RESEARCH EXAM 3--ch 12 & 13 Flashcards Preview

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Flashcards in RESEARCH EXAM 3--ch 12 & 13 Deck (136):
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statistical analysis examples (2)

descriptive statistics and inferential statistics

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o Used to DESCRIBE and synthesize data

descriptive statistics

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

inferential statistics

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descriptive indexes examples (2)

parameter

statistic

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o A descriptor for a population

(The average or percentage of age of menses for American females)

parameter

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o A descriptor for a sample, a descriptive index

o the average age of menses for female professors at MSU

statistic

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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

freq. distributions

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freq. distributions can be presented in which 2 ways

a table

graphically (frequency polygons)

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freq distributions can be described in terms of:

shape
central tendency
variability

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

MIRROR IMAGES OF EACH OTHER

symmetric shape

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

skewed (asymmetric)

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long tail drifts off to the right

− Positive skew

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− long tail drifts off to the left

negative skew

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# of peaks

modality

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1 peak

unimodal

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2 peaks

biomodal

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2+ peaks =

multimodal

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normal distribution = what shape

bell-shaped curve

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characteristics of normal distribution (bell shaped curve)

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

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

inferential

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

central tendency

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

• the mode, the median, and the mean

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most frequent

mode

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


median


(NOMINAL MEASURES)

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

mean

(SKEWED DISTRIBUTION)

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

MODE

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

median

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♣ Preferred when a distribution is highly skewed

median

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

mean

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

inferential statistics

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

variability

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− Used to describe one variable at a time

univariate

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o Little variability

homogeneity

TALLER AND SKINNIER TABLE

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great variability

heterogeneity

WIDER AND SHORTER TABLE

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

range and SD

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o Highest value minus lowest value

range

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

standard deviation (SD)

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

Bigger

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

SD

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

2 SDs of the mean

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ϖ Used for describing the relationship between TWO variables


don't answer research questions

Bivariate Descriptive Statistics

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2 common approaches for bivariate descriptive statistics

crosstabs and
correlation coefficients

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crosstabs =

contingency tables

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

cross-tabulated

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at intersection of rows and columns display counts and percentages

• “Cells”

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crosstab variables are usually ____ or _____

nominal or ordinal

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o whether there is a relationship between smoking and sex)

crosstabs

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• Indicate direction and magnitude of relationship between two variables

o CORRELATION COEFFICIENTS

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The most widely used correlation coefficient is

Pearson’s r

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Pearson’s r is used when

• both variables are interval or ratio-level measures

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• Correlation coefficients can range from

-1.00 to +1.00

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the higher the correlation coefficients the ____ the relationship

stronger

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− Negative (inverse) relationship ranges from?

(0.00 to -1.00)

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♣ One variable increases in value as the other decreases

e.g. amt of exercise and weight

Negative (inverse) relationship

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positive relationship ranges from

0.00 to +1.00

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♣ Both variables increase


o E.g., calorie consumption and weight

positive relationship

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is -0.45 or +0.40 stronger?

-0.45

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

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

.70

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frequently used indexes:

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

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


widely reported risk index

odds

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

inferential statistics

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ϖ inferential statistics is based on:

laws of probability

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ϖ Its standard deviation is called the

standard error of the mean (SEM)

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

SD for the sample

sample size

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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

Confidence Intervals

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

5 times out of 100

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

hypothesis testing

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

null hypothesis

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

ϖ Statistical tests

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ϖ statistical test Involve statistical decision making to either:

o Accept the null hypothesis
o Reject the null hypothesis

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


A false-positive result

type 1 error

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o Risk of Type I error is controlled by the

level of significance (alpha)

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the minimal acceptable alpha level is

0.05

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

type 2 error

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the risk of a type 2 error

beta

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

power

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power =

1 - B

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how to fix a type 2 error

increase the sample size

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o Use study data to compute a test statistic

ϖ Tests of Statistical Significance

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value that indicates that the null is improbable

statistically significant

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


(bias, etc.)

nonsignificant results

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Overview of Hypothesis Testing Procedures steps:

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

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o Involves estimation of a parameter
o Assumes variables are normally distributed in the population

parametric statistics

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parametric statistics: which part of NOIR

interval/ratio scale

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ϖ Nonparametric Statistics: which part of NOIR

nominal/ordinal

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o Tests the significance of differences between TWO group means

t-Tests

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independent t-test test what?

between subjects

(men vs. women)

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dependent (paired) groups t-tests test what?

within subjects

(before and after surgery)

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o Tests the mean group differences of THREE or more groups

ANOVA

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

chi-squared

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chi-squared tests between which 2 frequencies?

observed and expected frequencies

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a descriptive and an inferential statistic

pearson's r

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o Tests that the relationship between two variables is not zero

correlation coefficients

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

effect size indexes


(POWER ANALYSIS)

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

Multivariate Statistical Analysis

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

multiple regression

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o Continuous
o Interval- or ratio-level variables

• Dependent/Outcome variables

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o Either interval or ratio level variables
OR dichotomous

• Independent/Predictor variables

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• Used to control confounding variables statistically

ANCOVA

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= the confounding variables being controlled

• Covariates

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depdent variables in ANCOVA

continuous--ratio or interval

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independent variables in ANCOVA

nominal (group status)

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covariates in ANCOVA

continuous or dichotomous

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

Logistic Regression

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

OR

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ϖ typically summarize sample characteristics

Descriptive statistics

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ϖ Text gives you the following information:

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

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

chi squared

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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

t-tests

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

ANOVA

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Discussion Section of the Research Report has what content:

An interpretation of the results

• Clinical and research implications

• Study limitations and ramifications for the believability of the results

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Remember that researchers are seldom totally

objective

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• 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.

interpreted

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Interpretive Task
• Involves addressing six considerations:

credibility
precision
magnitude
meaning
generalizability
implications

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• Interpreting research results involves making a series of

inferences

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• We infer from study results to

"truth in the real world"

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Approach the task of interpretation with what type of mindset

critical and even skeptical

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

null wrong

hypothesis right

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• The better the _____, the more credible the results

proxies

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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.

CONSORT guidelines

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What alternative and potentially competing hypotheses could explain the results?

internal validity

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

construct validity

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Was a power analysis done?

statistical conclusion validity

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To whom would it be safe to generalize the results
in this study?

external validity

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Types of Biases

• Expectation Bias • Hawthorne Effect • Selection Bias

• Attrition Bias

• History Bias

• Extreme Response Bias • Type II error

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Multiple measures of the same outcome

triangulation

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

credibility

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results should be interpreted in light of ?

precision of the estimates

and

magnitude of the effects

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(often communicated through confidence intervals)

precision

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effect sizes

magnitude

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precision =

confidence intervals

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

accuracy

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magnitude =

effect sizes

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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.

effect sizes

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correlation does not prove

causation

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• Greatest challenges to interpreting the meaning of results:

– Nonsignificant results

– Serendipitous significant results

– Mixed results