Module 5 Flashcards
(86 cards)
Four levels of statistical measurement
Nominal
Ordinal
Interval
Ratio
Nominal Measurement
The lowest in the hierarchy of measurement scales.
Involves labelling or categorizing - the number does not suggest rank or ability, it is simply the means of identifying data.
Ordinal Measurement
Ranks events or objects on some attribute, assigning numbers to each category.
i.e., shortest to tallest, best student to worst student, etc.
Interval Measurement
Involves ranking events or variables on a scale in which the intervals between the numbers are equal and the zero value is arbitrarily set and does not have an absolute value.
i.e., Stanford-Binet Intelligence Scale
Ratio Measurement
The highest form of measurement.
Ratio measurement has a true zero on the scale.
i.e., time, weight, height
Statistical procedures such as calculating means and standard deviations are suitable for ratio-level data.
Descriptive Statistics
Used to describe and synthesize data.
Includes: frequency distributions, measures of central tendency, measures of variability.
Frequency Distributions
A systemic listing of all the values of a variable from the lowest to the highest with the number of times (frequency) each value was observed.
Often presented in the form of a table, graph, or frequency polygon.
Measures of central tendency
Measures to calculate an average.
Three types: mean, median, mode
Mean
The sum of a set of scores divided by the number of scores.
The most widely used measure of central tendency.
Median
The middle score.
The score of the point in a distribution above which one-half of the scores lie.
Mode
The score that occurs most frequently.
Best used with nominal data such as gender.
Measures of Variability
Used to describe the dispersion or the spread of data.
Appropriate for specific kinds of measurement and types of distributions.
Several measures: range, percentile, standard deviation
Range
The difference between the highest and the lowest scores in a distribution.
Percentile
Assigns the score to a specific place within the distribution.
Describes the number of cases a given score exceeds.
Standard Deviation
The most commonly used measure of variability.
The average amount that each of the individual scores varies from the mean of the set of scores.
Bivariate Descriptive Statistics
Allow a researcher to consider two variables together and describe the relationship between the variables.
Shows a statistical relationship between variables.
i.e., correlations and crosstabulations
Correlations
Tell the researcher to what extent the variables are related.
i.e., is there a relationship between smoking and lung capacity?
Correlation coefficient (r)
An index that describes the relationship between two variables.
Possible values range from -1.00 through .00 to +1.00
Positive Correlation
Indicates that high scores on one variable are paired with high scores on the other variable and low scores on one variable are paired with low scores on the other variable.
Negative Correlation
Indicates that low scores on one variable are paired with high scored on the other variable and high scores on one variable are paired with low scores on the other variable.
Inferential Statistics
Based on the law of probability.
Used to draw conclusions about the population on the basis of data obtained from the sample.
Purposes are to estimate the probability that the sample accurately reflects the population and to test hypotheses about the population.
Should be used when the sample is randomly selected and the measurement scale is at the interval or ratio area.
Sample
Used as a basis for making estimates of population characteristics.
Probability Samples
Selection of sample units by random selection.
Most effective means of securing representative samples.
Sampling Error
The variation in the statistical values that different samples of the population may present.
Will effect the statistical probability that the sample will accurately reflect the population.