Statistics In Research Complete

Front 1

study population

Back 1

the population of individuals from which samples are obtained for inclusion in an investigation

Front 2

sample

Back 2

a subset of all possible measurements from a population

Front 3

inferential statistics

Back 3

-test hypotheses
-demonstrate an association-make population comparisons

Front 4

descriptive statistics

Back 4

describe and summarize sample characteristic

Front 5

gaussian distribution

Back 5

-the most commonly assumed population distribution used in statistics
-emipirical rule: bell curve

Front 6

normal distribuition data

Back 6

1SD: 68%
2SD: 95%
3SD: 99.7%

Front 7

standard error

Back 7

-a measure of sampling error
-the spread in the mean that would be if ALL possible samples of the same size as the one actually obtained were drawn from the population
-used to determine confidence intervals

Front 8

kurtosis

Back 8

- without changing the mean
- vertical distortion

Front 9

leptokurtosis

Back 9

- high middle

Front 10

platykurtosis

Back 10

- lower middle
- can be flat

Front 11

negative skew

Back 11

higher data values predominate

Front 12

postivie skew

Back 12

lower data values predominate

Front 13

statistics

Back 13

- values calculated from sample data used to estimate a value or parameter in a larger population

Front 14

point estimate

Back 14

a single statistic calculated from sample observations used to estimate the numerical value of a particular population parameter

Front 15

statistical significance testing

Back 15

tells us how much statistical determinations are expected to vary due to chance variations in random sample

Front 16

two-tailed test

Back 16

- investigator can't be sure that the population's parameter is greater or less than the null hypothesis
- most common

Front 17

one-tailed test

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used when the direction of the relationship being studed is known and analysis is only concerned with determining the strength of the relationship
- greater statistical power

Front 18

dependent variables

Back 18

- the study outcome or endpoint
- usual approach: analyze one such variable at a time

Front 19

independent variable(s)

Back 19

- variable(s) measured to estimate the corresponding measurement of the dependent variable
- defines the conditions under which the dependent variable is to be measured

Front 20

univariable analysis

Back 20

- 1 dependent variable, no independent variable
- estimation of the probability of a disease without regard to any characteristic such as age, gender, diet

Front 21

bivariable analysis

Back 21

- 1 dependent variable, 1 independent variable
- determining the if there is an assciation between birth control pill use and stroke

Front 22

multivariable analysis

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- 1 dependent variable, >1 independent variable
- determining the likelihood of stroke for femails, of different ages and smoking status
- also used to adjust for confoudning variables

Front 23

continuous data

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- the range of measurement is any number of equally spaced numerical values between any two points
- measure on ratio or interval scales
- a great number of possible values exist
- ex. serum cholesterol, age, weight
- wide range of values

Front 24

ordinal data

Back 24

- discrete data
- the interval between consecutive measurements is not necessarily known or constant
- ordered sequence
- ex. number of pregancies, scoring systems, mammogram interpretation, cancer staging
- limited number of possible variables

Front 25

nominal data

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- data which is not ordered is measured on a nominal scale
- ex. eye color, gender
- the number of nominal variables is the number of categories minus one

Front 26

rescaling of data

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- age to age range to young, old description
- shifting down results in the loss of information
- statistical power reduced, increasing the likelihood of type II error

Front 27

Uses of univariable analysis

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1.) Descriptive studies - clinical and lab results on a group of diseased patients
2.) Descriptive measurements of the study sample
3.) Comparison, using 2 measurements, with the same or paired individuals: a special situation - a hypothesis may be tested in some cases, no independent variable
*ex. a blood pressure determination made twice on the same individual, or on two paired similar individuals

Front 28

no independent variable ->
one continuous dependent variable ->

Back 28

mean is point estimate -> student's t-test is statistical method

Front 29

no independent variable -> one ordinal dependent variable ->

Back 29

median is point estimate -> Wilcoxon Signed rank test

Front 30

no independent variable -> one nominal dependent variable ->

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"regular" proportion -> binomial distribution
rare -> proportion -> Poisson distribution

Front 31

natural sampling

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- a random, unbiased, sampling from the population
- The distribution of values for independent and dependent variable is representative of the population distribution

Front 32

purposive sampling

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- The researcher determines and therefore defines the sample distribution
- The distribution of values for the independent variable is not representative of the population

Front 33

one continuous dependent variable -> one nominal independent variable

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difference between means is estimation -> student's t-test is inference

Front 34

one continuous dependent variable -> one continuous independent variable from naturalistic or purposive sample

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slope and intercept is estimations -> regression analysis -> F test is inference

Front 35

one continuous dependent variable -> one continuous independent variable from naturalistic sample

Back 35

correlation coefficient -> correlation analysis -> student's t-test

Front 36

least squares regression analysis

Back 36

- Defines a line using a formula which minimizes the sum of the squared differences between the sample data and those estimated by the regression equation
- Y = a + bX
- Slope indicates how a change in the independent variable affects a change in the dependent variable
- Intercept is the mean of the dependent variable when the independent variable =0
- Slope and Intercept are point estimates
- Provides estimate of dependent variable values from independent variable values
- Regression analysis does NOT indicate the strength of a relationship in the population

Front 37

correlation analysis

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- Requires naturalistic sample, continuous independent and dependent variables
- Determines how independent and dependent variables change together
- Covariance

Front 38

Pearson's correlation coefficient

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- equals r
- The point estimate of the strength of association between two continuous variables
- Results range: -1 to +1

Front 39

coefficient of determination

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- r^2
- Represents the amount of variation in the dependent variable that can be explained by the change in the independent variable

Front 40

advantages of multivariable analysis

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1.) Study the relationships between a dependent variable and an independent variable while adjusting for the effect of other independent variables
2.) Perform statistical significance testing on several variables with a chosen value for Type 1 error
3.) One procedure for estimation, inference, and adjustment

Front 41

analysis of variance

Back 41

An analysis of the variation in the outcomes of an experiment to assess the contribution of each variable to the variation

Front 42

covariance

Back 42

Statistic which estimates how closely a dependent and independent variable change together

Front 43

ANOVA

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analysis of variance

Front 44

Multiple regression and correlation analysis

Back 44

Dependent variable predicted by two or more independent variables

Front 45

ANCOVA

Back 45

analysis of covariance