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| The shape of a relationship. Depending on the functional relationship between the independent and dependent variable, a graph of the relationship might look like a straight line or might look like a U, an S, or some other shape. |
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| A relationship between an independent and dependent variable that is graphically represented by a straight line. |
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| An experiment that examines two or more independent variable (factors) at a time. |
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variables, other than the independent variable, that may b responsible for the differences between your conditions.
1. ones that are manipulation irrelevant and are harmful to internal validity
2. ones that are the result of manipulation and hurt the construct validity |
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| When participants alter their behavior to conform to their guess as to what the research hypothesis is. Hypothesis-guessing can be a serious threat to construct validity, especially if participants guess right. |
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| A group that does not get any kind of treatment. The group gets nothing, not even a placebo. Usually, because of participant and experimenter biases that may result from such a group, you will want to avoid using an empty control group. |
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| an index of the degree to which group means differ. An index of the combined effects of random error and treatment. This quantity is compared to the within-groups variance in ANOVA. It is the top have of the F ration. If the treatment has no effect, the between-groups variance should be roughly the same as the within-groups variance. If the treatment has an effect, the between-groups variance should be larger than the within-groups variance. |
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| an estimate of the amount of random error in your data. The bottom half of the F ration in a between-subjects ANOVA |
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| analysis of variance (ANOVA) |
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| a statistical test for analyzing data from experiments that is especially useful when the experiment has more than one independent variable or more that two levels of an independent variable. |
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| ANOVA yields an F ration for each main effect and interaction. In between-subjects experiments, the F ration is a ration of between-groups variance to within-groups variance. If the treatment has no effect, F will tend to be close to 1. |
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| an estimate of effect size that ranges from 0 to 1 and is comparable to r-squared. |
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| Usually refers to a statistical test that has been performed after an ANOVA has obtained a significant effect for a factor. Because the ANOVA says only that at least two of the groups differ from one another, post hoc tests are performed to find out which groups differ from one another. |
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| a type of post hoc test designed to determine whether a linear or curvilinear relationship is statistically significant (reliable.) |
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| the effects of of one independent variable at a specific level of a second independent variable. The simple main effect could have been obtained merely by doing a simple experiment. |
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| the overall or average effect of an independent variable. |
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| occurs when a relationship between two variables is affected by the amount of the third variable. |
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| crossover (disordinal) interaction |
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| When an independent variable has one kind of effect in the presence of one level of a second independent variable, but a different kind of effect in the presence of a different level of the second independent variable. |
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| A study that varies from the original study only in some minor aspect. |
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| The particular stimulus materials that are shown to two or more groups of participants. Researchers may use more than one stimulus set in a study so that they can see whether the treatment effect replicates across different stimulus sets. In those cases, stimulus sets would be replication factors. |
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| A factorial design in which, to boost power, participants are first divided into groups (blocks) on subject variable (ex: low IQ vs. high IQ). Then participants from each block are randomly assigned to experimental condition. Ideally, a blocked design will be more powerful than a simple, between subjects design. |
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| designs in which at least one factor is a within-subjects factor, and at least one factor is a between subjects factor. In mixed designs, all participants get all levels of the within subjects factors but different participants get different levels of the between subjects factors. |
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| combines the best aspects of matching and random assignment: it uses matching to reduce the effects of irrelevant variables, and it uses random assignment to establish internal validity. |
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| the ability to find differences between conditions. |
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| a statistical test used with interval or ration data to test differences between two conditions on a single dependent variable. Differs from the between-groups t test in that it is to be used only when you are getting two scores from each participant(within subjects design) or when you are using a matched-pairs design. |
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| An experimental design in which each participant is tested under more that one level of the independent variable. The sequence in which the participants receive the treatments is usually randomly determined. |
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| The place in the sequence when a treatment occurs. |
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| A big problem with within-subjects designs. The order in which the participant receives a treatment will affect how participants behave. |
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| The change in a score on a test resulting from previous practice with the test. In a within-subjects design, this improvement might be incorrectly attributed to receiving a treatment. |
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| decreased performance on a test due to being tired or less enthusiastic as a study continues. In a within-subjects design, this decrease in performance might be incorrectly attributed to a treatment. |
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Definition
| The effect of a treatment administered earlier in the experiment persists so long that it is even present while participants are receiving additional treatments. It is often a problem with single-subject designs because you do not know whether the participant’s behavior is due to the treatment or just administered or to a lingering effect of treatment administered some time ago. |
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Definition
| After getting several different treatments and performing the dependent variable task several times, participants may realize what the hypothesis is Sensitization is a problem in within subjects designs. |
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| Randomized within-subjects design |
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Definition
| As in all within-subjects designs, all participants receive more than one level or type of treatment. However to make sure that not every participant receives the series of treatments in the same sequence, the researcher randomly determines which treatments comes first, which comes second, and so on. In other words, participants all get the same treatments but they receive different sequences of treatments. |
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| Counterbalanced within-subjects design |
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Definition
| Design that gives the participants the treatments in different sequences these design balance out routine order effects. |
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| Participants who receive one series of treatments score differently than those participants who receive the same treatments in a different sequence when you have a sequence effect. |
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| changes in the treatment are accompanied by changes in the behavior. To establish causality, you must establish covariation. |
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| the casual factor comes before the change in behavior. Because the cause must come before the effect, researchers trying to establish causality must establish that the factor alleged to be the cause was introduced before the behavior changed. |
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| When the covariation observed between two variables is not due to the variables influencing each other, but because both are being influences by some third variable. |
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| Design that tries to establish causality by studying a single participant and arguing that the covariation between treatment and changes in behavior could not be due to anything other that the treatment. |
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| The simplest single-n design, consisting of measuring the participant's behavior at a baseline (A) and then measuring the participant after the participant has received the treatment. |
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| When the participant's behavior, prior to receiving treatment, is consistent. |
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| The participant's behavior on the task before receiving the treatment. A measure of the dependent variable as it occurs without the experimental manipulation. Used as a standard of comparison in single-subject and small-n designs |
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| changes in participants due to natural growth or development. A researcher may think that the treatment had an effect when the difference in behavior is really due to maturation. |
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Term
reversal design
A-B-A design
A-B-A reversal design |
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Definition
| A single-subject or small-n design in which baseline measurements are made of the target behavior (A), then an experimental treatment is given (B), and the target behavior is measured again (A). |
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| A single-subject or small-n design in which different behaviors receive baseline periods of varying lengths prior to the introduction of the treatment variable. Often the goal is to show that the behavior being rewarded changes, whereas the other behaviors stay the same until they too are reinforced. |
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Definition
A study that resembles an experiment except that random assignment played no role in determining which participants got which level of treatment.
Have less internal validity |
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Definition
| A before-after design in which each participant is given the pretest, administered the treatment, and then given the posttest |
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| a quasi-experimental design in which a series of observations are taken from a group of participants before and after they receive treatment. Because it uses many times of measurement, it is an improvement over pretest-posttest design. However, it is still extremely vulnerable to history effects. |
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Term
| nonequivalent control-group design |
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Definition
| a quasi-experimental design that, like a simple experiment, has a treatment group and a no-treatment comparison group. However, unlike the simple experiment, random assignment does not determine which participants get the treatment and which do not. |
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Definition
| The assumption that the explanation that is simplest, most straightforward, and makes the fewest assumptions is most likely. |
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| The part of the article that occurs right after the abstract. In the intro, the authors tell you what their hypothesis is, why their hypothesis makes sense, how their study fits with previous research, and why their study was worth doing. |
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| A study investigation (exploring) a new area of research. Unlike replications, an exploratory study does not follow directly from an existing study. |
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| direct (exact) replication |
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Definition
| Repeating a study as exactly as possible, usually to determine whether or not the same results will be obtained. Direct replications are useful for establishing that the findings of the original study are reliable. |
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| A study that is based on the original study but uses different methods to assess the true relationships between the treatment and dependent variables better. You might use a different manipulation or a different measure. |
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| The part of the article immediately following the introduction. Explains how and what was done and why. |
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| The part of the article, immediately following the results section, that sidcusses the research findings and the study in a broader context and suggests research projects that could be done to follow up the study. |
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Definition
| The part of an article, immediately following the method section, that reports statistical results and relates those results to the hypotheses. From treading this section, you should know whether the results supported the hypothesis. |
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| probability value (p value) |
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Definition
| The chances of obtaining a certain pattern of results if there really is no relationship between the variables. |
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