Quantitative Data Analysis Flashcards
Descriptive statistics
Summary statistics that allow the researcher to organize data in different ways that give meaning and facilitate insight
Inferential statistics
Statistics designed to allow in inference from a sample statistic to a population parameter
Levels of measurement
The assignment of numbers to variables or events according to statistical rules
Nominal
Classified in mutually exclusive categories, no ranking within the categories, ex gender, marital status, ethnicity
Ordinal
Data must be mutually exclusive and exhaustive and is sorted on the relative ranking of variables, ex education level
Interval
Mutually exclusive categories, exhaustive categories, and ranking order plus the distances between the intervals are numerically equal with no zero point on the interval scale
Ratio
Highest level of measurement mutually exclusive and exhaustive categories, ranking order, equal spacing between intervals, and a continuum of values; absolute zero exists
Frequency distribution
The number of times each event occurs is counted, and the data is then group according to categories
Normal distribution
A theoretical concept that observes that interval or ratio data group themselves around a midpoint in a distribution, closely approximating the normal curve
Positive skew
The mean of the distribution is almost always greater than it’s median
Negative skew
The mean of a distribution is almost always less than it’s median
Percentile
The percentage of cases a given score exceeds
Standard deviation
The average amount of variability in a set of scores from the mean, measure of how dispersed the data is
Inferential statistics
Combine mathematical processes with logic and allow researchers to test hypotheses about populations by using data obtained from probability samples
Parameter
A characteristic of a population
Statistic
A characteristic of a sample and is used to estimate population parameters
Parametric tests
Statistical procedures that can be used when: the sample from the population has a normal distribution, level of measurement must be interval or ratio, the sample is a tamed through a random sampling procedure
Non-parametric tests
Statistical procedures that can be used when: the sample does not have a normal distribution, level of measurement is nominal or ordinal, the sample was attained through non-random sampling
Type I error
Rejection of the null hypothesis when it is true
Type II error
Accepting the null hypothesis when it is false
Level of significance/alpha level
The probability of making a type I error
Practical significance
The value that the study contributes to practice
