exam 2 Flashcards

1
Q

what is research

A

the systematic investigation into and study of materials and sources to establish facts and reach conclusions

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2
Q

what is key to research

A

carefully formulating a research question/falsifiable hypothesis

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3
Q

what are the types of research

A
  • observational
  • experimental
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4
Q

observational research

A
  • measuring of relationships betwen events or conditions
  • no manipulation or intervening
  • supports future experimental work
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5
Q

what is the con of observational research

A

hard to make confidenct cause-and-effect inferences

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6
Q

expiremental research

A

investigator directly manipulates conditions to measure the response

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7
Q

what are the different types of variables

A

-independent variable
- dependent variable
- intervening/confounding variable

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8
Q

independent variable

A

controlled by the investigator, or the uncontrolled cause

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9
Q

other ways to refer to IV

A

treatment, predictor

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10
Q

dependent variable

A
  • not controlled
  • the effect
  • the variable that is measured in response to the IV
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11
Q

other ways to refer to DV

A

response, criterion

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12
Q

intervening/confounding variables

A

influence the DV as well but are not controlled by the investigator

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13
Q

what can intervening/confounding variables lead to

A

erroneous conclusions

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14
Q

what is the goal of sampling

A

to extract a sample (n) that is representative of the population (N)

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15
Q

what does having a representative same do

A

the effect of an IV on a DV can be generalized to all other members of that population

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16
Q

what are the types of sampling methods

A
  • random sample
  • stratified sample
  • conenience sample
  • systemic sample
  • cluster sample
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17
Q

random sample

A

each member of the population has an equal chance of being selected

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18
Q

stratified sample

A

ensure representation of subgroups within the population of interest

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19
Q

convinence sample

A

members are selected based on “ease and proximity”

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20
Q

systemic sample

A

members are selected at regular intervals from a randomly ordered list

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21
Q

cluster sample

A

populations are divided into subgroups or “clusters” then members are randomly selected from a cluster

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22
Q

what is the ideal sampling method how come it isnt used

A

random sample, but its difficult for this to occur

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23
Q

what is the sampling method that is most suseptable to bias

A

convinence sampling

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24
Q

bias

A

aspects of the sample make it unrepresentative to the population

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25
Q

how can bias be decreased

A

having a larger sample = gerater proportion of the population = less bias

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26
Q

what are the factors that go into determining which sampling method is appropriate

A

the research objectives, resources/cost/times, and population characteristics

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27
Q

what are the type of experimental designs

A
  • pre-experimental
  • quasi- experimental
  • true experimental
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28
Q

Pre- experimental design

A
  • exploratory
  • used when rigorous approaches are not feasible
  • weak evidence of causality
  • no control group or random assignment
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29
Q

quasi experimental

A
  • moderate evidence of causality
  • no random assignment
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30
Q

true experimental

A
  • random assignment of participants to treatment or control groups
  • strong evidence of causality
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31
Q

how many treatment groups can a participant be in

A

multiple

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32
Q

how many control groups can a participant be in

A

one

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33
Q

examples of pre-experimental

A
  • case study
  • pretest-posttest
  • static group comparison
  • nonequivalent groups
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34
Q

examples of quasi-experimental

A
  • interupted time series
  • natural experiment
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35
Q

examples of true experimental

A
  • independent groups
  • matched groups
  • randomized controlled trial
  • repeated measures
  • factoral
  • pretest-posttest
  • solomon four-group
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36
Q

how would a pre-experimental case study group be set up

A

single group is exposed to an intervention/treatment and the outcome is measured

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37
Q

what are the cons of case study pre-experimental study

A

theres no way of knowing whether other factors contributed to the outcome

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38
Q

how is a one group pretest-posttest pre-experimental study set up

A

a single group is measured before and after an intervention/treatment

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39
Q

how is a static group comparison pre-experimental study set up

A
  • one of two groups recieves the intervention/treatment
  • includes a control group
  • no randomization
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40
Q

what influences a static group comparison stuyd

A

pre-existing differences between groups may influence the outcomes

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41
Q

how is an interupted time series quasi-experiment set up

A

multiple measurements taken before and after intervention/treatment

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42
Q

how is a natural quasi-experiment run?

A

observation of effects of natural occurances/changes

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43
Q

how does a natural experiment influence external validity

A

non labratory based setting and in the natural environment can be used to study how the real world operates and generalize findings

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44
Q

how is an independent group, betwen subject, true experiment run

A
  • random assignment to study groups
  • can have more than two groups
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45
Q

what does random assignment to study groups for independent group, between-subjects, tru experimental studies do

A

minimizes the effect of individual differences

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46
Q

can individual differences still affect the results of an inependent group, between subjects, true experimental study

A

yes

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47
Q

how are matched groups, true experimental study run

A
  • participants matched on key attributes then randomly assigned to groups
  • helps to minimize individual differences further
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48
Q

what is a matched group true experimental group useful for

A

when specific attributes are expected to interact with the IV

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49
Q

how are randomized control trial true experimental studies run

A
  • participants are randomly assigned to treatment or control group
  • uses blinding to reduce bias
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50
Q

what is the gold standard for clinical research

A

Randomized controlled trials

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51
Q

single blind

A

particicpants unaware of grouping

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52
Q

double blind

A

participants and researchers unaware of grouping

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53
Q

how are repeated measures, within subjects, true experimental studies run

A

participants complete all conditions
- the participants are their own controls
- random/counterbalanced order to minmize carryover effects
- smaller sample sizes needed comapted to equiavalent independent/matched groups design

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54
Q

what are repeated measures, within subjects, true experimental studies sensituve to

A

the effects of the IV

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55
Q

how are factorial, true experimental studies run

A
  • examines the effects of multiple IVs on a single DV
  • can be incorporated into between, within subjects design
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56
Q

how are solomon four-group experiments designed

A
  • separate treatment and control groups may or may not be “pretested”
  • controls for carryover effects from the pretest, improved internal validitiy
  • requires a larger sample size and randomization into each group
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57
Q

criterion of parameters

A

source: population
calculated: no
constants: yes
examples: mean, standard deviation, population

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58
Q

criterion of statistics

A

source: sample
calculated: yes
constants: no
examples: mean, SD, n

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59
Q

statistical inference

A

estimating population parameters from sample statistics

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60
Q

sampling error

A

amount of error in th estimate of a population paramter that is derived from a sample statistic

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61
Q

why are probability statements accompanied with statistics

A

because of the uncertainty in our parameter estimate

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62
Q

law of large numbers

A

as a sample size increases, the sample mean approaches the population mean if 1. samplesa are independenct 2. samples are identically distributed

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63
Q

what are easily swayed by extreme values according to the law of large numbers

A

means of small random samples = larger sampling error

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64
Q

what are resistant to extrememe values according to the law of large numbers

A

means of large random samples = smaller sampling error

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65
Q

sampling distribution of the mean

A

theoretical frequency distribution of all possible sample means that can be calculated from a population

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66
Q

according to the sampling distribution of the mean what is the relationship between variability of the sampling distribution and each sample mean

A

the variability of the sampling distribution decreases as sample size of each sample mean increases

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67
Q

according to the sampling distribution of the mean what is the relationship between the variability of sampling distribution and the variability of the population

A

the variability of sampling distribution is smaller than the variability of the population

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68
Q

standard error of the mean

A

how much the sample mean (statistics) is likely to differ from the true population mean (parameter)

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69
Q

what is the standard error of the mean also known as

A

the standard deviation of the sampling distribution of the mean

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70
Q

what is the equation for SEm

A

SD/sqrtn

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71
Q

when will samples have smaller SEm

A
  • they are homogenous
  • they have a larger sample size
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72
Q

what is the square root law

A

the accuracy of a parameter estimate is inversely proportional to the square root of the sample size

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73
Q

how will quadrupuling the sample size affect the SEm if everything else is normla

A

it willl half the SEm (half the variablity) according to the square root law

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74
Q

how do you interpret SEm

A

just like SD on a normal curve
- e.g. SEm = Z score of +/- 1.0

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75
Q

how do you state standard error of mean

A

there is a 68% chance the population mean is within 163.5 <= mu <= 182.5 lbs

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76
Q

what does the 68.5% chance the population mean is within a given interval mean

A

that this is also the confidence interval of 68%

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77
Q

what does stating a confidence interval of 68% mean

A

that there is also a 32% probability of error, or a chance that the mean is not within that range
- p = 0.32

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78
Q

what is an acceptable level of uncertainty

A
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79
Q

what is Alpha (a)

A

the area under the curve that represents the probability of error, the liklihood of chance ocurrence

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80
Q

what does an alpha value of 0.05 mean

A

that there is 5% chance of rejecting the null hypothesis incorrectly

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81
Q

what is the equation for finding the confidence interval

A

C.I. = Z-score mean +/- Z-score * SEm

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82
Q

how to report a C.I.

A

A 95% CI will give the mean +/- 1.96(SEm)
- the 1.96 is the interval where 95% of the data is found

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83
Q

how to interpret a confidence interval of 173 +/- 18.62 lbs or 154.38 lbs <= mu <= 191.62

A

with 95% confidence we conclude that the mean weight of all college-ages men is between 154.38 and 191.62 lbs. However, there is a 5% chance (p = 0.05) that the true mean falls outside of this range

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84
Q

what does a larger confidence interval result in

A
  • less likely to be wrong
  • less precise
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85
Q

how does statistical hypothesis testing begin

A

with two mutually exclusive, exhaustive mathematical statements about the relationship between variables/groups are formed

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86
Q

what are the two hypothesese formed for statistical hypothesis testing

A
  • null hypotheses (H0) (this is assumed to be true unless evidence is found to the contrary)
  • alternative hypothesis
87
Q

what does mutual exclusive hypothesis means

A

only one can be true

88
Q

what does exhaustive mean in terms of hypothesis testing

A

that no other option exists

89
Q

nondirectional hypothesis

A

H0: mean 1 = mean 2
H1L mean 1 does not equal mean 2

90
Q

directional hypothesis

A

H0: mean 1 < mean 2
H1: mean 1 > mean 2

91
Q

what does a p value indicate in statistical hypothesis testing

A

indicates the probabilituy of obtaining the data collected IF the null hypothesis H0 is true

92
Q

what does a p value < 0.05 indicate

A

that the result is statistically significant and the H0 can be rejected and you accept the alternative hypothesis

93
Q

what does rejecting a H0 indicate

A

depending on what the H0 is, it would be indicating that there is a difference bettwen the two variables or that there the treatment group is significant

94
Q

two tailed test hypotheses

A

H0: mean 1 = mean 2
HA: mean 1doesnt = mean 2

95
Q

region of rejection for two tailed tests

A
  • set by alpha value
  • split between tails of the distribution (each 2.5% AUC)
96
Q

when do you use a two tailed test

A

when prior research/logical reasoning does not suggest a direction or different, a difference should be expected

97
Q

one tailed test hypotheses

A

H0: category 1 > category 2
HA: category 1 </= category 2

98
Q

region of rejection

A
  • set by alpha value
  • concentrated at one tail of the distribution
99
Q

when to use a one tailed test

A

when there is a strong evidence to think a difference exists

100
Q

Type I error

A

H0 is rejected when it is actually true (a false positive)

101
Q

what is confluded froom a type I error

A

conclude that an effect/relationship exists when, in reality, if does not

102
Q

how can type I error risk be reduced

A

by decreasing alpha

103
Q

Type II error

A

H0 is accepted when it is actually false (falsse negative)

104
Q

what does a Type II error conclude

A

that no effect/relationshiup exists when it really does

105
Q

how can Type II error risk be reduced

A

through decreasing beta

106
Q

what is beta

A

the probability of committing a Type II error (typically strive for beta=0.2)

107
Q

statistical power

A

probability of rejecting H0 when H0 is false

108
Q

what is the equation for statistical power

A

power = 1 - beta
- typically strive for 0.8

109
Q

factors that may tie into Type I error

A
  • measurement error
  • lack of random sample
  • alpha value too liberal (a=0.10)
  • investigator bias
  • improper use of one tailed test
110
Q

factors that tie into Type II

A
  • measurement error
  • lack of sufficient power (N too small)
  • alpha value too conservative ( a = 0.01)
  • treatment effect not properly applied
111
Q

how to decrease a

A
  • decrease a priori significance level a (a bonferonni correction)
  • control confounding variables
  • increase sample size
112
Q

what does decreasing the signifcance level of alpha do

A

you will increase the chance of a Type II error

113
Q

what is a bonferoni correction

A

correction to the alpha value dividing 0.05/# of tests

114
Q

how do you decrease beta

A

increase a priori significance level alpha

115
Q

what does increasing significance level alpha result in

A

may increase the chance of a Type I error

116
Q

what is the based way to Type I, II error risk with available resources

A

conducting a power analysis

117
Q

Correlation

A

the degree of association between betwen two interval- level variables

118
Q

what is correlation represented by

A

a coefficient between +1.00 and -1.00

119
Q

what does a +1.00 correlation coefficient mean

A
  • perfect positive correlation
  • the size of deviations from the mean in both variables are equal in the same direction
120
Q

what does a -1.00 correlation coefficient mean

A
  • perfect negative correlation
  • or the size of deviations from the mean in both variables are euqal in opposite directions
121
Q

what is a 0.00 correlation coefficient mean

A
  • no correlation
  • there is no pattern to the size and direction of deviations from the mean between variables
122
Q

what does the sign of the coefficient indicate

123
Q

what does the magnitude of the correlation coefficient indicate

124
Q

what are scatter plots best used for

A

to visualize the correlation between variables

125
Q

what is the line of best fit

A

best linear estimate of the relationship between variables given the data used to calculate it

126
Q

what does the line of best fit minimize

127
Q

what are risiduals

A

error between measured and predicted values by the lines equation

128
Q

what is pearson correlation coefficient also called

A

pearon’s product moment correlation coefficient

129
Q

what is the equation for pearson correlation coefficient

A

r = sum of (ZxZy)/N
- Zx being number of score pairs
- Zy being product of z-scores for each variable

130
Q

what is the alternative “machine formula” that does not require z-scores

A

r = (sum of (x-mean)(y-mean))/sqrt(sum of (x-mean x)^2) sum(y-mean y)^2))

131
Q

what are the assumptions of pearson correlation

A
  • both variables must be on a continuous (interval or ratio) scale
  • each pair of variables must be indepoendent
  • both variables should be approximately normally distributed
  • the relationship between variables (if one exists) must be linear
  • the dataset should not contain outliers
132
Q

what do you do if the relationship is non linear for pearson correlation

A

use spearman’s rank

133
Q

how does outliers affect Pearson Correlation

A

it is really sensitive to outliers so it may creat an overly strong correlation or weak correlation

134
Q

what is the eqation for spearman’s rank correlation coefficient

A

p = 1- (6*sum of di^2)/(n(n^2-1))
- di^2: the difference between variable ranks
- n = number of observations

135
Q

what is spearman’s rank

A
  • a nonparametric test
  • w/ fewer assumptions including about the data distribution
136
Q

what are the parameters of spearman’s rank

A
  • variables do not need to be normally distributed
  • variables can be discrete
  • relationship between variables can be non-loinear but must be monotonic
  • less sensitive to outliers
137
Q

coefficient of determination

A

r^2
- quantifies the shared variance betwen variables
- how well the indeoendent variables explain the variation in the dependent variables

138
Q

how to verbally express the coefficient of determination

A

_____% of the variance in the dataset can be explained by the variance in what is being looked at.

139
Q

degrees of freedom (df)

A

the number of scores that are free to vary when the sum the scores is set

140
Q

what is the equation for degrees of freedom

A

df = N-#of variables in the correlation

141
Q

what does correlation doesnt = causation mean

A

correlation does not necessarily mean that a change in one variable will result in a change in the other

142
Q

bivariate regression

A

strong enough correlations allow for predictions of one variable based on the values of another variable

143
Q

what is the equation for a bivariate regression model

A

y = beta not + beta1x + e

144
Q

bivariate regression assumptions

A
  • the relationship between variables must be linear
  • each pair of variables must be independent
  • for any value of a predictor (independent variable) the dependent variable must be approximately normally distributed
  • the variance of the residuals must be consistent across the range of predictor values
145
Q

what is homoscedasticity

A

when the spread of residuals is relatively consistent within the regression model

146
Q

how to calculate coefficients for the bivariate regression model

A

beta1: (r(SDy)/(SDx))
beta0: mean y - ((rSDy)/(SDx))mean x

147
Q

will there always be residuals

A

yes, unless there is a perfect correlation between variables

148
Q

how can the residuals in a regression model be represented

A
  • using the standard error of the estimate
  • or the SD of the residuals
149
Q

standard error of estimate equation

A

SEe = sqrt ((sum(yactual-ypred)^2)/(n-2)

150
Q

what is the alternate equation for the standard error of estimate

A

SEe = SDysqrt(1-r^2)

151
Q

what does using the alternate equation for the standard error of estimate result in

A

it underestimates SEe when the sample size is small

152
Q

in a regression coefficient what is H0

153
Q

ina regression coeeficent what is HA

A

beta1 does not equal 0

154
Q

what does the t-statistic tell you

A

determine significance of beta1

155
Q

what is multiple correlation

A

quantifies the degree of relationship/association betwen a function of independent variables and one dependent variabl

156
Q

what is multiple correlation represented by

A

a coefficient R between 0 and 1

157
Q

what does a R = 0.00 value indicate

A

no correaltion, or there is no relationship/association between independent variables and the dependent variable

158
Q

what does a R = 1.00 value indicate

A

perfect correaltion, or the independent variables completely explain the dependent variable

159
Q

what is the multivariabe coeeficient of determination

A
  • R^2
  • same interpretation as bivariate r^2
160
Q

partial correlation

A

quantifies the relationship between an independent variable and dependent variable after removing the effect of another variable

161
Q

covariate

A

an independent variable that can influence the outcome of a given statistical trial, but which is not of direct interest

162
Q

partial coefficient of determination

A

the variance in Y explained by X1 after removing the effects of X2 on both

163
Q

what is an example of partial correlation

A
  • interested in association between children’s age (X1) and muscle strength (Y)
  • children grow and get heavier with age (X2) and may be a covariate
  • using partial correlation = partial out the effect of weight and can leave the variance in strength due solely to age
164
Q

what is unexplained variance and what is it represented by

A
  • (1-R^2)
  • the amount of variation in a dependent variable that a model can explain using the independent variables
165
Q

Multiple and partial correlation assumptions

A
  • both variables must be on a continious (interval or ratio) scale
  • each pair of variables must be independent
  • both variables should be approx. normally distributed
  • the relationship between variabels (if one exists) must be linear
  • the dataset should not contain outliers
166
Q

multiple linear regression

A

prediction of one dependent variable from multiple predictor variables (independent variables)

167
Q

what is the equation of a multiple linear regression

A

Y = a + b1X1 + b2X2 + …. bkXk
- b values are the slope coefficients
- x values are the independent variables
- a is the Y-intercept

168
Q

hierarchial multiple regression

A

reseracher has full control over the model equation and which predictors are included

169
Q

when is hierarchial multiple regression used

A

when hypothesis testing is the goal rather than accurate, efficient dependent variable prediction

170
Q

algorithmic multiple regression

A

computer software/algorithms construct the model equation

171
Q

what are the types of algorithmic multiple regression

A
  • forward selection
  • backward elimination
  • stepwise
172
Q

forward selection algorithmic multiple regression

A

starts with the intercept only, predictors are added to the model one-by-one and assessed, if R^2 increases that shows unique variablility

173
Q

backward elimination algorithmic multiple regression

A
  • starts with all predictors
  • eliminates predictors one-by-one and assesses the resulting model
  • if the removal of the variable decreses explained the varible the least (not sig decrease) the variable is eliminated
174
Q

stepwise algorithmic multiple regressio

A

same as forward selection but previously entered variables can be eliminated in later steps
- if R^2 is not affected by the inclusion or exclusion

175
Q

what is the drawback of a stepwise multiple regression

A

requires a larger sample size compared to other methods to return reliable results

176
Q

what is the ideal ratio of subjects:variables for a stepwise multiple regression

A

20:1 to 40:1 ratio

177
Q

in a table of correlation values for a given dataset how do you interpret the values prsented

A

the values are the r values that indicate the strength of correaltion between variables
- values close to 1.00 indicate strong correalation
- this is then squared to report how much variance of the dataset is explained through this variable

178
Q

how can you tell if a variable has unique variance

A
  • based on if the variable is highly correlated with other variables
  • if the addition of the variable in the R^2 calculation increases significantly, if it does this indicates unique variance
179
Q

how can you visually tell if variables offer unique variance

A
  • if the circle overlaps heavily with the dependent variable
  • if the overlap is present but more overlap is seen with another variable, it doesnt explain that much for the variance and therefore isnt unique
180
Q

what are the multiple regression assumptions

A
  • the relationship between variables must be linear
  • each pair of variables must be independent
  • for any value of a predictor (independent variable), the dependent variables must be approx normally distributed
  • variance of the residuals must be consistent across the range of predictor values
  • independent variables (predictors) should not be correlated with each other
181
Q

what does multicollinearity lead to

A

leads to inflated confidence intervals for slope coefficient estimates and unstable slope coefficient estimates when addtional predictors are added

182
Q

is there a threshold off acceptable multicolinearity

183
Q

what should the variance inflation factor be

A

greater than 10 should be suspicious

184
Q

what is the equation of variance inflation factor

A

VIF = 1/1-R^2

185
Q

what is singularity in multicollinearity

A

two IVs are perfectly related (r=1.00) usually because one was mathematically derived from the other

186
Q

cross validation

A

the process of testing regression equations on a separate and equivalent sample from which they were built to ensure accuracy in their predictions

187
Q

what is expected when applying models to different samples

A

higher prediction errors

188
Q

what is the cross validation model good for and what will be a result

A

training data
- the correlation coefficient will undergo shrinkage and would be smaller on different samples

189
Q

who developed the T test

A

william sealy

190
Q

when are t tests useful

A
  • we do not know the distribution of the population
  • we have a relatively small sample relative to the population
191
Q

how does sample size relate to t distribution

A

as sample size increases, the t distribution approaches a normal distribution

192
Q

what is a t statistic

A

the ratio between mean differences and variability

193
Q

what is a critical statistic

A

the value that must be met to reach statistical significance at a given alpha level

194
Q

what is the generic t test formula

A

t = mean difference/SEof mean difference

195
Q

what can a t statistic be though of as

A

a signal to noise ratio

196
Q

SEM for t statistic

197
Q

what is the standard error of the difference look at

A

the variability of the difference between two groups

198
Q

what are the assumptions of a t Test

A
  • the data must be normally distributed
  • the data must be on the interval or ratio scales
  • the sample is randomly selevted from the greater population
  • when two samples are taken, they should have homogeneity of variance
199
Q

what is a single sample t Test

A

used to compare a single sample mean with a known population meanat i

200
Q

what is the equation for single sample t Test

A

t = (sample mean - population mean)/SEM

201
Q

what is used to determine the critical statistic for significance

A

the degrees of freedom

202
Q

when can H0 be rejected and HA accepted

A

if the |t statistic| > criticial statistic

203
Q

how to calculate a confidence interval for a single sample t Test

A

C.I. = sample mean +/- tcv(SEM)

204
Q

when is the adjusted standard error of difference equation used

A

when unequal sample sizes are present

205
Q

what is the formula for the adjusted standard error of the difference

A

SED = Square root([((n1-1)(SD1^2)+(n2-1)(SD2^2))/(n1+n2-2)][(1/n1)+1/n2)]

206
Q

paired sample t test

A

used ot compare two means from the same or correlated samples

207
Q

what is the equation for a paired sample t test

A

t = sample mean pre - sample mean post /SED

208
Q

what is the corrected SED for the paired sample t test

A

SED = square root((SD1^@)/n1)+(SD2^2/n2)-2r(SD1^2/n1)(SD2^2/n2))

209
Q

what is the alternate approach to calculting the t statistic and SED for a paired sample t Test

A

t = d/SED
- d = mean difference between individual’s scores
SED = SDd/sqrt n
- SDd = standard deviationof the difference

210
Q

what is the confidence interval for the alternate t statistic and SED for paired samples t test

A

CI= mean difference between individuals socres +/-tcv(SED)

211
Q

what is used if data violates the assumptions of a t Test

A

single samples: Wilcoxon signed rank test
independent samples: Mann-Whitney U test
paired samples: Tilcoson signed rank test

212
Q

effect size

A

the stregnth of the relationship between variables

213
Q

omega sqaured

A

estimate of the varinace explained by the influence of the independent variable

214
Q

what is used as a measure of effect size for pretest-posttest

A

percent change