Sampling Error
the difference between the observations in a population in the sample that represents that population in a study
Descriptive Statistics
measures that help us summarize data sets
Distribution
a set of scores
Central Tendency
the representation of a typical score in a distribution
Variability
the spread of scores in a distribution
Mean
calculated average of the scores in a distribution
Median
the middle score in a distribution, such that half of the scores are above and half are below that value
Mode
the most common score in a distribution
Outliers
extreme high or low scores in a distribution
Reaction Time
measurement of the length of time to complete a task
Range
the difference between the highest and lowest scores in a distribution
Standard Deviation
a measure representing the average difference between the scores and the mean of a distribution
Variance
the standard deviation of a distribution squared
Degrees of Freedom
number of scores that can vary in the calculation of a statistic
Frequency Distribution
a graph showing the frequency of each response in the distribution
Bar Graph
a graph showing height representing the size of the mean
Line graph
a graph where each point is graphed as a point which are connected to show differences between mean scores
Scatter Plot
a graph showing the relationship between two dependent variables for a group of individuals
Predictor Variable
the dependent variable in a correlational study that is used to predict the score on another variable
Outcome Variable
the dependent variable in a correlational study that is being predicted by the predictor variable
Inferential Statistics
a set of statistical procedures used by researchers to test hypotheses about populations
Scientific/Alternative Hypothesis
the hypothesis that an effect or relationship exists (or exists in a specific direction) in a population
Null Hypothesis
the hypotheses that an effect or relationship does not exist (or exists in the opposite direction of alternative hypothesis) in the population
Two-Tailed Hypothesis
both directions of an effect or relationship are considered in an alternative hypothesis of the test
One-Tailed Hypothesis
only one direction of an effect or relationship is predicted in the alternative hypothesis of the test
Distribution of Sample Means
the distribution of all possible sample means for all possible samples from a population
Alpha Level
the probability level used by researchers to indicate the cutoff probability level (highest value) that will allow them to reject the null hypothesis
P Value
probability value associated with an inferential test that indicates the likelihood of obtaining the data in a study when the null hypothesis is true
Significant Test
the p value is less than or equal to alpha in an inferential test, and the null hypothesis can be rejected
Critical Region
the most extreme portion of a distribution of statistical values for the null hypothesis determined by the alpha level (typically 5%)
Type I Error
error made in a significance test when the researcher rejects the null hypothesis when it is actually true
Type II Error
error made in a significance test when the researcher fails to reject the null hypothesis when it is actually false
Power
ability of a significance test to detect an effect or relationship when one exists (equal to 1�the probability of a Type II error)
How can we summarize a set of data to better understand it?
Data can be summarized with descriptive statistics. Measures of central tendency indicate a typical score in a distribution. Measures of variability indicate the spread of scores in a distribution. Graphs and tables also can provide a visual summary of th
What is the difference between the different measures of central tendency of distribution (mean, median, and mode)?
The mean is the average, the median is the middle score, and the mode is the most frequent score in the distribution.
How do inferential statistics allow us to learn about populations from the data collected from a sample?
Inferential statistics estimate sampling error to adjust for how well the sample represents the population in hypothesis tests. Then, an inferential statistic is calculated from the sample values with an estimate of sampling error included in the calculat
How do we make hypotheses about populations?
Null and alternative hypotheses about populations are stated for studies as either comparisons of conditions or predictions about relationships.
What can we learn from significance testing?
We can determine if there is enough evidence against the null hypothesis to reject it and conclude that the alternative hypothesis is true.
Correlational Study
a type of research design that examines the relationships between multiple dependent variables, without manipulating any of the variables
Descriptive Research Question
a research question that asks about the presence of behavior, how frequently it is exhibited, or whether there is a relationship between different behaviors
Predictive Research Question
a research question that asks if one behavior can be predicted from another behavior to allow predictions of future behavior
Predictor Variable
the dependent variable in a correlational study that is used to predict the score on another variable
Outcome Variable
the dependent variable in a correlational study that is being predicted by the predictor variable
Third-Variable Problem
the presence of extraneous factors in a study that affect the dependent variable can decrease the internal validity of the study
Positive Relationship
a relationship between variables characterized by coupled increases in the two variables
Negative Relationship
a relationship between variables characterized by an increase in one variable that occurs with a decrease in the other variable
What types of research questions can be answered with correlational studies?
Correlational studies can answer descriptive or predictive research questions.
What is the difference between correlational and causal hypotheses?
Hypotheses tested in correlational studies are about describing or predicting behavior. Causal hypotheses predict a specific cause of a behavior.
What aspects of correlational studies prohibit testing causal hypotheses?
Correlational studies do not contain independent variables that are manipulated by the researcher. Thus, the third-variable problem exists in correlational studies due to lack of sufficient control of extraneous factors.
How correlational studies allow us to predict behavior?
Regression analysis of the relationship between factors in a correlational study can test predictive relationships between variables.
Experiment
a type of research design that involves manipulation of an independent variable, allowing control of extraneous variables that could affect the results
Small-n Design
an experiment conducted with one or a few participants to better understand the behavior of those individuals
Independent Variable
a variable in an experiment that is manipulated by the researcher such that the levels of the variable change across or within subjects in the experiment
Confounding Variable
an extraneous factor present in a study that may affect the results
Random Assignment
participants are randomly assigned to levels of the independent variable in an experiment to control for individual differences as an extraneous variable
Order Effects
occur when the order in which the participants experience conditions in an experiment affects the result of the study
Testing Effects
occur when participants are tested more than once in a study, with early testing affecting later testing
Matched Design
a between-subjects experiment that involves sets of participants matched on a specific characteristic with each member of the set randomly assigned to a different level of the independent variable
Latin Square
partial counterbalancing technique where the number of orders of conditions used is equal to the number of conditions in the study
Factorial Design
an experiment or quasi-experiment that includes more than one independent variable
Levels of the Independent Variable
different situations or conditions that participants experience in an experiment because of the manipulation of the independent variable
Simple Effects Tests
statistical tests conducted to characterize an interaction effect when one is found in an ANOVA
What aspects of an experiment make it different from other research designs?
Experiments contain an independent variable that is manipulated and allows for control of alternative explanations of the results. Thus, experiments are the best research design for testing causal relationships.
What aspects of experiments allow for greater internal validity than other research designs?
The manipulation of a variable (independent variable) and the additional controls for confounding variables allow for greater internal validity in experiments than other types of research design.
What are the different ways that independent variables can be manipulated to allow comparisons of conditions?
Independent variables can be manipulated by type of something, amount of something, or the presence/absence of something. They can also be manipulated between subjects, where each participant receives only one level of the independent variable, or within
What is an interaction between independent variables?
An interaction can occur between independent variables such that the effect of one independent variable depends on which level of the other independent variable one is looking at. (Ex. An independent variable can show a difference between levels for Level
t Test
significance test used to compare means
One-sample t test
tests an effect. Use when the population mean, without the treatment, is known and is compared with a single sample
Independent samples t test
tests an effect. Use when two samples, with different individuals, are compared.
Repeated measures/paired samples t test
tests an effect. Use when two related samples, or two sets of scores from the same individual, are compared.
ANOVA
tests an effect. Use when more than two samples, or sets of scores from the same individual, are compared.
Pearson r test
tests a relationship. Use when the relationship between two sets of scores is being tested.
Regression
tests a relationship. Use when you want to predict an individual's score on one variable from the score on a second related variable
Degrees of Freedom
number of scores that can vary in the calculation of a statistic
Between-Subjects Variable
each participant experiences only one level of the independent variable
Quasi-Independent/Subject Variable
variable that allows comparison of groups of participants without manipulation (ex. No random assignment)
Homogeneity of Variances
assumption of between-subjects t tests and ANOVAs that the variance in the scores in the population is equal across groups
Within-Subjects Variable
each participant experiences all levels of the independent variable
Multivalent Variable
a variable that includes three or more levels�a design is considered multivalent if there is only one independent variable that contains three or more levels
Factoral Design
an experiment or quasi-experiment that includes more than one independent variable
Analysis of Variance (ANOVA)
analysis of variance test used for designs with three or more sample means
Main Effect
test of the differences between all means for each level of an independent variable in an ANOVA
Post Hoc Tests
additional significance tests conducted to determine which means are significantly different for a main effect
Interaction Effect
tests the effect of one independent variable at each level of another independent variable(s) in an ANOVA
Counterbalance
a control used in within-subjects experiments where equal numbers of participants are randomly assigned to different orders of the conditions
Sphericity Assumption
assumption of the repeated measures (within-subjects) ANOVA that pairs of scores in the population have equal variance
Chi-Square Test
a significance test used to determine if a relationship exists between two variables measured on nominal or ordinal scales
Pearson r Test
a significance test used to determine if a linear relationship exists between two variables measured on interval or ratio scales
Linear Regression
a statistical technique that determines the best fit line to a set of data to allow prediction of the score on one variable from the score on another variable
Which statistical tests are best for different research designs?
Different designs and measurement scales call for different types of statistical tests. (Look at the chart in figure 14.2 to see different tests)
How do we choose an appropriate statistical test?
A test is chosen based on (1) whether you are comparing means or looking for relationships, (2) what type of measurement scale was used to collect the data, and (3) the design of the study (the number of independent variables, the number of levels of the
How do we use SPSS to run statistical tests?
SPSS is a software package that allows researchers to compute descriptive and inferential statistics. Different inferential statistics require different types of tests to be run within the SPSS program.
What information is provided in an SPSS output?
SPSS output differs a bit from test to test, but the output for all tests described in this chapter include sample means and standard deviations, the statistical value (t, F, r, etc.), and the p value that corresponds to the statistic calculated.