Research Methods In Psychology Test 4

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Experimental designs in which only one independent variable is manipulated

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One-Way Designs.

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This is the simplest one-way design, in which there are only two levels of the independent variable.

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Two-Group Experimental Design

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A between subjects design in which participants are randomly assigned to one of two or more conditions.

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Randominzed Groups Design

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Participants are matched into blocks on the basis of a variable the researcher believes relevant to the experiment.

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Matched-Subjects Design

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Each participant serves in all experimental conditions.

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Repeated Measures Design

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The dependent variable is only measured after the experimental manipulation has occured

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Post-test Only Design

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Measure the dependent variable twice-before and after the independent variable is manipulated.

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Pretest-Posttest Design.

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One drawback of using pretests.

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Pretest Sensitization

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3 basic one-way designs.

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1) Randomized Groups Design
2) Matched-Subjects Design
3) Repeated Measures Design

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An experimental design in which 2 or more independent variables are manipulated.

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Factorial Design

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Often, the independent variables are referred to as:

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Factors

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A two-way factorial design has how many independent variables?

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2

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A 2x2x2 design has how many independent variables and how many levels?

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3 independent variables and 2 levels.

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Participants are assigned randomly to one of the possible combinations of the independent variables.

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Randomized Groups Factorial Design.

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In this design, first participants are matched into blocks on the basis of some variable that correlates with the dependent variable.

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Matched-Subjects Factorial Design.

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There will be as many participants in each matched block as there are:

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Experimental Conditions

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Requires all participants to participate in every experimental condition.

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Repeated Measures Design

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A design that combines one or more between-subjects variables with one or more within-subjects variables is called:

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Mixed Factorial Design, Between-Within Design, or Split-Plot Factorial Design.

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Primary advantage of factorial designs over one-way.

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Provide information not only about the separate effects of each independent variable but also about the effects of the independent variables combined.

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The effect of a single independent variable in factorial design.

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Main effect.

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This is present when the effect of one independent variable differs across the levels of other independent variables.

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Interaction

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Researchers seldom design studies with more than:

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Three or Four independent variables.

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Age, sex, intelligence, ability, personality and attitudes are examples of:

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Subject Variables.

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Called by the author...refers to design in which independent variables are manipulated and features of correlational designs in which subject variables are measured.

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Expericorr Factorial Designs.

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Procedure in which the researcher identifies the median of the distribution of participants' scores on the variable of interest.

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Median-Split Procedure.

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Selecting participants for the experiment whose pretest scores are unusually low or high on the variable of interest.

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Extreme Groups Procedure.
(often criticized and rarely used).

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The subject variable's effect on the independent variable is known as the

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Moderator Variable
(not causal, but moderating)

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Researchers use this to determine whether the observed differences between the means of the experimental conditions are greater than expected on the basis of error variance alone.

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Inferential Statistics.

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States that the independant variable did not have an effect on the dependent variable.

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Null Hypothesis.

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States the independent variable did have an effect.

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Experimental (or Research) Hypothesis.

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Means that the researcher will conclude that the independent variable did indeed have an effect.

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Rejecting The Null Hypothesis

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Means that the researcher will conclude that the independent variable had no effect.

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Failing to reject the null hypothesis.

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Occurs when a researcher erroneously concludes that the null hypothesis is false, and thus rejects it.

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Type I error.

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The probablility of making a Type I error.

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Alpha Level.

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When we reject the null hypothesis with a low probability of making a Type I error we refer to the difference between the means as

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Statistically Significant.

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Mistakenly fail to reject the null hypothesis when it is in fact false.

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Type II (incorrectly assuming the ind. variable has no effect when it actually does)

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The probability that a study will correctly reject the null hypothesis when the null hypothesis is false.

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Power.

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This is used to determine the number of participants that is needed in order to detect the effect of a particular independent variable.

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Power Analysis.

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The proportion of variability in the dependent variable that is due to the independent variable.

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Effect Size.

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Two statistical tests used most often to analyze data collected in experimental research.

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t-Tests and F-Tests

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States which of the two condition means is expected to be larger.

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Directional Hypothesis

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Merely states that the two means are expected to differ, but no prediction made regarding which will be larger.

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Nondirectional Hypothesis.

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When a researcher's prediction is directional this is used.

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One-tailed test.

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Used when the experiment involves a matched-subjects or within-subjects design.

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paired t-test

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Why do we use ANOVA?

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Because multiple t-tests inflate Type I error.

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Researchers sometimes use the ____________ in which they adjust their desired alpha level by the number of tests they plan to conduct.

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Bonferroni Adjustment

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A statistical procedure when researchers want to test differences among many means.

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Analysis of Variances (ANOVA)

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ANOVA is based on a statistical test called the ________.

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F-Test

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This is the ratio of the variance among conditions to the variance within conditions.

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F-Test

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This reflects the total amount of variability in a set of data.

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Sum of Squares

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In ANOVA, this is equal to the sum of the sums of squares for each of the experimental groups.

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Sum of Squares Within Groups

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SSwg reflects __________

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Error Variance

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What is the mean square within groups?

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MSwg = SSwg/dfwg

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dfwg = _________

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(n-k)

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The mean of all the group means.

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The Grand Mean

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dfbg=

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k-1

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SSbg/dfbg=

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MSbg

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F=

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MSbg/MSwg

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If our calculated F value exceeds the critical F value for our degrees of freedom we can then......

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Reject the null hypothesis

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To identify which means differ significantly researchers use ______

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follow-up tests, (post hoc tests, multiple comparisons)

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If an ____________ is significant, we know that the effects of one independent variable differ depending on the level of another independent variable.

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Interaction

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The effect of one independent variable at a particular level of another independent variable.

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Simple Main Effect.

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This tests the means between two different conditions

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t-test

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tests the differences among more than 2 conditions

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ANOVA

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Whereas an ANOVA tests differences among the means of two or more conditions on one dependent variable, a ____________, tests differences between the means of two or more conditions on two or more dependent variables simulaneously.

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Multivariate analysis of variance
MANOVA

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Why use MANOVA?

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1) researcher has measured several dependent variables, all which tap into the same construct.
2) To control Type I error

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What are the 3 advantages to a pre-test post-test design?

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-can determine that participants did not differ with respect to the dependent variable intially
-can determine how much the independent variable changed their behavior
-they are more powerful

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What is a between-within factorial design?

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A design that combines one or more between subjects variables with one or more within-subjects variables.

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Distinguish main effect and interaction.

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Main effect is the effect of a single independent variable in a factorial design. Interaction is present when the effect of one independent variable differs across the levels of the other independent variables.

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What is an expericorr factorial design?

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A design that combine features of an experimental design and features of a correlational design in which subject variables are measured.

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Why is it insufficient to simply inspect the condition means to det. whether or not the ind. variable affected scores on the dep. variable?

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The means may differ due to simple error or confound variance. This is the need for inferential statistics.

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What is Power?

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Power is the probability that a study will correctly reject the null hypothesis when it is false.

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Why do researchers desire high power?

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Higher power has a higher chance of detecting an effect of an independent variable.

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Define statistical significance.

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When we can reject the null hypothesis with a low probability of making a Type i error we say that the means are statistically significant.

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When would you use a paired t-test?

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When the experiment involves a matched-subjects design or a within-subjects design.

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When ANOVA is used to analyze data from experiments with one independent variable, the sum of squares is composed of what two parts?

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SSbg and SSwg

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When ANOVA is used to analyze data from experiments with two independent variables, the total sum of squares is composed of four parts. what are they?

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1. Error Variance
2. Main effect of A
3. Main effect of B
4. A x B interaction

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When the calculated value of F is less than the critical value of F, what decision does the researcher make regarding the null hypythesis.

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The researcher fails to reject the null hypothesis.

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What is an interaction?

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An interaction is present when the effect of one independent variable differs accross the levels of other independent variables.

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When do researchers do post-hoc tests?

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If ANOVA reveals a difference among 3 levels the significant main effect indicates that a difference exists, but doesn't say which. Post hoc tests such as LSD, Tukey's, etc. reveals this.