Module 4 Flashcards
Read chapter to clarify concepts in this because its going to be on exam
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Statistical Techniques
Analysis procedures to examine, reduce and give meaning to numerical data gathered in a study.
Descriptive statistics
Summary statistics that allow researcher to organize date in ways that give meaningful and facilitate insight.
Inferential statistics
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)
Elements of the Statistical Analysis Process include
- Management of missing data
- Description of the sample
- Reliability and validity of measurement methods
- Statistical analyses
Level of Statistical Significance
Probability level at which the results of the statistical analysis are judged to indicate a statistically significant difference between groups
P-Value (Probability Value)
- 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.
Layman terms: P-Value
You want to ensure your statistical results are not due to chance alone
Lower p-value
Less likely the results are due to chance and are more likely an indication of reality
High p-value
More likely the results are due to chance and are not a true indication of reality
If the p-value of the result is LESS than 0.05
It is considered statistically significant
This means the researcher can confidently assume that the results are not due to chance alone
Frequency distributions
Describes the occurrence of scores or categories in a study
Measures of central tendency
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.
What are the three common measures of central tendency?
Mean, Median and Mode
Mode
Numerical value/score that occurs with greatest frequency
Median
Midpoint or the score at the exact center of the ungrounded frequency distribution.
*Not affected by extreme scores (outliers)
Mean
Most commonly used measure - sum of scores divided by the number of scores being summed
*Extreme scores (outliers) affect this measure.
Measures of Dispersion/Variability
Measures of individual differences of the members in a sample.
Measures of Dispersion/Variability indicate
- 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
Dispersion/Variability: If individual scores are similar,
Measures of variability are small
Dispersion/Variability: If individual scores are dissimilar,
Measures of variability are larger
Common measures of dispersion/variability include
Range, Variance and Standard Deviation
Range
Simplest measures of Dispersion
Obtained by subtracting the lowest score from the highest score
Standard Deviation
Is the average difference value, provides a measure of the average deviation of a value from the mean in a particular sample.
Why is understanding levels of measurement important?
When evaluating the statistical analyses use in a study
Four levels of measurement include
Nominal
Ordinal
Interval
Ratio
The higher the level of measurement, the
The greater the flexibility the researcher has in choosing statistical procedures.
Level of Measurement: Nominal
Used to classify variables or events into categories.
I.e race/ethnicity, gender/sex, marital status
Nominal Categories
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)
Levels of Measurement: Ordinal
- 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.
Example of ordinal measurement
Educational attainment (some high school, high school grad/GED, 4-year college degree, graduate degree)
Levels of Measurement: Interval
-Shows rankings of events or variables on a scale with EQUAL intervals between the numbers.
-Zero point remains arbitrary and not absolute.
Ex. Temperature
Levels of Measurement: Ratio
- 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
Will most likely see on exam one: identify what level of measurement certain categories are?
I.e age, height, gender/sex, length of time it takes to insert an IV, GPA, FACES pain rating scale, etc.
Pearson Product-Moment Correlation
Determines the relationship among variables
Regression Analysis
Predicts the value of one variable when he value of one or more other variables is known.
The following tests examine differences between or among groups, such as examine differences between experimental and control groups
- 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)
