Chapter 14: Quantitative Data-Statistical Analysis Flashcards by Mary Price

Things to keep in mind for this LOOOOONG chapter:

1- you will not be asked to calculate any of the parameters described

2- Research studies are not published and peer-reviewed if they used the wrong type of statistical test or measure to analyze their study.

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Quantitative Studies:

Levels of Measurement (5):

1) Nominal
2) Ordinal
3) Interval
4) Ratio
5) Continuous variable

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Quantitative Studies: Levels of Measurement
_______ measurements are two values that can be accurately described by their ratio; the highest level, and the numbers included are able to have a meaningful zero.

Give an example:

Ratio

Example: Weight
A 200-pound person is clearly twice the weight of a 100-pound person.

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Quantitative Studies: Levels of Measurement
______ measurements rank people on attributes with a number that clearly specifies a distance between the two.

Give an example:

Interval

Example:
An IQ test

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Quantitative Studies: Levels of Measurement
_______ measurements rank people based on an attribute, such as ADLs.

Give an example:

Ordinal

Example: ADL measurements
4 = being independent
1 = being completely dependent
The number relates to the rating of an attribute.

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Quantitative Studies: Levels of Measurement

_________ are variables with interval and ratio measurements.

Continuous variables

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Quantitative Studies: Levels of Measurement
_______ measurement is the lowest level and involves using numbers to designate attributes. In other words, things are indicated by a numeric value, but that numeric value can’t be used mathematically.

Give an example:

nominal

Example: Eye color
Brown can be 1
Blue can be 2
Green can be 3, etc.

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Quantitative Studies:
_________ Statistics are used to synthesize and describe data.
________ are calculated values such as averages and percentages.
_______ is a descriptive index from a sample.

Descriptive statistics

Parameters

Statistic

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Quantitative Studies: Descriptive Statistics

Name the 4 Types of Descriptive Statistics

1) Frequency distribution
2) Symmetric distribution
3) Skewed distribution
4) Normal distribution

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Quantitative Studies: Descriptive Statistics
A ______ distribution is a bell-shaped curve that has a lower peak than other types of distributions.
SD within 1?
SD within 2?

normal

For a normal distribution or bell-shaped curve,
68% of values will be within 1 SD of the mean.
95% of values will be within 2 SD of the mean.

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Quantitative Studies: Descriptive Statistics

________ distributions are ones where if the graph is folded in half the two halves would be superimposed.

Symmetric

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Quantitative Studies: Descriptive Statistics

In a _______ distribution, the majority of the data peaks to one side.

skewed

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Quantitative Studies: Descriptive Statistics
A _______ distribution takes a list of values and organizes it lowest to highest and includes a count.

Give an example:

frequency

Example:
If there were 10 values and 5 of them were 80, it would list 80 once and put a count of 5 and a percentage of 50%.

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Quantitative Studies:
______ ________ includes methods to determine a central value for a set of numbers.

This includes _____, _____, and ______.

Central tendency

Mode, Median, and Mean

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Quantitative Studies: Central Tendency

The ____ is the number that occurs most frequently in a distribution.

mode

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Quantitative Studies: Central Tendency

The ______ is the point in a distribution that divides scores in half.

median

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Quantitative Studies: Central Tendency

The _____ equals the sum of all values divided by the number of participants.

mean

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Quantitative Studies: Central Tendency
Measures of central tendency can be the same for two different sets of distributions.
This is known as _______.

variability

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Quantitative Studies: Variability

The _____ is the highest minus the lowest score in a distribution.

range

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Quantitative Studies: Variability
The ___________ is a variability index calculated based on every value in a distribution; it is the average amount of deviation of values from the _____.

standard deviation

average amount of deviation of values from the MEAN

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Quantitative Studies: Variability + Standard Deviation

The lower the standard deviation, the more _________ the distribution.

homogenous

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What Human attributes occur as bell-shaped curves?

height and intelligence

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Quantitative Studies: Bivariate Descriptive Statistics

If a frequency distribution is based on two variables instead of one, a _____________ can be used to depict the data.

crosstabs crosstable (Crosstabulations)
-Two-variable descriptive statistics

Correlation
Correlation coefficient

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Quantitative Studies: Bivariate Descriptive Statistics

Crosstabulations are used for what 2 kinds of data?

Used with nominal or ordinal data

The most widely used correlation statistic is Pearson’s r (the product-moment correlation coefficient).

Spearman’s rho is a correlation coefficient calculated for values on an ordinal scale.

A correlation matrix displays correlation coefficients in tables displaying rows and columns.

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Quantitative Studies: Bivariate Descriptive Statistics _________ are methods to describe relationships between two variables, and utilizes the ________ _________, which is the intensity and direction of the relationship between two variables.

Correlations | correlation coefficient

Quantitative Studies: Bivariate Descriptive Statistics Correlation coefficients range from _________.

range from -1.0 to 0 to 1.0.

Quantitative Studies: Bivariate Descriptive Statistics + Correlation coefficients: A +1.0 would mean a _______ relationship. Values b/t 0 and +1.0 represent a _____relationship. Values b/t -1 and 0 represent a ______ relationship

Perfect relationship Positive relationship Negative relationship

Quantitative Studies: Bivariate Descriptive Statistics Name the 2 Types of Correlation Coefficients: Which one is the most widely used correlation statistic?

1) Pearson's r --> MOST WIDELY USED | 2) Spearman's rho

Quantitative Studies: Bivariate Descriptive Statistics + Correlation Coefficients: The product-moment correlation coefficient =

Pearson’s r --> MOST WIDELY USED Spearman’s rho is a correlation coefficient calculated for values on an ordinal scale.

Quantitative Studies: Bivariate Descriptive Statistics + Correlation Coefficients: ________ is a correlation coefficient calculated for values on an ordinal scale.

Spearman’s rho

Quantitative Studies: Bivariate Descriptive Statistics + Correlation Coefficients: What is used to display correlation coefficients in a table with rows and columns?

A correlation matrix displays correlation coefficients in tables displaying rows and columns.

Quantitative Studies: Describing Risk ________ risk is the proportion of people who experienced an undesirable outcome in each group. How do we calculate this and what is it called? Give an example:

Absolute risk Absolute risk reduction is a comparison of the two risks. Example: one group of people had education on falls and are the experimental group, and the control group did not have education on falls. 10% of individuals in the experimental group had falls, while 30% of the individuals had falls in the control group. Experimental group AR = 0.10 Control group AR = 0.30 Calculation: (Experimental group AR) - (Control group AR) x100 = .30 - .10 = .20 or 20%. This suggests that 20% more of the control group would not have fallen with the intervention.

Quantitative Studies: Describing Risk _________ is the ratio of two odds, and gives us the odds of an undesirable effect. How do we calculate this? Give an example:

Odds ratio Calculation: (# who didn't fall) / (# who did fall) = odds ratio Example: 10% of individuals in the experimental group had falls, while 30% of the individuals had falls in the control group. For the experimental group, 70 / 30 = 2.33 For the control group, 90 / 10 = 9.00 Odds ratio = 2.33 / 9 = 0.259 This means the odds of falling with education is 25%, and the odds of falling without education is 400% more likely.

Quantitative Studies: Describing Risk The number needed to treat estimates is ......? How we calculate this? Give an Example:

how many people need to receive an intervention to prevent one undesirable outcome Calculation: 1 / Absolute risk reduction Example: 1 / .20 = 5 Five people would need to receive education on falls.

Quantitative Studies: ________ statistics use the law of probability to test research hypotheses with data. Use ____ to test research hypotheses.

Inferential statistics Use data to test research hypotheses.

Quantitative Studies: Inferential Statistics A confidence interval relates to the probability of being right. A confidence interval of __%-__% is the goal for most researchers.

confidence interval of 95%-99% GOAL FOR US: To understand that in a research study, seeing the researchers describe obtaining a confidence interval of 95% or 99% indicates a high probability of the data being right.

Quantitative Studies: Inferential Statistics ______ ______ of the Mean is the standard deviation of the mean of a sample. The higher the number --> ? What sample size (small or large) will help reduce the chance of outlying data?

Standard Error of the Mean (SEM) The higher the number --> the more error associated with the sample selected. - In other words, a lower SEM reflects a more appropriate sample size. LAAAAARGE

Quantitative Studies: Hypothesis Testing | Hypothesis testing uses ______ criteria to determine if the hypothesis is supported or rejected.

uses objective criteria

Quantitative Studies: Hypothesis Testing ______ ________ means the results are not likely to be due to chance. What Standard Deviation value indicates this? What Confidence Interval (%) indicates this?

Statistical significance Falls within 2 standard deviations of the mean Confidence intervals of 95% or greater represent statistically significant values.

Quantitative Studies: Hypothesis Testing | A _______ result means that the results could be due to chance.

nonsignificant

Quantitative Studies: Hypothesis Testing | A ____ hypothesis is when there is no relationship between the independent variable and dependent variable.

null hypothesis

Quantitative Studies: Hypothesis Testing A type __ error is rejecting the null hypothesis that is in fact true. Type __ error assumes the null hypothesis is true when in fact the independent variable did have an effect.

Type I Type II

Quantitative Studies: Hypothesis Testing | A power analysis is calculated to determine the chance of a type __ error.

Type II = power analysis

Quantitative Studies: Hypothesis Testing | Alpha, or level of significance, is calculated to determine the chance of a type __ error.

Type I error = alpha

Quantitative Studies: Hypothesis Testing | Is it possible to eliminate Type I and Type II errors?

No, IMPOSSIBLE to eliminate type I and type II errors So, statistical tests will aim to be statistically significant.

Quantitative Studies: Specific Statistical Tests Name the 3 Specific Statistical Tests: What value determines Statistical significance for all 3 tests?

1) T-tests 2) Analysis of Variance (ANOVA) test 3) Chi-Squared Test (X^2) A p value of <0.05 indicates Clinical Significance

Quantitative Studies: Specific Statistical Tests | T-tests, ANOVA tests, and Chi-squared tests use _____ to determine Clinical Significance.

p value <0.05 = Clinical Significance

Quantitative Studies: Specific Statistical Tests _________ is used to test mean group differences of three or more groups. Calculates a __ value, which varies for each study.

Analysis of variance (ANOVA) test ANOVA calculates a F value

Quantitative Studies: Specific Statistical Tests A parametric test for testing differences in two group means is called a _______. Calculates a __ value, but the individual value is not important.

T-test Calculates a t value, but the individual value is not important.

Quantitative Studies: T-test _______ t-test is appropriate for two different groups of people. _______ t-test is used when one group is tested at two different points in time.

Independent t-test Paired t-test

Quantitative Studies: Specific Statistical Tests A _______ test focuses on the difference in proportion. Calculates the value of ______, which varies from test to test. Give an example:

chi-squared test Calculate the value of chi-squared, which varies from test to test. If blood sugars were controlled in 60% of patients with a diabetes educational intervention, versus 40% of patients without, is the 20% real?

Quantitative Studies: Multivariate Statistical Analysis Name the 3 Types of Multivariate Statistical Analysis Tests:

1) Multiple Regression 2) Analysis of Covariance (ANCOVA) 3) Logistic Regression

Quantitative Studies: Multivariate Statistical Analysis _________ is a combination of ANOVA and multiple regression. This test is good when there is not control through _______. ANCOVA attempts to control _________.

Analysis of Covariance (ANCOVA) ANCOVA is good when there is not control through randomization. ANCOVA attempts to control covariates of confounding variables.

Quantitative Studies: Multivariate Statistical Analysis ___________ measures several independent variables, called ______ variables in this type of test. A statistic called a _____________ is calculated and known as R.

Multiple Regression Predictor variables multiple correlation coefficient (R)

Multiple Regression Predictor variables multiple correlation coefficient (R) R varies from 0.0 to 1.00.

Quantitative Studies: Multiple Regression ________ gives a % of how influential the predictor variables were. A higher R-squared value means --> ?

R-squared A higher R-squared value means --> it is more likely the predictor variables accounted for the variation.

Quantitative Studies: Reliability Measurements What are the 3 Types of Reliability Tests? Indicate what each test calculates: For all of these, scores closer to ___ means a higher level of reliability.

1) Test-Retest Reliability - Intraclass correlation coefficient (ICC) 2) Interrater reliability - Cohen’s kappa 3) Internal consistency reliability - Coefficient alpha or Cronbach’s alpha For all of these, scores closer to 1.0 means a higher level of reliability.

``` Quantitative Studies: Reliability Measurements Intraclass correlation coefficient is used for _______ reliability and ranges from 0.00 to 1.00. ```

test-retest reliability

Quantitative Studies: Reliability Measurements Cohen’s kappa is used for dichotomous classifications and is used for _______ reliability; helpful to determine if two individuals would rate something similarly.

Interrater reliability

Quantitative Studies: Reliability Measurements Coefficient alpha aka Cronbach’s alpha is used for __________ reliability, which measures how often components of a multi-component tool measure the same attribute

Internal consistency reliability

Quantitative Studies: Validity Assessment | Name the 3 Types of Validity

1) Content Validity 2) Criterion Validity 3) Construct Validity

Quantitative Studies: Validity Assessment _____ validity determines if the content of the items adequately reflects the construct of interest. An index of ___ or higher indicates good validity.

Content Validity - Experts meet to rate each item to determine a content validity index. An index of 0.90 or higher indicates good content validity.

Quantitative Studies: Validity Assessment ______ validity concerns the extent to which a measure is truly measuring the target construct. It uses various procedures such as Pearson’s r value, or an independent group t-test.

Construct validity

Quantitative Studies: Validity Assessment ______ validity concerns the extent to which scores on a measure are consistent with a gold standard criterion. Two terms fall under this type of validity, What are they?

Criterion validity 1) Sensitivity 2) Specificity

Quantitative Studies: Criterion Validity Assessment | ________ is the ability of a measure to correctly screen or diagnose a condition.

Sensitivity Think: SENSITIVITY = HOW MANY TIMES DID I GET CLOSE TO THE TARGET the ability of a test to correctly identify patients WITH a disease.

Quantitative Studies: Criterion Validity Assessment | ________ is the measure’s ability to correctly identify non-cases or screen out those without the condition.

Specificity Think: SPECIFICITY = HOW CLOSE TO A TARGET the ability of a test to correctly identify people WITHOUT the disease

Quantitative Studies: Criterion Validity Sensitivity vs Specificity using Walter's Example of a flu swab diagnostic test: A specific flu swab would.... A sensitive flu swab would...

A specific flu swab would NOT be positive for strep or other conditions. A sensitive flu swab would be positive for the flu MOST of the time.

Quantitative Studies: Criterion Validity Sensitivity vs Specificity using Walter's Example of a flu swab diagnostic test: What if a flu swab always caught the flu, but also would show positive for strep?

highly sensitive, but has LOW specificity

Quantitative Studies: Criterion Validity Sensitivity vs Specificity using Walter's Example of a flu swab diagnostic test: What if a flu swab ONLY detected the flu, but only detected half the cases of flu successfully?

highly specific, but has LOW sensitivity