Module 4

Question 1

Read chapter to clarify concepts in this because its going to be on exam

Answer 1

….

Question 2

Statistical Techniques

Answer 2

Analysis procedures to examine, reduce and give meaning to numerical data gathered in a study.

Question 3

Descriptive statistics

Answer 3

Summary statistics that allow researcher to organize date in ways that give meaningful and facilitate insight.

Question 4

Inferential statistics

Answer 4

Addresses objectives, questions and hypothesis in studies to allow inference from the study sample to the target population (identify relationships, examine predictions and determine group differences)

Question 5

Elements of the Statistical Analysis Process include

Answer 5

• Management of missing data
• Description of the sample
• Reliability and validity of measurement methods
• Statistical analyses

Question 6

Level of Statistical Significance

Answer 6

Probability level at which the results of the statistical analysis are judged to indicate a statistically significant difference between groups

Question 7

P-Value (Probability Value)

Answer 7

• Usually set at 0.05 (5%) or 0.01 (1%)

- These are arbitrary numbers - are greed-upon values that indicates the likelihood the result is NOT due to chance.

Question 8

Layman terms: P-Value

Answer 8

You want to ensure your statistical results are not due to chance alone

Question 9

Lower p-value

Answer 9

Less likely the results are due to chance and are more likely an indication of reality

Question 10

High p-value

Answer 10

More likely the results are due to chance and are not a true indication of reality

Question 11

If the p-value of the result is LESS than 0.05

Answer 11

It is considered statistically significant

This means the researcher can confidently assume that the results are not due to chance alone

Question 12

Frequency distributions

Answer 12

Describes the occurrence of scores or categories in a study

Question 13

Measures of central tendency

Answer 13

Frequently referred to as midpoint in the data or as an average of the date.
Most concise statement of the nature of the date in a study.

Question 14

What are the three common measures of central tendency?

Answer 14

Mean, Median and Mode

Question 15

Mode

Answer 15

Numerical value/score that occurs with greatest frequency

Question 16

Median

Answer 16

Midpoint or the score at the exact center of the ungrounded frequency distribution.
*Not affected by extreme scores (outliers)

Question 17

Mean

Answer 17

Most commonly used measure - sum of scores divided by the number of scores being summed
*Extreme scores (outliers) affect this measure.

Question 18

Measures of Dispersion/Variability

Answer 18

Measures of individual differences of the members in a sample.

Question 19

Measures of Dispersion/Variability indicate

Answer 19

• How scores in a sample are dispersed or spread around the mean
• How different the scores are or the extent to which individual scores deviate from one another

Question 20

Dispersion/Variability: If individual scores are similar,

Answer 20

Measures of variability are small

Question 21

Dispersion/Variability: If individual scores are dissimilar,

Answer 21

Measures of variability are larger

Question 22

Common measures of dispersion/variability include

Answer 22

Range, Variance and Standard Deviation

Question 23

Range

Answer 23

Simplest measures of Dispersion

Obtained by subtracting the lowest score from the highest score

Question 24

Standard Deviation

Answer 24

Is the average difference value, provides a measure of the average deviation of a value from the mean in a particular sample.

Question 25

Why is understanding levels of measurement important?

Answer 25

When evaluating the statistical analyses use in a study

Question 26

Four levels of measurement include

Answer 26

Nominal Ordinal Interval Ratio

Question 27

The higher the level of measurement, the

Answer 27

The greater the flexibility the researcher has in choosing statistical procedures.

Question 28

Level of Measurement: Nominal

Answer 28

Used to classify variables or events into categories. | I.e race/ethnicity, gender/sex, marital status

Question 29

Nominal Categories

Answer 29

Are mutually exclusive; the variable or event either has or does not have the characteristics. (If numbers are assigned to the category, they are only used as labels the numbers do not indicate more or less of a characteristic)

Question 30

Levels of Measurement: Ordinal

Answer 30

- Used to show relative rankings of variables or events. - The numbers assigned to each category can be compared and a member of a higher category can be said to have more of an attribute than a person in a lower category. - Intervals between numbers on the scale are not necessarily equal and there is no absolute zero.

Question 31

Example of ordinal measurement

Answer 31

Educational attainment (some high school, high school grad/GED, 4-year college degree, graduate degree)

Question 32

Levels of Measurement: Interval

Answer 32

-Shows rankings of events or variables on a scale with EQUAL intervals between the numbers. -Zero point remains arbitrary and not absolute. Ex. Temperature

Question 33

Levels of Measurement: Ratio

Answer 33

- Shows rankings of events or variables on scales with equal intervals and absolute zeros. - Numbers represents the actual amount of the property the object possesses; highest level of measurement. - All mathematical procedures can be used - Ex. Height, weight, pulse, BP, age

Question 34

Will most likely see on exam one: identify what level of measurement certain categories are?

Answer 34

I.e age, height, gender/sex, length of time it takes to insert an IV, GPA, FACES pain rating scale, etc.

Question 35

Pearson Product-Moment Correlation

Answer 35

Determines the relationship among variables

Question 36

Regression Analysis

Answer 36

Predicts the value of one variable when he value of one or more other variables is known.

Question 37

The following tests examine differences between or among groups, such as examine differences between experimental and control groups

Answer 37

- Chi-Square (nominal level data) - T-Test (interval or ratio level data) - Analysis of Variance (among three or more groups) - Analysis of Covariance (effects of confounding variables)