S
The complete set of individuals, objects, or scores that the investigator is interested in studying.
Population
Any property or characteristic of some event, object, or person that may have different values at different times depending on the conditions.
Variable
Variable that the investigator measures to determine the effect of the independent tvariable.
Dependent variable
The variable that is systematically manipulated by the investigator.
Independent variable
Subset of the Population
Sample
Measurements that are made on the subjects of an experiment
Data
Deals with the methods of organizing, summarizing, and presenting a mass of data so as to yield meaningful information.
Descriptive Statistics
A number calculated on the population that quantifies a characteristic of the population.
Parameter
Deals with making generalizations about a body of data where only a part is examined.
Inferential Statistics
A number calculated on sample data that quantifies a characteristic of the sample.
Statistic
Categorical Data Qualitative, lowest form, no order. Data consisting of names, labels, or categories only.
Nominal
Highest level of measurement Zero means nothing. Has an absolute zero value
Ratio
Collection, presentation, analysis, and interpretation of data
Statistics
Meaningful amounts of differences between data can be determined. Zero value is accepted.
Interval
Data are ranked-ordered Qualitative, order and hierarchy
Ordinal
Use this when you want to show how often a response is given.
Measures of Frequency
Measure of the amount of dispersion(variation) of a set of values
Standard Deviation
Difference between the highest and lowest values.
Range
Variance
Use this when you want to show how an average or most commonly indicated response.
Measures of Central Tendency
Describes how scores fall in relation to one another. Relies on standardized scores.
Use this when you need to compare scores to a normalized score.
Measures of Position
Quantitatively expresses the magnitude and direction of the relationship
Correlation
Use this when you want to show how "spread out" the data are.
Measures of Dispersion or Variation
Predicting the value of a variable with only one x and y variables
Linear Regression
Represents how many standard deviations a given measurement deviates from the mean