(not sample means) are equal so the difference seen is not real

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40
Q

The alternative hypothesis states that the difference seen, represents __.

A

The alternative hypothesis states that the difference seen, represents a real difference.

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41
Q

What is a type 1 error in hypothesis testing? What is its symbol? This is considered a liar

A

When the null hypothesis is true, and we choose to reject it.
Symbol: “Alpha”

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42
Q

What is a type 2 error in hypothesis testing? What is its symbol? This is considered to be blind

A

When the null hypothesis is false, and we do not reject it. (accept it)
Symbol: Beta

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43
Q

___ is the maximum probability of type 1 error that a researcher is willing to accept

A

Alpha is the maximum probability of type 1 error that a researcher is willing to accept

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44
Q

When does the researcher set the alpha?

A

Set before running statistics

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45
Q

What is alpha usually set to?

A

0.05. (5%)

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46
Q

What is the simple definition of a p-value?

A

The probability of type 1 error if the null hypothesis is true

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47
Q

True or false.

You can have a probability of type 1 error what the null hypothesis is false

A

False

You can NOT have a probability of type 1 error what the null hypothesis is false

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48
Q

When is the p-value calculated?

A

After research

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49
Q

What is the formal definition of a p-value?

A

Probability of observing a value more extreme than actual value observed, if the null hypothesis is true

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50
Q

If the p-value is less than or equal to alpha, we ___ the null hypothesis

A

If the p-value is less than or equal to alpha, we REJECT the null hypothesis

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51
Q

If the p-value is greater than or equal to alpha, we ___ the null hypothesis

A

If the p-value is greater than or equal to alpha, we ACCEPT the null hypothesis

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52
Q

If we “fail to reject” (accept) Ho, we attribute any

observed difference to ____ only

A

If we “fail to reject” (accept) Ho, we attribute any

observed difference to sampling error only

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53
Q

We don’t interpret non-significant differences as “__”

maybe not even as “trends”

A

• We don’t interpret non-significant differences as “real” (maybe not even as “trends”)

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54
Q

We understand that a non-significant difference is

attributable only to __.

A

We understand that a non-significant difference is

attributable only to chance.

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55
Q

How do you use confidence intervals for hypothesis testing?

A

Look at the 95% CI of the mean difference, and evaluate whether or not it includes zero

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56
Q

If the confidence interval includes 0, it is ____ in hypothesis testing

A

If the confidence interval includes 0, it is nonsignificant in hypothesis testing

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57
Q

If the confidence interval excludes 0, it is ____ in hypothesis testing

A

If the confidence interval excludes 0, it is significant in hypothesis testing

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58
Q

What is the benefit of a CI over a p-value when hypothesis testing?

A

CIs give an estimate of effect size

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59
Q

P-values and CIs tells us about ___ not ____

A

P-values and CIs tells us about statistical significance not clinical significance

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60
Q

What is statistical power?

A

The probability of finding a statistically significant difference if such a difference exists in the real world

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61
Q

What are the main things that affect the statistical power of a study?

A
  • Alpha
  • Effect size
  • Variance
  • Sample size
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62
Q

Increasing alpha will ___ power

A

Increasing alpha will increase power

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63
Q

An effect size is known as the ____

A

An effect size is known as the mean difference

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64
Q

What is standardized effect size?

A

The mean difference divided by the variance

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65
Q

__ is the spread of scores

A

Variance is the spread of scores

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66
Q

Increasing the effect size will ___the power

A

Increasing the effect size will increase the power

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67
Q

Increasing the sample size will ___the power

A

Increasing the sample size will increase the power

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68
Q

___ is the best way to increase statistical power

A

Sample size is the best way to increase statistical power

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69
Q

Increasing variance will ___ power

A

Increasing variance will decrease power

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70
Q

What are the things that will decrease power?

A
  • Decreased alpha
  • Decreased effect size
  • Increased variance
  • Decreased sample size
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71
Q

What are the two types of power analysis?

A
  • Power a priori

- Power post-hoc

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72
Q

What is power a priori?

A

A power analysis done before we collect data, to determine if the design is powerful enough

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73
Q

What is power post-hoc?

A

Power analysis done after the research is complete by the consumers to find if there was enough power/ if they failed to reject the null hypothesis

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74
Q

If a difference is found post-hoc/the null hypothesis was rejected, then the power issue is ___

A

If a difference is found post-hoc/the null hypothesis was accepted/fail to reject, then the power issue is moot/not a problem

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75
Q

If a difference not is found post-hoc/the null hypothesis was accepted/fail to reject, then the power issue is ___ and you have to do a ___

A

If a difference not is found post-hoc/the null hypothesis was accepted/fail to reject, then the power issue is huge and you have to do a post-hoc analysis

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76
Q

A priori is used to figure out how many subjects to use ___

A

A priori is used to figure out how many subjects to use before a study is started

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77
Q

What is the minimal accepted power during power a priori?

A

0.8

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78
Q

What are the 2 ways to determine a post doc analysis?

A
  1. Compute with traditional cohen approach

2. Determine with confidence interval analysis of effect size

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79
Q

What is involved in computing the post doc analysis with the traditional approach?

A 
• Continuous scale result: 0.0 – 1.0 ( > 0.8 is default)
• Based on:
   • Sample size
   • Alpha
   • Variance (observed)
   • Effect size (use MCID, not 
      observed)
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80
Q

____ is the better way to determine the post hoc analysis, while with ____, the answer will probably be the same as a priori

A

Determine with confidence interval analysis of effect size is the better way to determine the post hoc analysis, while with compute with traditional cohen approach, the answer will probably be the same as a priori

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81
Q

If the MCID is excluded from the CI, then it is definitively negative and ___ powered

A

If the MCID is excluded from the CI, then it is definitively negative and adequately powered

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82
Q

If the MCID is included from the CI, then it is not definitive and ___ powered

A

If the MCID is included from the CI, then it is not definitive and inadequately powered/ underpowered

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83
Q

A two tailed testis testing to see ____

A

A two tailed testis testing to see if your calculated value is either above or below where it is expected to be

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84
Q

A one tailed test is testing to see if ____ or ___

A

A one tailed test is testing to see if your calculated value is above where it’s expected to be or below where it is expected to be

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85
Q

___ is the assumption you’re beginning with and is opposite of what you’re testing

A

Null hypothesis(H0) is the assumption you’re beginning with and is opposite of what you’re testing

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86
Q

___ is the claim you’re testing

A

Alternating hypothesis is the claim you’re testing

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87
Q

What is a t-statistical test?

A

Statistical method to decide whether an observed difference in sample scores represents a “real” difference in the population…. vs. just sampling error

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88
Q

How many groups are in a t-test?

A

2 groups

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89
Q

2 groups is another way of saying…?

A

2 levels of 1 IV

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90
Q

What does a t-test do?

A

Finds the difference between group means divided by the variability within the groups( standard error of the mean difference)

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91
Q

The error in a standard error refers to…?

A

All sources of variability within a set of data

that cannot be explained by the independent variable.

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92
Q

A within group variability with no variability is known as being ___ ?

A

A within group variability with no variability is known as being definitely different ?

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93
Q

A within group variability with little bit of variability is known as ___ ?

A

A within group variability with little bit of variability is known as probably different

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94
Q

A within group variability with larger amounts of variability is known as ___

A

A within group variability with larger amounts of variability is known as maybe not different

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95
Q

When the variability between groups are not necessarily the same, it is called…?

A

When the variability between groups are not necessarily the same, it is called a differing variance

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96
Q

What is a parametric statistics?

A

A branch of statistics which assumes that sample data comes from a population that follows a probability distribution based on a fixed set of parameters.

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97
Q

What are the basic assumptions for all parametric test?

A
  • Samples are randomly drawn from populations
  • Population is normally distributed
  • Homogeneity of variance (roughly)
  • Data from ratio or interval (i.e. continuous) scales
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98
Q

What rarely happens, but one still needs to be careful with when samples are randomly drawn from populations?

A

Generalization

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99
Q

What are the ways to test if the population is normally distributed?

A
  • Statistically
  • Graphically
  • Common sense
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100
Q

When is the homogeneity of variance especially important?

A

With unequal group sizes

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101
Q

How is the homogeneity of variance tested?

A

Statistically

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102
Q

What statistical test is used for the t-test?

A

Levene’s test

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103
Q

What are the statistical hypotheses for the null hypothesis for a two-level design?

A
  • The two population means are equal
  • The hypothesis can be in a nondirectional format (not equal)
  • Directional format (one is greater than the other)
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104
Q

A two-tailed test uses a ___ hypothesis

A

A two-tailed test uses a nondirectional hypothesis

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105
Q

A one-tailed test uses a ___ hypothesis

A

A one-tailed test uses a directional hypothesis

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106
Q

A two tailed test has ___ statistical power compared to the one tailed test

A

A two tailed test has less statistical power compared to the one tailed test

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107
Q

What are the two types of t-test?

A
  • Independent/unpaired t-test

- Paired t-test

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108
Q

What happens in an unpaired(independent) t-test?

A

Testing to see if there is a difference between 2 groups

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109
Q

What kind of design is found in an unpaired t-test?

A
  • Pretest-posttest design (compare change scores)

- Posttest only design

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110
Q

What happens in a paired(dependent) t-test?

A

Testing to see if there is a difference between conditions in the same person

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111
Q

What kind of design is found in a paired t-test?

A
  • Difference scores or pretest-posttest

- Repeated measures design

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112
Q

A repeated measures factor is an example of a ___

A

A repeated measures factor is an example of a within-subjects factor

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113
Q

A non-repeated measures factor is an example of a ____ factor

A

A non-repeated measures factor is an example of a between-subjects factor

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114
Q

What is an ANOVA?

A

Statistical method to decide whether an observed difference in sample scores represents a “real” difference in the population…. vs. just sampling error, but with 3 or more groups/levels of 1 IV and or 2 or more IVs

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115
Q

What is the question asked in an ANOVA?

A

Are observed differences in whole set of means greater than would be expected by chance alone?

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116
Q

What statistic is looked at for ANOVA?

A

An f- statistic

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117
Q

What is an F-statistic?

A

The between group variability divided by the within group variability

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118
Q

What is the null hypothesis in the ANOVA?

A

All of the population means are even

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119
Q

What is the alternative hypothesis in the ANOVA?

A

At least one pair of samples is significantly different, but we don’t know which one

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120
Q

What are the basic assumptions for ANOVA?

A
  • Samples are randomly drawn from populations
  • Population is normally distributed
  • Homogeneity of variance (roughly)
  • Data from ratio or interval (i.e. continuous) scales
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121
Q

What does one need to be careful with when randomly drawing samples from the population?

A

Generalization

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122
Q

How can the normal distribution of a population be tested?

A
  • Statistically
  • Graphically
  • Common sense
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123
Q

When is the homogeneity of variance especially important?

A

When there is an unequal group size

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124
Q

How is the homogeneity of variance usually tested?

A

Statistically

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125
Q

The types of ANOVA concern what…?

A
  • Whether they are one way (1 IV) or multiple ways

- Whether the IV are between subjects(independent groups) or within subjects (repeated measure) or a mixed model

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126
Q

What is a mixed model?

A

Where there is 1 IV that is between subject and 1 IV that is within subjects

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127
Q

What are the types of ANOVA?

A
  • One way ANOVA: independent samples
  • Two way ANOVA: independent samples
  • One way ANOVA: Repeated measures samples
  • Two way ANOVA: Repeated measures samples
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128
Q

What is the characteristic of a one way ANOVA: independent variable?

A

1 IV with 3 or more levels

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129
Q

What does the result of an ANOVA show?

A

Whether or not there is a difference overall, but not where the difference is

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130
Q

What is the characteristic of a two way ANOVA: independent variable?

A

2 or more IV

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131
Q

What are the things you’re interested in when performing a two way ANOVA: independent variable?

A
  • Main effect of IV A
  • Main effect of IV B
  • Main effect of IV A & B (interaction effect)
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132
Q

What is the interaction effect?

A

Saying that the scores across one of the IV depends on the levels of the other IV

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133
Q

It is really helpful to look at ____ when talking about interaction effects

A

It is really helpful to look at graphs when talking about interaction effects

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134
Q

What does it mean when the lines of an interaction effect graph are parallel?

A

There is no interaction

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135
Q

What does it mean when the lines of an interaction effect graph are not parallel?

A

There is an interaction

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136
Q

What is a disordinal interaction?

A

When the lines cross and significant main effects cannot be interpreted

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137
Q

What is an ordinal interaction?

A

When the lines don’t cross and significant main effects can be interpreted

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138
Q

The one way ANOVA: Repeated measures samples is more powerful that the independent ANOVA because ___

A

The one way ANOVA: Repeated measures samples is more powerful that the independent ANOVA because it has less error variance

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139
Q

What is the homogeneity of variance in the one way ANOVA: Repeated measures samples?

A

Sphericity

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140
Q

What is sphericity?

A

The homogeneity of variance of differences

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141
Q

How is sphericity tested?

A

Test with Mauchly’s Test of Sphericity

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142
Q

What is a non-significant finding of sphericity mean?

A

No difference in variance

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143
Q

If sphericity assumption is failed, what happens?

A

Use correction/adjusted p-value

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144
Q

What is a multiple comparison test used for?

A

To determine where the difference is

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145
Q

The multiple comparison test is also called the ____

A

The multiple comparison test is also called the pairwise comparisons

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146
Q

What are the different strategies of performing a multiple comparison test?

A
  1. Post-hoc

2. Planned comparison

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147
Q

When is a post-hoc performed?

A

Performed after ANOVA

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148
Q

___ multiple comparison strategy is the most common

A

Post-hoc multiple comparison strategy is the most common

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149
Q

The post hoc test ___ and therefore are exploratory

A

The post hoc test every difference and therefore are exploratory

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150
Q

When is a planned comparison performed?

A

Performed instead of ANOVA (a priori)

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151
Q

What does a planned comparison focus on?

A

Focused only on specific comparisons

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152
Q

How do you calculate the family wise type 1 error rate that is used for the one way ANOVA?

A

Add up all the alpha values

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153
Q

When the family wise type 1 error rate is too high, what do you do?

A

A Bonferroni Correction can be done

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154
Q

How is a Bonferroni Correction done?

A

Divide alpha by the number of statistical tests to be performed and use that for each post hoc test

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155
Q

What is the downside to the Bonferroni Correction?

A

Because it has less power and a higher chance of a type 1 error, must balance risk of Type 1 and Type 2 error

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156
Q

What are the types of post hoc test to perform in the order of least conservative/most likely to find a significant difference?

A
  • Fisher’s least significant difference
  • Duncan multiple range test
  • Newman-Keuls method
  • Tukey’s honestly significance difference
  • Bonferroni t-test
  • Scheffe’s comparison
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157
Q

What are the post-hoc test that are performed the most?

A
  • Fisher’s least significant difference
  • Tukey’s honestly significance difference
  • Bonferroni t-test
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158
Q

What is the Fisher’s least significant difference test?

A

Essentially and unadjusted t-test (LSD)

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159
Q

Why is the Tukey’s honestly significance difference important?

A

“Middle of the road” in
terms of risk and most
commonly used

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160
Q

What does the Bonferroni t-test do?

A

Simply divides α by # of

comparisons

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161
Q

When is the Fisher’s least significant difference test, Tukey’s honestly significance difference important, and Bonferroni t-test suitable for use?

A

When an independent groups type test is being performed

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162
Q

What are the multiple comparison test to be used for repeated measures?

A
  • LSD
  • SIdak
  • Bonferoni correction
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163
Q

LSD is an _____

A

LSD is an unadjusted paired t-test

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164
Q

Sidak is ___

A

Sidak is adjusted, but good balance of type 1 & type 2 error protection

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165
Q

The LSD test has a high risk of ___, type 1 error meaning it is less conservative

A

The LSD test has a high risk of high, type 1 error meaning it is less conservative

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166
Q

The bonferoni correction test has a high risk of ___ error and is more conservative

A

The bonferoni correction test has a high risk of type 2 error and is more conservative

167
Q

What is an ANCOVA?

A

(Analysis of covariance) is a statistical technique that is used when you cannot control a variable through research design and sampling

168
Q

What does the ANCOVA do?

A

It statistically adjust the dependent variable based on the covariate

169
Q

ANCOVA produces ____

A

ANCOVA produces adjusted means

170
Q

ANCOVA is a combination of ___ and _____

A

ANCOVA is a combination of ANOVA and linear regression

171
Q

What are the assumptions of ANCOVA?

A
  • Usual parametric assumptions
  • Linear relationship between CoV and DV (with r>.6)
  • Homogeneity of slopes
172
Q

You can also use ANCOVA to adjust for ____ scores

A

You can also use ANCOVA to adjust for baseline scores

173
Q

When do you do a non-parametric test?

A

When the basic assumptions for a parametric test are not met

174
Q

Non- parametric statistics are based on…?

A
  • Comparisons of ranks of scores

* Comparisons of counts(yes/no) or “signs” of score

175
Q

Non- parametric statistics are ___ compared to parametric statistics

A

Non- parametric statistics are less powerful compared to parametric statistics

176
Q

What kind of parametric test do you perform when you have 2 independent groups?

A

Unpaired t-test

177
Q

What kind of parametric test do you perform when you have 2 related scores?

A

Paired t-test

178
Q

What kind of parametric test do you perform when you have 3 or more independent groups?

A

One-way analysis of variance (ANOVA) (F)

179
Q

What kind of parametric test do you perform when you have 3 or more related scores?

A

One-way repeated measures analysis of variance (MANOVA)

180
Q

What kind of non-parametric test do you perform when you have 2 independent groups?

A

Mann-Whitney U test

181
Q

What kind of non-parametric test do you perform when you have 2 related scores?

A
  • Sign test

- Wilcoxon signed ranks test (T)

182
Q

What kind of non-parametric test do you perform when you have 3 or more independent groups?

A
  • Kruskal-Wallis analysis of variance by ranks (H or x^2)
183
Q

What kind of non-parametric test do you perform when you have 3 or more related scores?

A

Friedman two way analysis of variance by ranks

184
Q

True or False

You’re able to perform a non-parametric test on complex designs like a 2 x 3

A

FALSE

Unable to perform on more complex designs (e.g. 2x3)

185
Q

What question is being asked in the comparison based on ranks in a non-parametric t-test?

A

Is the difference in ranks larger than would be expected by chance alone?

186
Q

What question is being asked in the comparison based on signs in a non-parametric t-test?

A

Is the difference in sign frequencies larger than would be expected by chance alone?

187
Q

What type of test do we use when the IV and DV are both on the nominal level?

A

Chi- Square

188
Q

What are you looking at in a chi-square?

A

Are observed frequencies different than expected frequencies

189
Q

What are the 2 types of chi square?

A
  • Goodness of fit

* Tests of independence (association)

190
Q

What do you do in the goodness of fit chi square test?

A

• Compare observed frequencies of 1 variable to uniform frequencies of another

191
Q

What is an example of the goodness of fit chi square test?

A

• Eg: flip coin 50 times. Get 15 heads & 35 tails. Is this difference due to chance or a “real” bias?

192
Q

____ chi square test is much more common?

A

Tests of independence (association)

193
Q

What do you do in the tests of independence (association) chi square test?

A

Compare observed frequencies from 1 variable to observed frequencies of another variable

194
Q

What is an example of the tests of independence (association) chi square test?

A

Eg: Is owning a mac laptop related to gender?

195
Q

What is the McNemar test?

A

Requirement of chi-square is that variable levels must be independent (e.g. can’t be “healed” and “unhealed”)

196
Q

___ is the form of a chi square test that is used for 2x2 with correlated sample

A

McNemar test* is the form of a chi square test that is used for 2x2 with correlated sample

197
Q

What is a phi coefficient?

A

A correlation coefficient for 2 nominal variables/ degrees of association for 2x2

198
Q

The phi coefficient is based off the ___

A

The phi coefficient is based off the chi-square test

199
Q

What is the IV level of measurement for a t- test?

A

Nominal

200
Q

What is the IV level of measurement for an ANOVA?

A

Nominal

201
Q

What is the IV level of measurement for a non parametric test?

A

Nominal

202
Q

What is the DV level of measurement for a t- test?

A

Continuous

203
Q

What is the DV level of measurement for an ANOVA?

A

Continuous

204
Q

What is the DV level of measurement for a non parametric test?

A

Ordinal

205
Q

What is the question asked with a t-test?

A

Difference between means?

206
Q

What is the question asked with an ANOVA?

A

Difference between means?

207
Q

What is the question asked with a non parametric test?

A

Ranks different?

208
Q

What is the IV level of measurement for a correlation?

A

Continuous

209
Q

What is the IV level of measurement for a regression?

A

Continuous

210
Q

What is the DV level of measurement for a correlation?

A

Continuous

211
Q

What is the DV level of measurement for a regression?

A

Continuous

212
Q

What is the question asked with a correlation?

A

Strength of association?

213
Q

What is the question asked with a regression?

A

Strength of prediction?

214
Q

What does a correlation have to do with?

A

A pair of scores and how much they co-vary

215
Q

What does it mean for something to co-vary?

A

Directly or inversely proportional. When one is high, so is the other and vice versa

216
Q

What are the things that a correlation looks at?

A
  • Do they vary together (covary)?
  • How strong is their linear relationship?
  • What is the nature of the relationship?
217
Q

A correlation has to be ___

A

A correlation has to be linear

218
Q

What is a correlation coefficient?

A

A number that quantifies the strength of a linear relationship that can range from -1 to 1

219
Q

What does it mean when a correlation coefficient is closer to 1, whether positive or negative?

A

Closer to |1.00|, higher strength of relationship

220
Q

What does the sign of the correlation coefficient indicate?

A

The direction

221
Q

The tighter the grouping of the linear relationship, the ___ the correlation coefficient

A

The tighter the grouping of the linear relationship, the higher the correlation coefficient

222
Q

What does a 0.00- 0.25 coefficient correlation mean?

A

Little or no relationship

223
Q

What does a 0.26- 0.50 coefficient correlation mean?

A

Fair relationship

224
Q

What does a 0.51- 0.75 coefficient correlation mean?

A

Moderate to good

225
Q

What does a 0.75- 1.00 coefficient correlation mean?

A

Good to excellent

226
Q

What is the coefficient of determination?

A

The square of the correlation coefficient

227
Q

What is the coefficient of determination equal to?

A

The percent of variance in one variable that is explained (or accounted for) by the other variable

228
Q

What is the significance of the coefficient correlation?

A

To test the null hypothesis

229
Q

What is the null hypothesis as it relates to the coefficient correlation?

A

The correlation between variable x and variable y is not significantly different from zero.

230
Q

Coefficient correlation is very sensitive to ___

A

Coefficient correlation is very sensitive to * sample size*

231
Q

What is the most common type of correlation coefficient?

A

Pearson Product-Moment Correlation Coefficient (r)

232
Q

When is the Pearson Product-Moment Correlation Coefficient applicable?

A

When both variables continuous (Interval or Ratio scale)

233
Q

What is the Spearman Rank (rho) Correlation Coefficient (rs)?

A

Non-parametric analog of Pearson r

234
Q

When is the Spearman Rank (rho) Correlation Coefficient (rs) applicable?

A

When 1 continuous, 1 ordinal variable or 2 ordinal variables

235
Q

When do you use a Point Biserial Correlation (rpb)?

A

When one variable is dichotomous, and the other variable continuous (interval or ratio)

236
Q

When does a Point Biserial Correlation (rpb) not work?

A

dichotomous nominal (e.g Age & Race)

237
Q

Computationally, a Point Biserial Correlation (rpb) is the same as a ___

A

Computationally, a Point Biserial Correlation (rpb) is the same as a Pearson’s r

238
Q

The results of a Point Biserial Correlation (rpb) is the same as ___

A

The results of a Point Biserial Correlation (rpb) is the same as a t-test

239
Q

When do you use a Rank Biserial Correlation (rrb)?

A

When one variable is dichotomous (nominal), and the other variable is ordinal

240
Q

A Rank Biserial Correlation (rrb) is computationally about the same as ___

A

A Rank Biserial Correlation (rrb) is computationally about the same as Spearman Rank

241
Q

When do you use a Phi coefficient (Φ)?

A

When both variables dichotomous

242
Q

A Phi coefficient (Φ) is computationally same as ___ (special case)

A

A Phi coefficient (Φ) is computationally same as Pearson’s r (special case)

243
Q

A scatterplot is ___ with a Phi coefficient (Φ)

A

A scatterplot is worthless with a Phi coefficient (Φ)

244
Q

Can a Phi coefficient (Φ) work with a non- dichotomous nominal?

A

NO

245
Q

A Phi coefficient (Φ) is similar to a ____, but unlike it, a Phi coefficient (Φ) gives gives strength of relationship, while the ___ only gives statistical significance

A

A Phi coefficient (Φ) is similar to a chi square test, but unlike it, a Phi coefficient (Φ) gives gives strength of relationship, while the chi-square test only gives statistical significance

246
Q

A correlation does not tell you ___

A

Does NOT assess differences or agreement

247
Q

How can an extreme outlier affect the interpretation of a correlation?

A

Can create inflated correlation with only a few extreme data points

248
Q

Can a correlation data be generalized beyond the range of scores in the sample?

A

Can’t generalize beyond range of scores in sample

249
Q

Low correlation may be due to ___ range

A

Low correlation may be due to limited range

250
Q

What is reliability?

A

Extent to which a measurement is consistent and free from error

251
Q

What can a reliable measurement be expected to do?

A

A reliable measure can be expected to repeat the same score on two different occasions provided that the characteristic of interest does not change

252
Q

Reliability is closely tied to the concept of ___

A

Reliability is closely tied to the concept of measurement error

253
Q

What are the continuous data reliability coefficients?

A
  • Pearson correlation (r)

* Intraclass correlation coefficient (ICC) (best)

254
Q

What are the discrete/ categorical data reliability coefficients?

A
  • Percent agreement

* Kappa (best)

255
Q

What are the problems with using a Pearson correlation (r) to quantify reliability?

A
  1. Assesses relationship, not agreement

2. Only two raters or occasions could be compared

256
Q

Why do we prefer to use ICCs and Kappa for quantifying reliability?

A

Both ICCs and kappa give single indicators of reliability that capture strength of relationship plus agreement in a single value

257
Q

____ is stated in terms of variance

A

Reliability coefficients is stated in terms of variance

258
Q

What is the range of a reliability coefficient and what does it mean?

A

Range 0-1

0 = no reliability, 1 = perfect reliability

259
Q

The more error variability you have, the ____ reliability coefficient will be

A

The more error variability you have, the lower your reliability coefficient will be

260
Q

Reliability coefficient will be bigger, when ___ is larger

A

Reliability coefficient will be bigger, when true variance is larger

261
Q

What is the equation for the reliability/ correlation coefficient?

A

True score variability divided by true score variability plus error variability

262
Q

What does a high error variability do to correlation coefficient?

A

It will reduce it

263
Q

What will not having enough true score variability do to correlation coefficient?

A

It will reduce it

264
Q

What will happens to correlation coefficient with a large true variance?

A

It will be bigger

265
Q

What are the things that an ICC measures?

A

Measures degree of relationship (association) and

agreement simultaneously

266
Q

ICCs give ____ estimate of reliability (can compare different things)

A

ICCs give standardized estimate of reliability

267
Q

ICC is often reported in conjunction with ____

A

ICC is often reported in conjunction with * Standard error of the measurement (SEM)*

268
Q

ICC is designed for____ data but can be used with ___ data

A

ICC is designed for interval/ ratio data but can be used with ordinal data

269
Q

When can can ICC be used with ordinal data?

A

If intervals “assumed” to be equivalent

270
Q

SEM gives ____ estimate of reliability (i.e. in units

of measurement)

A

SEM gives “unstandardized” estimate of reliability (i.e. in units of measurement)

271
Q

The 6 types of ICC dependent on ….?

A
  • Purpose of study
  • Design of study
  • Type of measurements taken
272
Q

ICC type defined by ___

A

ICC type defined by two numbers in parentheses

273
Q

What does each number in the parenthesis of an ICC type mean?

A

The first number is the model and the second number is the form. (2, 6) 2 = model, 6 = form

274
Q

How many models of ICC are there?

A

3

275
Q

What is model 1 of an ICC?

A
  • Each subject measured by a different set of raters; raters “randomly” chosen
  • Rarely used in clinical research
276
Q

What is model 2 of an ICC?

A

Each subject measured by same raters; raters “randomly” chosen & representative of rater population; results generalizable

277
Q

What is ICC model 2 commonly used for?

A

Most common for inter-rater reliability or test-retest reliability

278
Q

What is model 3 of an ICC?

A

Each subject measured by same rater(s); raters are only ones of interest; results not generalizable

279
Q

What is ICC model 3 commonly used for?

A

Most common for intra-rater reliability

280
Q

Rank the models of ICC in order from most conservative to least conservative

A
  • Model 1 (most conservative, lowest number)
  • Model 2 (neutral)
  • Model 3 (least conservative, highest number)
281
Q

When can a model ICC be used for inter rater reliability?

A

Can be for inter-rater reliability if study raters only ones of interest

282
Q

What does the form/ 2nd number in parenthesis of an ICC represent?

A

Second number in parentheses represents number of observations used to obtain reliability estimate

283
Q

When is form = 1?

A

If only one observation per subject per rater (or rating)

284
Q

When is form a number more than 1?

A

If multiple observations averaged to get single number for analysis, form = number of observations averaged

285
Q

What ICC is best for clinical measures?

A

ICC > 0.90

286
Q

What ICC has good reliability?

A

ICC > 0.75

287
Q

What ICC has poor to moderate reliability?

A

ICC < 0.75

288
Q

The interpretation of an ICC depends on ____

A

The interpretation of an ICC depends on intended use

289
Q

ICC estimate based on ____ will always be substantially higher than estimate based on ____

A

ICC estimate based on average measures will always be substantially higher
than estimate based on single measure

290
Q

What are the characteristics of reliability for categorical scales?

A
  • Based on frequency table
  • Agreements on on diagonal
  • Disagreements are all others
291
Q

What is percent agreement?

A

How often the raters agree

292
Q

How do you calculate percent agreement?

A

Divide number of agreements by total of all possible agreements

293
Q

What is the problem with a percent agreement?

A
  • Does not account for agreement due to chance

* Tends to overestimate reliability

294
Q

What is the kappa coefficient?

A

Proportion of agreement

between raters after chance agreement has been removed

295
Q

On what kind of data is a kappa coefficient used?

A

Can be used on both nominal and ordinal data

296
Q

What does a weighted kappa do?

A

Can choose to make “penalty” worse for larger disagreements

297
Q

What can the weight of a weighted kappa be?

A

Weights can be arbitrary, and

symmetric or asymmetric

298
Q

A weighted kappa is best for what kind of data?

A

Best for ordinal data

299
Q

The kappa interpretation depends on ____

A

The kappa interpretation depends on the weights used

300
Q

What does a kappa value of <0.4 mean?

A

Poor to Fair agreement beyond chance

301
Q

What does a kappa value of 0.4–0.6 mean?

A

Moderate agreement beyond chance

302
Q

What does a kappa value of 0.6–0.8 mean?

A

Substantial agreement beyond chance

303
Q

What does a kappa value of 0.8–1.0 mean?

A

Excellent agreement beyond chance

304
Q

Internal consistency is often used to do what?

A

Often used to construct and evaluate scale / questionnaires

305
Q

What does internal consistency estimate?

A

Estimate how well the items that reflect the same construct yield similar results. So, do different questions measure same concept or indicator?

306
Q

What does cronbach’s alpha (a) do?

A

Represents correlation among items and correlation of each individual item with the total score

307
Q

What is recommended that cronbach’s alpha be between?

A

Recommended that cronbach’s alpha be between 0.70 to 0.90

308
Q

Cronbach’s alpha can have ___ or ____ on test/questionnaire

A

Cronbach’s alpha can have dichotomous or multiple-choice responses on test/questionnaire

309
Q

What can cronbach’s alpha (a) help eliminate?

A

Can help eliminate items from test/questionnaire that are not homogenous to the set or are not contributing unique information

310
Q

What is response stability?

A

A way to quantify stability of repeated measures over time

311
Q

Response stability is basically the same as ___

A

Response stability is basically the same as test-retest reliability

312
Q

What are the different ways to test response stability?

A
  • SEM: standard error of the measurement
  • MDC: minimal detectable difference/change
  • CV: coefficient of variation
313
Q

Standard error of measurement is a ___ measure of reliability, while ICC and kappa is a ____ measure of reliability

A

Standard error of measurement is a absolute measure of reliability, while ICC and kappa is a relative measure of reliability

314
Q

SEM is in units of _____

A

SEM is in units of measurement as variable

315
Q

What is SEM theoretically?

A

Standard deviation of the distribution of theoretical multiple measurements

316
Q

An SEM can be used to create a ____

A

An SEM can be used to create a 95% CI around a measurement

317
Q

What is the MDC?

A

Amount of change in a variable that must be achieved to reflect a true change/difference

318
Q

___ is a mathematical multiple of SEM

A

MDC is a mathematical multiple of SEM

319
Q

What is the coefficient of variation (CV)?

A

A standardized way to measure variability. (SD divided by the mean times 100)

320
Q

What is the coefficient of variation helpful in comparing and why?

A

Unit-less, so is helpful comparing variability between two distributions on different scales

321
Q

What is an alternate form reliability?

A

Comparing different methods of testing same phenomenon with different instruments (goniometer vs inclinometer)

322
Q

What analysis or agreement is seen with an alternate form reliability?

A
  • Limit of agreement

- Bland- altman analysis

323
Q

What is a bland- altman plot?

A

When you plot the mean of two measures on the x- axis and the difference between the 2 measures on the y- axis, and the center of the plots is a bias

324
Q

What does a tighter range on the bland altman plot mean?

A

There is more agreement between the two measures

325
Q

When is there no bias on a bland altman plot?

A

When the line of bias is at 0

326
Q

When is there a consistent bias on a bland altman plot?

A

When the points on the plot are on one side of the bias line

327
Q

When is there an asymmetrical bias on a bland altman plot?

A

When the points are split between the two sides of the bias line

328
Q

What is epidemiology?

A

A study aimed at studying determinants of disease, injury or dysfunction in populations

329
Q

Epidemiology is another way of saying ____

A

Epidemiology is another way of saying risk

330
Q

Risk in PT can be expressed in terms of _____

A

• Experiencing an adverse outcome
• Patients not improving with treatment
• Requiring more invasive or expensive subsequent
interventions in spite of treatment

331
Q

Epidemiology generally uses observational designs with ___ variables

A

Epidemiology generally uses observational designs with dichotomous variables

332
Q

What studies are intended to study risk factors?

A

Case-Control & Cohort Studies

333
Q

Case-Control & Cohort Studies looks at the ____ between disease & exposure

A

Case-Control & Cohort Studies looks at the association (“cause”) between disease &
exposure

334
Q

The IV and DV in case-control & cohort studies are what kind of variables?

A

Dichotomous

335
Q

In case-control & cohort studies, there is ___ strength in thinking something is causal of the other

A

In case-control & cohort studies, there is less strength in thinking something is causal of the other

336
Q

How are subjects in a cohort study selected?

A

Subjects selected based on

exposure or not

337
Q

Is a cohort study usually prospective or retrospective?

A

Usually prospective, but

can be prospective or retrospective

338
Q

Does a cohort study work for rare conditions?

A

Doesn’t work well for very

rare conditions

339
Q

What does a cohort study examine?

A

Examine if there is a different

incidence of disease

340
Q

How are subjects in a case control study selected?

A

Subjects selected based on
whether or not they have
disorder

341
Q

Where should the controls of a case control be selected from?

A

Controls should be selected

from same population as Cases

342
Q

What does a case-control study examine?

A

Examine if exposure is different between cases and control

343
Q

What condition does a case control work especially well for?

A

Works especially well for very

rare conditions

344
Q

What are the primary ways to quantify risk?

A
  • Relative Risk (RR)

* Odds Ratios (OR)

345
Q

What do the primary ways to quantify risk actually quantify?

A

Both quantify strength of association between “exposure” and “disease”

346
Q

In what study is RR used and in what study is OR used?

A
  • RR in Cohort studies

* OR in Case-control studies

347
Q

What does it mean when an RR or OR = 1 ?

A
  • = “null value”

* No association between an exposure and a disease

348
Q

What does it mean when an RR or OR > 1?

A
  • A positive association between an exposure and a disease

* The exposure is considered to be harmful

349
Q

What does it mean when an RR or OR < 1?

A
  • A negative association between an exposure and a disease

* The exposure is protective

350
Q

RR is the ratio of ___ compared to ____

A

Incidence of disease among
exposed individuals compared to Incidence of disease among
unexposed individuals

351
Q

Since OR is selected based on whether they have disease or not, so can’t determine rate of ___

A

Since OR is selected based on whether they have disease or not, so can’t determine rate of “incidence”

352
Q

OR is the ratio of ___ compared to ____

A

Odds of exposure among cases (with disease) compared to Odds of exposure among controls (w/o disease)

353
Q

The computation of OR is kinda like ___

A

The computation of OR is kinda like kappa

354
Q

____ uses relationships (correlation) as a basis for prediction

A

Regression uses relationships (correlation) as a basis for prediction

355
Q

What are the characteristics of a linear regression?

A 
X and Y are correlated
• X = independent variable (= predictor variable)
• Y = dependent (or criterion) variable
• We use X to predict Y
    • The value of Y depends 
       on X
    • (Thats why Y is called the 
        dependent variable)
356
Q

What is the error from line/ residual in a regression line?

A

The distance between each data point and the line of best fit

357
Q

Residuals are squared to eliminate ___ and penalize for ___

A

Residuals are squared to eliminate sign and penalize for worse errors

358
Q

What is the line of best fit?

A

Line with least squared errors

359
Q

Is regression a parametric or non parametric statistic?

A

Parametric

360
Q

What are the assumptions of a linear regression analysis?

A
  1. Linear relationship = approximation of true line in population
  2. For every X there is a normal distribution of Y
    • Sample data include random samplings from these distributions on Y
  3. Homogeneity of variance
361
Q

What is a way to test the assumptions of a linear regression?

A

Analysis of residuals by:

Plot Residuals on Y-axis, vs predicted values on x-axis

362
Q

What assumption of linear regression does the analysis of residuals test the most?

A

Homogeneity of variance

363
Q

What are you looking for in the analysis of residuals to test linear regression assumptions (assumptions are met)?

A

Looking for the residual’s distance between the predictive value and the actual value be symmetric and consistent throughout

364
Q

What does the analysis of residuals graph look like when the assumptions of linear regression are not met?

A
  • The graph starts to get wider the further it goes(data is further away from the line, the higher you go)
  • Data is not symmetric
365
Q

What happens if the linear regressions assumptions are not met?

A

Use a non linear regression

366
Q

What are the thing that helps a researcher determine whether to retain or discard a data with an outlier?

A

• Due to peculiar circumstances?
• Can discard if error identified
• Generally not justified on statistical grounds
alone

367
Q

What are the peculiar circumstances that have to be taken into consideration when determining whether to retain or discard a data?

A
  • Measurement error
  • Recording error
  • Equipment malfunction
  • Miscalculation
  • Aberrant subject (should have been excluded)
368
Q

What are the things that looks a the accuracy of prediction of the regression equation?

A

• Correlation coefficient (R)
Coefficient of determination (R2)
• ANOVA of Regression

369
Q

What are the characteristics of a correlation coefficient as it relates to the accuracy of prediction?

A
  • Rough indicator of goodness of fit for regression line

* Same as correlation coefficient (r)

370
Q

What does the coefficient of determination represent?

A

Proportion of variance in Y scores that can be explained by X scores

371
Q

What does the ANOVA of regression test?

A

Tests hypothesis that predictive relationship occurred by chance (Ho: b = 0)

372
Q

What does it mean when b=0 in an ANOVA of regression?

A

If b (slope) = 0, line is horizontal = no relationship

373
Q

What happens when p< than alpha in an ANOVA of regression?

A

If p < than alpha, reject the null and conclude the predictive relationship is
significant

374
Q

How many predictors are in a simple linear regression model and how many are in a multiple linear regression model?

A

There is only 1 predictor in a simple model and there are multiple predictors in a multiple linear regression model

375
Q

What are the assumptions of a multiple linear regression analysis?

A
  1. Linear relationship = approximation of true line in population
  2. For every X there is a normal distribution of Y
    • Sample data include random samplings from these distributions on Y
  3. Homogeneity of variance
  4. DV = continuous measure
376
Q

Coefficient of determination is the square of ____

A

Coefficient of determination is the square of correlation coefficient

377
Q

What is an adjusted R squared and what do you get punished for?

A

Chance corrected R2, get punished for having more predictor variables

378
Q

What is the goal of a linear regression?

A

The more you can predict with fewer variables, the better

379
Q

What is a regression coefficient?

A
  • The value/slope in the linear equation

* The rate of change in Y for each unit change of X

380
Q

What is a standardized beta weight helpful for?

A

Helpful to know relative contribution of each predictor

variable

381
Q

Which will always be higher or the same, out of an R square or an adjusted R square?

A

The R square will always be higher than or equal to the adjusted R square

382
Q

What is multicolinearity?

A

When the Xs in the model are substantially correlated with each other

383
Q

What does multicolinearity create a problem with?

A

Creates problems with interpretations of b weights

384
Q

What is the risk of the force entry of all possible predictors in a multiple regression method?

A
  • Risk of multicolinearity (correlation between predictors)
  • Risk of retaining non-contributing predictors
  • Risk of more predictors than justified by sample size
385
Q

How is the criteria in a stepwise procedure set?

A

Criteria set to retain or reject predictors

386
Q

Which predictor is entered first in a stepwise procedure?

A

Predictor with highest partial correlation entered first

387
Q

What does a stepwise procedure result in?

A

Should result in model with greatest parsimony and

least multicolinearity

388
Q

What is a parsimony model?

A

A model that is the most predictive, with the least amount of variables

389
Q

What is a simple correlation?

A

The overlap between 2 variables

390
Q

What is a partial correlation?

A

The unique correlation between 2 variables

391
Q

What is a forward stepwise regression method?

A

A method that starts with no predictors, then adds them, starting with the strongest

392
Q

What is a backward stepwise regression method?

A

A method that starts with all predictors, then removes them, starting with the weakest

393
Q

What is a stepwise stepwise regression method?

A

A method that starts with no predictors, then add,

but can also remove

394
Q

What is the level of measurement for predictors/ IV in a stepwise multiple linear regression model?

A
  • Most predictors are continuous scales
  • Can also use dichotomous or ordinal scale predictors
  • But not multicategory nominal (e.g. race)
395
Q

A large number of predictors is needed in a stepwise multiple linear regression hence it requires ___

A

A large number of predictors in a regression requires a very large sample size

396
Q

What is the rule of thumb for the predictors of a stepwise multiple linear regression model?

A

At least 10-15 subjects per predictor in model

397
Q

What happens if there are too many or too few predictors in a stepwise multiple linear regression model?

A

Become susceptible to “model overfit” (chance associations, i.e. type 1 error).

398
Q

What is a logistic regression?

A

When you are trying to predict a dichotomous variable

399
Q

What is the DV level of measurement of a logistic regression?

A

Dichotomous

400
Q

What is the predictor/ IV level of measurement of a logistic regression?

A

Continuous, ordinal, or dichotomous

401
Q

What are the pros MANOVA?

A

• MANOVA gets around multiplicity problem (familywise alpha:
increased Type I error risk)
• MANOVA can be more powerful if DVs related

402
Q

What are the cons MANOVA?

A

• “Combo DV” is not directly interpretable
• If statistically significant, then must follow up with post-hoc
ANOVAs

403
Q

What is a factor analysis?

A

Method of simplifying & organizing large sets of variable into fewer abstract components

404
Q

What is a path analysis?

A

Visual modeling of both direct & indirect relationships

405
Q

Path analysis is an extension of ____

A

Path analysis is an extension of multiple regression

406
Q

Compared to a multiple regression, a path analysis is more __ and ____

A

Compared to a multiple regression, a path analysis is more flexible and comprehensive

407
Q

What can a path analysis analyze?

A

Can analyze both direct and indirect relationships between 1 or more exogenous variables (IVs) and 1 or more endogenous variables (DVs)

408
Q

What is a hierarchical linear modeling also known as?

A
  • Multilevel linear modeling

* Linear mixed modeling

409
Q

A hierarchical linear modeling comes from what type of analysis?

A

The type of analysis where you have some variables nested within other variables (students nested in a classroom when studying schools)

410
Q

A hierarchical linear modeling, has far fewer __ and is highly ___

A

A hierarchical linear modeling, has far fewer assumption and
highly flexible

411
Q

What is the Number Needed to Treat (NNT)?

A

How many patients you have to provide treatment to in order to prevent one bad outcome

412
Q

What is Control Event Rate (CER)?

A

Percent of patients in control group with bad outcome

413
Q

What is Experimental Event Rate (EER)?

A

Percent of patients in experimental group with bad outcome

414
Q

What is the equation for RR?

A

EER/CER

EBP 2 (7 decks)

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