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105 Cards in this Set
- Front
- Back
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Between-subjects design
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An experimental design in which different groups of subjects are exposed to the various levels of the independent variable.
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Within-subjects design
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An experimental design in which each subject is exposed to all levels of an independent variable.
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Single-subject design
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An experimental design that focuses on the behavior of an individual subject rather than groups of subjects.
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Error variance
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Variability in the value of the dependent variable that is related to extraneous variables and not to the variability in the independent variable
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Randomized two-group design
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A between-subjects design in which subjects are assigned to groups randomly
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Parametric design
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An experimental design in which the amount of the independent variable is systematically varied across several levels.
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Nonparametric design
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Experimental research design in which levels of the independent variable are represented by different categories rather than differing amounts.
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Multiple control group design
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Single-factor, experimental design that includes two or more control groups
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Matched groups design
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Between-subjects experimental design in which matched sets of subjects are distributed, at random, one per group across groups of the experiment
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Matched pairs design
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A two-group matched groups design
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Carryover effects
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A problem associated with within-subjects designs in which exposure to one level of the independent variable alters the behavior observed under subsequent levels.
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Counterbalancing
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A technique used to combat carryover effects in within-subjects designs. Counterbalancing involves assigning the various treatments of an experiment in a different order for different subjects.
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Factorial design
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An experimental design in which every level of one independent variable is combined with every level of every other independent variable
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Main effect
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The independent effect of one independent variable in a factorial design on the dependent variable. There are as many main effects as there are independent variables.
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Interaction
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When the effect of one independent variable on the dependent variable in a factorial design changes over the levels of another independent variable.
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Simple effects
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In a factorial analysis of variance (ANOVA), the effect of one factor at a given level (or a combination of levels) of another (or factors).
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Higher order factorial design
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Experimental design that includes more than two independent variables (factors).
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Mixed design
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An experimental design that includes between-subjects as well as within-subjects factors. Also called a split-plot design.
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Nested Design
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An experimental design with a within-subjects factor in which different levels of one independent variable are included under each level of a between-subjects factor.
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Covariate
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A correlational variable (usually a characteristic of the subject) included in an experiment to help reduce the error variance in statistical tests
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Quasi-independent variable
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A variable resembling the independent variable in an experiment, but whose levels are not assigned to subjects at random (the subject’s age, for example).
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Quasi-experimental design
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A design resembling an experimental design but using quasi-independent rather than true independent variables.
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Time series design
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A research design in which behavior of subjects in naturally occurring groups is measured periodically both before and after introduction of a treatment.
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Interrupted time series design
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A variation of the time series design in which changes in behavior are charted as a function of time before and after some naturally occurring event.
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Equivalent time samples design
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A variation of the time design in which a treatment is administered repeatedly, with each administration followed by an observation period.
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Nonequivalent control group design
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A time series design in which levels of the independent variable are represented by different categories rather than differing amounts
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Pretest—posttest design
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A research design that involves measuring a dependent variable (pretest), the introducing the treatment, and then measuring the dependent variable a second time (posttest).
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Solomon four-group design
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An expansion of the pretest-posttest design that includes control groups to evaluate the effects of administering a pretest on your experimental treatment.
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Cross-sectional design
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A developmental design in which participants from two or more age groups are measured at about the same time. Comparisons are made across age groups to investigate age-related changes in behavior
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Longitudinal design
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A developmental design in which a single group of subjects is followed over a specified period of time and measured at regular intervals
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Cohort-sequential design
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A developmental design including cross-sectional and longitudinal components
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Baseline design
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A single-subject experimental design in which subjects are observed under each of several treatment conditions. Observations made during baseline periods (no treatment) are compared with observations made during intervention periods (treatment introduced).
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Behavioral baseline
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Level of behavior under the baseline and intervention phases of a single-subject, baseline design. It is used to determine he amount of uncontrolled variability in the data.
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Stability criterion
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Criterion used to establish when a baseline in a single-subject, aseline design no longer shows any systematic trends. Once the criterion is reached, the subject is placed in the next phase of the experiment.
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Baseline phase
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Phase of a single-subject, basline design in which you establish the level of performance on the dependent measure before introducing the treatment.
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Intervention phase
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Phase of a single-subject, baseline design in which the treatment is introduced and the dependent measure evaluated
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ABAB design
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In a single-subject baseline design, the baseline (A) and intervention (B) phases are each repeated to provide an immediate intrasubject replication
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Intrasubject replication
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In a single-subject experiment, each treatment is repeated at least once for each subject and behavior is measured. This helps establish the reliability of the results obtained from a single-subject experiment.
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Reversal strategy
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Running a second baseline phase after the intervention phase in a single-subject, baseline design
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Intersubject replication
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The behaviors of multiple subjects used in a single-subject design are compared to establish the reliability of results
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Systematic replications
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Conducting a replication of an experiment while adding new variables for investigation
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Direct replications
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Exactly replicating an experiment. No new variables are included in the replication.
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Multiple-baseline design
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Simultaneously sampling several behaviors in a single-subject, baseline design to provide multiple baselines of behavior. Used if your independent variable produces irreversible changes in the dependent variable
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Dynamic design
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An experimental design in which the independent variable is varied continuously over time while monitoring the response of the dependent variable
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Discrete trials design
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A single-subject experimental design in which subjects receive each treatment condition dozens or hundreds of times. Each trial (exposure to a treatment) produces one data point, and data points are averaged across trials to provide stable estimates of behavior.
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Exploratory data analysis (EDA)
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Examining data for potentially important patterns and relationships, especially through the use of simple graphical techniques and numerical summaries.
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Dummy code
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In a data file, numbers used to stand for category values; for example, 0 = male, 1 = female.
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Bar graph
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A graph on which data from groups of subjects are represented by bars of differing heights tied to the value of the dependent variable for the group.
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Line graph
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A graph on which data relating the variables are plotted as points connected by lines.
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Scatterplot
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A plot used to display correlational data from two measures. Each point represents the two scores provided by each subject, one for each measure, plotted against one another.
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Pie chart
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Type of graph in which a circle is divided into segments. Each segment represents the proportion or percentage of responses falling in a given category of the dependent variable.
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Frequency distribution
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A graph or table displaying a set of values or a range of values of a variable, together with the frequency of each
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Histogram
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A graph depicting a frequency distribution in which the frequencies of class intervals are represented by adjacent bars along the scale of measurement.
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Stemplot
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A graphical display of a distribution of scores consisting of a column of values (the stems) representing the leftmost digit or digits of the scores and, aligned with each steam, a row of values representing the rightmost digit of each score having that particular stem value.
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Skewed distribution
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A frequency distribution in which most scores fall into categories above or below the middle category.
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Normal distribution
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A specific type of frequency distribution in which most scores fall around the middle category. Scores become less frequent as your move from the middle category. Also referred to as a bell-shaped curve.
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Outliers
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Values of a variable in a set of data that lie far from the other values.
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Resistant measures
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Statistics that are not strong affected by the presence of outliers or skewness in the data.
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Measure of center
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A single score, computed from a data set, that represents the general magnitude of the scores in the distribution
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Mode
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The most frequent score in a distribution. The least informative measure of center.
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Median
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The middle score in an ordered distribution.
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Mean
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The arithmetic average of the scores in a distribution. The most frequently reported measure of central tendency.
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Measure of spread
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A single score, computed from a data set, that represents the amount of variability of the scores in the distribution (i.e., how spread out they are).
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Range
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The least informative measure of spread; the difference between the lowest and highest scores in a distribution
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Interquartile range
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A measure of spread in which an ordered distribution of scores is divided into four groups. The score separating the lower 25 percent is subtracted from the score separating the upper 25 percent. The resulting difference is divided by 2.
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Variance
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A measure of spread. The averaged square deviation from the mean.
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Standard deviation
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The most frequently reported measure of spread. The square root of the variance
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Five-number summary
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A set of five numbers used to summarize the characteristics of a distribution: the minimum, first quartile, median, third quartile, and maximum.
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Boxplot
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A graphical display of the values of the five-number summary of a distribution
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Pearson product—moment correlation coefficient, or Pearson r
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The most popular measure of correlation. Indicates the magnitude and direction of a correlational relationship between variables
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Point-biserial correlation
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A variation of the Pearson correlation used when one variable can take on only two values.
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Spearman rank order correlation (rho)
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A measure of correlation used when variables are measured on at least an ordinal scale.
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Phi coefficient
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Measure of correlations used when both variables can take on only two values.
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Linear regression
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Statistical technique used to determine the straight line that best fits a set of data
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Bivariate linear regression
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A statistical technique for fitting a straight line to a set of data points representing the paired values of two variables.
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Least squares regression line
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Straight line, fit to data, that minimizes the sum of the squared distances between each data point and the line.
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Regression weight
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Value computed in a linear regression analysis that provides the slope of the least squares regression line. See also beta weight.
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Standard error of estimate
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A measure of the accuracy of prediction in a liner regression analysis. It is a measure of the distance between the observed data points and the least squares regression line.
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Coefficient of nondetermination
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Statistic indicating the proportion of variance in one variable not accounted for by variation in a second variable
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Correlation matrix
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A matrix giving the set of all possible bivariate correlations among three or more variables.
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Inferential statistics
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Statistical procedures used to infer a characteristic of a population based on certain properties of a sample drawn from that population
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Standard error of the mean
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An estimate of the amount of variability in expected sample means across a series of samples. It provides an estimate of the deviation between a sample mean and the underlying population mean.
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Degrees of freedom (df)
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The number of scores that are free to vary in a distribution of a given size having a known mean
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Type I error
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Deciding to reject the null hypothesis when, in fact, the null hypothesis is true. Also referred to as an alpha error.
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Type II error
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Deciding not to reject the null hypothesis when, in fact, the null hypothesis is false. Also referred to as a beta error.
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Alpha level
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The probability of obtaining a difference at least as large as the one actually obtained, given that the difference occurred purely as a result of chance factors. By convention, the maximum acceptable alpha level of .05 (5 chances in 100 or 1 change in 20).
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Critical region
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Portion of the sample distribution of a statistic within which observed values of the statistic are considered to be statistically significant. Usually the 5 percent of cases found in the upper and/or lower tail(s) of the distribution
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t test
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An inferential statistic used to evaluate the reliability of a difference between two means. Versions exist for between-subjects and within-subjects designs and for evaluating a difference between a sample mean and a population mean.
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t test for independent samples
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A parametric inferential statistic used to compare the means of two independent, random samples in order to assess the probability that the two samples came from populations having the same mean
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t test for correlated samples
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A parametric inferential statistic used to compare the means of two samples in a matched-pairs or within-subjects design in order to assess the probability that the two samples came from populations having the same mean.
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z test for the difference between two proportions
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A parametric inferential statistic used to determine the probability that two independent, random samples came from populations having the same proportion of “successes” (for example, persons favoring a particular candidate).
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Analysis of variance (ANOVA)
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An inferential statistic used to evaluate data from experiments with more than two levels of an independent variable or data from multifactor experiments. Versions are available for between-subjects and within-subjects designs.
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F ratio
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The test statistic computed when using an analysis of variance. It is the ratio of the between-groups variance and within-groups variance.
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p value
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In a statistical test, the probability, estimated from the data, that an observed difference in sample values arose through sampling error. p must be less than or equal to the chosen alpha level for the difference to be statistically significant/
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Planned comparisons
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Hypothesis-directed statistical tests made after finding statistical significance with an overall statistical test (such as ANOVA).
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Unplanned comparisons
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Comparison between means that is not directed by your hypothesis and is made after finding statistical significance with an overall statistical test (such as ANOVA).
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Per-comparison error
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The alpha level for each of any multiple comparisons made among means
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Familywise error
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The likelihood of making at least one Type I error across a number of comparisons
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Analysis of covariance (ANCOVA
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Variant of the analysis of variance used to analyze data from experiments that include a correlational variable (covariate).
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Chi-square
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Nonparametric inferential statistic used to evaluate the relationship between variables measured on a nominal scale.
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Mann—Whitney U test
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Nonparametric inferential statistic used to evaluate data from a two-group experiment in which the dependent variable was measured along at least an ordinal scale. It can also be used on interval or ratio data if the data do not meet the assumptions of the t test for independent samples
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Wilcoxon signed ranks test
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A nonparametric statistical test that can be used when the assumptions of the t test for correlated samples are seriously violated
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Power
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The ability of an experimental design or inferential statistic to detect an effect of a variable when one is present.
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Effect size
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The amount by which a given experimental manipulation changes the value of the dependent variable in the population, expressed in standard deviation units
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Data transformation
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Mathematical operation applied to raw data, such as taking the square root or arcsine of the original scores in a distribution. Often applied to data that violate the assumptions of parametric statistical tests, to help them meet those assumptions
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