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6TH SEMESTER > RESEARCH EXAM 3--ch 12 & 13 > Flashcards

RESEARCH EXAM 3--ch 12 & 13 Flashcards

1
Q

statistical analysis examples (2)

A

descriptive statistics and inferential statistics

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2
Q

o Used to DESCRIBE and synthesize data

A

descriptive statistics

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3
Q

o Used to make inferences/objective decisions about the population based on parameters using sample data

A

inferential statistics

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4
Q

descriptive indexes examples (2)

A

parameter

statistic

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5
Q

o A descriptor for a population

The average or percentage of age of menses for American females

A

parameter

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6
Q

o A descriptor for a sample, a descriptive index

o the average age of menses for female professors at MSU

A

statistic

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7
Q

ϖ A systematic arrangement of numeric values on a variable from lowest to highest, and a count of the number of times (and/or percentage) each value was obtained or has occurred

A

freq. distributions

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8
Q

freq. distributions can be presented in which 2 ways

A

a table

graphically (frequency polygons)

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9
Q

freq distributions can be described in terms of:

A

shape
central tendency
variability

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10
Q

− When folded over the two halves of a frequency polygon would be superimposed

MIRROR IMAGES OF EACH OTHER

A

symmetric shape

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11
Q

− Peak is in the center and one tail is longer than the other

A

skewed (asymmetric)

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12
Q

long tail drifts off to the right

A

− Positive skew

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13
Q

− long tail drifts off to the left

A

negative skew

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14
Q

of peaks

A

modality

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15
Q

1 peak

A

unimodal

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16
Q

2 peaks

A

biomodal

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17
Q

2+ peaks =

A

multimodal

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18
Q

normal distribution = what shape

A

bell-shaped curve

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19
Q

characteristics of normal distribution (bell shaped curve)

A

♣ Symmetrical
♣ Unimodal (1 peak)
♣ Not very peaked, not too flat

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20
Q

normal distribution (bell shaped curve) are an important distribution in _____ statistics

A

inferential

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21
Q

• Indexes of “typicalness” of a set of scores that comes from CENTER of the distribution

A

central tendency

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22
Q

Researchers avoid using the term “average” because there are three indexes of central tendency:

A

• the mode, the median, and the mean

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23
Q

most frequent

A

mode

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24
Q

the point in a distribution (middle) above which and below which 50% of cases fall

A

median

NOMINAL MEASURES

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25
Q

• equals the sum of all scores divided by the total number of scores

A

mean

SKEWED DISTRIBUTION

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26
Q

o Useful mainly as gross descriptor, especially of nominal (e.g., gender) measures

A

MODE

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27
Q

o Useful mainly as descriptor of typical value when distribution is skewed (household value)

A

median

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28
Q

♣ Preferred when a distribution is highly skewed

A

median

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29
Q

o Most stable (best) and widely used indicator of CENTRAL TENDENCY

A

mean

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30
Q

o the Mean Can do a lot of further analysis such as calculating _____ statistics

A

inferential statistics

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31
Q

• The degree to which scores in a distribution are spread out or dispersed – how scattered numbers are

A

variability

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32
Q

− Used to describe one variable at a time

A

univariate

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33
Q

o Little variability

A

homogeneity

TALLER AND SKINNIER TABLE

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34
Q

great variability

A

heterogeneity

WIDER AND SHORTER TABLE

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35
Q

• Indexes of variability describes how different the scores were such as homo or heater variability by using what 2 things:

A

range and SD

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36
Q

o Highest value minus lowest value

A

range

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37
Q

o Average amount of deviation (variability) of values from the mean

A

standard deviation (SD)

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38
Q

o _____ the number in the standard deviation, the more variable the sample’s variables were

A

Bigger

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39
Q

o tells use how much, on average, the scores deviate from the mean

A

SD

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40
Q

o In a normal distribution, 95% of the scores fall within

A

2 SDs of the mean

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41
Q

ϖ Used for describing the relationship between TWO variables

don’t answer research questions

A

Bivariate Descriptive Statistics

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42
Q

2 common approaches for bivariate descriptive statistics

A

crosstabs and

correlation coefficients

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43
Q

crosstabs =

A

contingency tables

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44
Q

• A two-dimensional frequency distribution; frequencies of two variables are

A

cross-tabulated

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45
Q

at intersection of rows and columns display counts and percentages

A

• “Cells”

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46
Q

crosstab variables are usually ____ or _____

A

nominal or ordinal

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47
Q

o whether there is a relationship between smoking and sex)

A

crosstabs

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48
Q

• Indicate direction and magnitude of relationship between two variables

A

o CORRELATION COEFFICIENTS

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49
Q

The most widely used correlation coefficient is

A

Pearson’s r

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50
Q

Pearson’s r is used when

A

• both variables are interval or ratio-level measures

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51
Q

• Correlation coefficients can range from

A

-1.00 to +1.00

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52
Q

the higher the correlation coefficients the ____ the relationship

A

stronger

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53
Q

− Negative (inverse) relationship ranges from?

A

(0.00 to -1.00)

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54
Q

♣ One variable increases in value as the other decreases

e.g. amt of exercise and weight

A

Negative (inverse) relationship

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55
Q

positive relationship ranges from

A

0.00 to +1.00

56
Q

♣ Both variables increase

o E.g., calorie consumption and weight

A

positive relationship

57
Q

is -0.45 or +0.40 stronger?

A

-0.45

− Because -.45 is closer to one & you disregard the (+) & (-)

58
Q

• Usually, think of an r of ___ as high; perfect correlations are very, very rare

A

.70

59
Q

frequently used indexes:

A

absolute risk
absolute risk reduction (ARR)
odds ratio (OR)

60
Q

the proportion of people with an adverse outcome relative to those without it (e.g., the odds of…)

widely reported risk index

A

odds

61
Q

ϖ Used to make objective decisions about population parameters using sample data

A

inferential statistics

62
Q

ϖ inferential statistics is based on:

A

laws of probability

63
Q

ϖ Its standard deviation is called the

A

standard error of the mean (SEM)

64
Q

o you Can estimate the SEM from data from a single sample, using two pieces of information:

A

SD for the sample

sample size

65
Q

indicate the upper and lower confidence limits

ϖ a wide range of values for the population value and the probability of being right

ϖ Reflect how much risk researchers are willing to take of being wrong

A

Confidence Intervals

66
Q

o CI of 95% reflects that researchers accept the risk they will be wrong

A

5 times out of 100

67
Q

ϖ Uses objective criteria for deciding whether research hypotheses should be accepted as true or rejected false

A

hypothesis testing

68
Q

states that there is no relationship between the independent and dependent variables

A

null hypothesis

69
Q

o The way researchers seek to accomplish rejection of the null hypothesis

A

ϖ Statistical tests

70
Q

ϖ statistical test Involve statistical decision making to either:

A

o Accept the null hypothesis

o Reject the null hypothesis

71
Q

o Rejection of a null hypothesis when it should not be rejected

A false-positive result

A

type 1 error

72
Q

o Risk of Type I error is controlled by the

A

level of significance (alpha)

73
Q

the minimal acceptable alpha level is

A

0.05

74
Q

o Failure to reject a null hypothesis when it should be rejected
• A false-negative result

A

type 2 error

75
Q

the risk of a type 2 error

A

beta

76
Q

• is the ability of a statistical test to detect true relationships and is the complement of beta

A

power

77
Q

power =

A

1 - B

78
Q

how to fix a type 2 error

A

increase the sample size

79
Q

o Use study data to compute a test statistic

A

ϖ Tests of Statistical Significance

80
Q

value that indicates that the null is improbable

A

statistically significant

81
Q

• Mean that any observed difference or relationship could have been the result of a chance fluctuation

(bias, etc.)

A

nonsignificant results

82
Q

Overview of Hypothesis Testing Procedures steps:

A

ϖ Select an appropriate test statistic
ϖ Establish significance criterion
ϖ Compute test statistic
ϖ Calculate degrees of freedom (df)
ϖ Compare the computed test statistic to the table value
ϖ Decide to accept or reject null hypothesis

83
Q

o Involves estimation of a parameter

o Assumes variables are normally distributed in the population

A

parametric statistics

84
Q

parametric statistics: which part of NOIR

A

interval/ratio scale

85
Q

ϖ Nonparametric Statistics: which part of NOIR

A

nominal/ordinal

86
Q

o Tests the significance of differences between TWO group means

A

t-Tests

87
Q

independent t-test test what?

A

between subjects

men vs. women

88
Q

dependent (paired) groups t-tests test what?

A

within subjects

before and after surgery

89
Q

o Tests the mean group differences of THREE or more groups

A

ANOVA

90
Q

o Tests the difference in proportions in categories within a contingency table

A

chi-squared

91
Q

chi-squared tests between which 2 frequencies?

A

observed and expected frequencies

92
Q

a descriptive and an inferential statistic

A

pearson’s r

93
Q

o Tests that the relationship between two variables is not zero

A

correlation coefficients

94
Q

Estimates of the magnitude of effects of an independent variable on the dependent variable

A

effect size indexes

POWER ANALYSIS

95
Q

ϖ Statistical procedures for analyzing relationships among THREE or more variables simultaneously

A

Multivariate Statistical Analysis

96
Q

• Used to predict a dependent variable based on two or more independent (predictor) variables

A

multiple regression

97
Q

o Continuous

o Interval- or ratio-level variables

A

• Dependent/Outcome variables

98
Q

o Either interval or ratio level variables

OR dichotomous

A

• Independent/Predictor variables

99
Q

• Used to control confounding variables statistically

A

ANCOVA

100
Q

= the confounding variables being controlled

A

• Covariates

101
Q

depdent variables in ANCOVA

A

continuous–ratio or interval

102
Q

independent variables in ANCOVA

A

nominal (group status)

103
Q

covariates in ANCOVA

A

continuous or dichotomous

104
Q

ϖ Analyzes relationships between a nominal-level dependent variable (outcome) and 2+ independent variables

A

Logistic Regression

105
Q

ϖ The ______is calculated after first removing (statistically controlling) the effects of confounding variables

A

OR

106
Q

ϖ typically summarize sample characteristics

A

Descriptive statistics

107
Q

ϖ Text gives you the following information:

A

o Which test was used
o Value of the calculated statistic
o Degrees of freedom
o Level of statistical significance

108
Q

o Tucker tested the difference in the proportion of smokers versus nonsmokers who had ever tried an illegal drug

A

chi squared

109
Q

o Chase tested the difference in the mean birth weights of infants whose mothers either had or had not participated in a special prenatal education program

A

t-tests

110
Q

o Hutchings compared mean preoperative anxiety levels in three groups of patients with different types of cancer

A

ANOVA

111
Q

Discussion Section of the Research Report has what content:

A

An interpretation of the results

  • Clinical and research implications
  • Study limitations and ramifications for the believability of the results
112
Q

Remember that researchers are seldom totally

A

objective

113
Q
  • The statistical results of a study, in and of themselves, do not communicate much meaning.
  • Statistical results must be ______ to be of use to clinicians and other researchers.
A

interpreted

114
Q

Interpretive Task

• Involves addressing six considerations:

A 
credibility
precision
magnitude
meaning
generalizability
implications
115
Q

• Interpreting research results involves making a series of

A

inferences

116
Q

• We infer from study results to

A

“truth in the real world”

117
Q

Approach the task of interpretation with what type of mindset

A

critical and even skeptical

118
Q

Test the ―null hypothesis that the results are ____ against the ―research hypothesis that they are _____

A

null wrong

hypothesis right

119
Q

• The better the _____, the more credible the results

A

proxies

120
Q

Reporting guidelines have been developed so that readers can better evaluate methodologic decisions and outcomes.

include a flow chart for documenting participant flow in a study.

A

CONSORT guidelines

121
Q

What alternative and potentially competing hypotheses could explain the results?

A

internal validity

122
Q

• Do the specified eligibility criteria adequately capture the population construct?

A

construct validity

123
Q

Was a power analysis done?

A

statistical conclusion validity

124
Q

To whom would it be safe to generalize the results

in this study?

A

external validity

125
Q

Types of Biases

A
  • Expectation Bias • Hawthorne Effect • Selection Bias
  • Attrition Bias
  • History Bias
  • Extreme Response Bias • Type II error
126
Q

Multiple measures of the same outcome

A

triangulation

127
Q

Mixed methods can corroborate evidence that can lead to heightened _____ of the data

A

credibility

128
Q

results should be interpreted in light of ?

A

precision of the estimates

and

magnitude of the effects

129
Q

(often communicated through confidence intervals)

A

precision

130
Q

effect sizes

A

magnitude

131
Q

precision =

A

confidence intervals

132
Q

• Confidence intervals indicate the ________ of the evidence of quantities of direct interest, such as treatment benefit

A

accuracy

133
Q

magnitude =

A

effect sizes

134
Q

tell us how much of a change implementing the intervention will give. Is it a large change or a relatively small change? This can affect whether we adopt the EBP change or not.

A

effect sizes

135
Q

correlation does not prove

A

causation

136
Q

• Greatest challenges to interpreting the meaning of results:

A

– Nonsignificant results

– Serendipitous significant results

– Mixed results

6TH SEMESTER (22 decks)

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