Final Exam Flashcards
(32 cards)
Qualitative variable
A variable whose levels are described with words or phrases. Examples include color (red, white, blue), gender (female, male), and size (small, medium, large).
Quantitative variable
A variable whose levels are described numerically. Examples include temperature, % body fat, and time.
Continuous variable
A quantitative variable that can be reduced to an infinite number of possible values, depending on the accuracy of the measuring instrument. Examples include height, weight, and distance.
Discrete variable
A variable, either qualitative or quantitative, with a finite number of levels that cannot be subdivided meaningfully. Examples include heart rate, IQ, and color.
Dependent variable
The outcome measure; the variable that is measured in a research study. It is affected by, or “dependent” on, the actions of other variables such as the independent variable(s).
Independent variable
A variable that you identify as having a potential influence on your outcome measure. This might be a variable that you control, like a treatment. It also might represent a demographic factor like age or gender.
Null hypothesis (H0)
The statistical hypothesis of no difference between means or no relationship between variables. It is the hypothesis that is tested.
Alternate hypothesis (H1)
A statistical hypothesis that offers an alternative to the null hypothesis when the null is rejected. This hypothesis may take on various forms depending upon the nature of the statistical test (t-test, ANOVA, correlation, etc) and the “direction” of the test (one or two tails).
Research hypothesis
The researcher’s educated guess as to the outcome of the study. In designs analyzed with t-tests, ANOVA, and correlation, the research hypothesis is typically related to the alternate statistical hypothesis.
Alpha level (α)
The probability of making a Type 1 error. The researcher sets this probability level as a criterion below which the null hypothesis will be rejected. It is typically set at α= 0.05.
Type 1 error
Rejecting a true null hypothesis. A Type 1 error occurs when the null hypothesis is rejected, indicating a significant difference or relationship, and the difference or relationship does not actually exist in the population. The probability of a Type 1 error is determined, or controlled by the alpha level. If the alpha level is set at 0.05, a Type 1 error should occur 5 times out of 100.
Type 2 error
Accepting, or failing to reject, a false null hypothesis. A Type 2 error occurs when the null hypothesis is retained (not rejected), indicating no difference or relationship, and the difference or relationship does actually exist in the population. The researcher cannot control the probability of a Type 2 error.
Inferential statistics
Inferential statistics are used to draw conclusions about a population based on information contained in a sample. Information is obtained from a sample and generalized to a population. In this category of statistics, conclusions are made with incomplete information.
Central Tendency
Mean, Median, and Mode
Mean
Arithmetic average of a set of scores
Median
Middle point in a set of scores
Mode
Most frequently occurring score in a set of scores
Variability
Standard deviation, Range, and Sum of squares
Standard Deviation
A measure of variability around the mean. The standard deviation is in the same units of measurement as the mean. For example, if the mean represents average time in seconds, the standard deviation represents variability in seconds.
Range
A measure of variability representing the distance from the highest score to the lowest score.
Critical value
A value obtained from a table of critical values specific to the test we are conducting. If the calculated value of our statistic exceeds the critical value, we will reject the null hypothesis.
Nominal Level of Measurement
Variables are categorical, qualitative, and discrete in nature. Although numbers can be used to represent levels of the variables, the numbers are treated as labels. Examples include brand of shoes, Social Security number, and gender.
Ordinal Level of Measurement
Variables are categorical and discrete in nature. Unlike variables at the nominal level, variable levels at the ordinal level of measurement can be rank-ordered meaningfully. Examples include finish position in a race (1st, 2nd, 3rd, . . .) and t-shirt size (S, M, L, XL).
Interval Level of Measurement
Variables at this level may be quantitative or qualitative, discrete or continuous. They possess the characteristics of ordinal level variables with the added characteristic of equal intervals between levels. Examples include temperature (F), shoe size, and IQ.
