Experimental Design

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Experimental Design Experimental Psychology Confounding Variables A variable that varies along with the independent variable. Confounding occurs when the effects of the IV and an uncontrolled variable are intertwined so you cannot determine which of the variables is responsible for the observed effect. Reminder: Internal Validity Basic Experiments • Posttest‐only Design 1. Two or more equivalent groups of participants • Selection Effects & Random Assignment 2. Introduce the Independent Variable • Control vs. Experimental; Amount 3. Measure the effect of the IV on the DV. Basic Experiments • Pretest‐Posttest Design The only difference is the addition of a pretest before the manipulation of the IV to ensure equivalent groups. Basic Experiments • Advantages & Disadvantages of Each Design 1. Sample Size vs. Randomization 2. Pretest may be necessary to equate groups 3. Mortality 4. Awareness of pretest intent? Demand characteristics! • Solomon Four Group Design Assigning Participants • Independent Groups Design (Between Subjects) – Random assignment to different levels of IV – Requires many participants – Controls for unknowns Assigning Participants • Repeated Measures Design (Within Subjects) – All participants experience all levels of IV – Less participants – Controls Random Error…more likely to find statistically significant results… if they exist – Order Effects & Carryover Effects • Practice, Fatigue, etc. Assigning Participants • Repeated Measures Design (Within Subjects) – Counterbalancing • Different Orders of Treatment Conditions Assigning Participants • Repeated Measures Design (Within Subjects) – Latin Squares A technique to control for order effects without having all possible orders. A limited set of orders is constructed to ensure that (1) each condition appears at each ordinal position and (2) each condition precedes and follows each condition one time. Assigning Participants • Repeated Measures Design (Within Subjects) – Time Between Treatments/Levels/Phases • Matched Pairs Design: Ensures that the groups are equivalent (on the matching variable) prior to introduction of the IV. Conducting Experiments.

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Conditional Process Analysis in Two-Instance Repeated-Measures Designs
Conditional Process Analysis in Two-Instance Repeated-Measures Designs Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Amanda Kay Montoya, M.S., M.A. Graduate Program in Department of Psychology The Ohio State University 2018 Dissertation Committee: Andrew F. Hayes, Advisor Jolynn Pek Paul de Boeck c Copyright by Amanda Kay Montoya 2018 Abstract Conditional process models are commonly used in many areas of psychol- ogy research as well as research in other academic fields (e.g., marketing, communication, and education). Conditional process models combine me- diation analysis and moderation analysis. Mediation analysis, sometimes called process analysis, investigates if an independent variable influences an outcome variable through a specific intermediary variable, sometimes called a mediator. Moderation analysis investigates if the relationship be- tween two variables depends on another. Conditional process models are very popular because they allow us to better understand how the processes we are interested in might vary depending on characteristics of different individuals, situations, and other moderating variables. Methodological developments in conditional process analysis have primarily focused on the analysis of data collected using between-subjects experimental designs or cross-sectional designs. However, another very common design is the two- instance repeated-measures design. A two-instance repeated-measures de- sign is one where each subject is measured twice; once in each of two instances. In the analysis discussed in this dissertation, the factor that differentiates the two repeated measurements is the independent variable of interest. Research on how to statistically test mediation, moderation, and conditional process models in these designs has been minimal.
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The Efficacy of Within-Subject Measurement (Pdf)
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M×M Cross-Over Designs
PASS Sample Size Software NCSS.com Chapter 568 M×M Cross-Over Designs Introduction This module calculates the power for an MxM cross-over design in which each subject receives a sequence of M treatments and is measured at M periods (or time points). The 2x2 cross-over design, the simplest and most common case, is well covered by other PASS procedures. We anticipate that users will use this procedure when the number of treatments is greater than or equal to three. MxM cross-over designs are often created from Latin-squares by letting groups of subjects follow different column of the square. The subjects are randomly assigned to different treatment sequences. The overall design is arranged so that there is balance in that each treatment occurs before and after every other treatment an equal number of times. We refer you to any book on cross-over designs or Latin-squares for more details. The power calculations used here ignore the complexity of specifying sequence terms in the analysis. Therefore, the data may be thought of as a one-way, repeated measures design. Combining subjects with different treatment sequences makes it more reasonable to assume a simple, circular variance-covariance pattern. Hence, usually, you will assume that all correlations are equal. This procedure computes power for both the univariate (F test and F test with Geisser-Greenhouse correction) and multivariate (Wilks’ lambda, Pillai-Bartlett trace, and Hotelling-Lawley trace) approaches. Impact of Treatment Order, Unequal Variances, and Unequal Correlations Treatment Order It is important to understand under what conditions the order of treatment application matters since the design will usually include subjects with different treatment arrangements, although each subject will eventually receive all treatments.
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Repeated Measures ANOVA
1 Repeated Measures ANOVA Rick Balkin, Ph.D., LPC-S, NCC Department of Counseling Texas A & M University-Commerce [email protected] 2 What is a repeated measures ANOVA? As with any ANOVA, repeated measures ANOVA tests the equality of means. However, repeated measures ANOVA is used when all members of a random sample are measured under a number of different conditions. As the sample is exposed to each condition in turn, the measurement of the dependent variable is repeated. Using a standard ANOVA in this case is not appropriate because it fails to model the correlation between the repeated measures: the data violate the ANOVA assumption of independence. Keep in mind that some ANOVA designs combine repeated measures factors and nonrepeated factors. If any repeated factor is present, then repeated measures ANOVA should be used. 3 What is a repeated measures ANOVA? This approach is used for several reasons: First, some research hypotheses require repeated measures. Longitudinal research, for example, measures each sample member at each of several ages. In this case, age would be a repeated factor. Second, in cases where there is a great deal of variation between sample members, error variance estimates from standard ANOVAs are large. Repeated measures of each sample member provides a way of accounting for this variance, thus reducing error variance. Third, when sample members are difficult to recruit, repeated measures designs are economical because each member is measured under all conditions. 4 What is a repeated measures ANOVA? Repeated measures ANOVA can also be used when sample members have been matched according to some important characteristic.
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Repeated Measures Analysis of Variance
NCSS Statistical Software NCSS.com Chapter 214 Repeated Measures Analysis of Variance Introduction This procedure performs an analysis of variance on repeated measures (within-subject) designs using the general linear models approach. The experimental design may include up to three between-subject terms as well as three within-subject terms. Box’s M and Mauchly’s tests of the assumptions about the within-subject covariance matrices are provided. Geisser-Greenhouse, Box, and Huynh-Feldt corrected probability levels on the within- subject F tests are given along with the associated test power. Repeated measures designs are popular because they allow a subject to serve as their own control. This improves the precision of the experiment by reducing the size of the error variance on many of the F-tests, but additional assumptions concerning the structure of the error variance must be made. This procedure uses the general linear model (GLM) framework to perform its calculations. Identical results can be achieved by using the GLM ANOVA program. The user input of this procedure is simply the GLM panel modified to allow a more direct specification of a repeated-measures model. We refer you to the GLM ANOVA chapter for details on the calculations and interpretations of analysis of variance. We will concentrate here on providing information specific to repeated measures analysis. An Example This section will give an example of a repeated-measures experiment. An experiment was conducted to study the effects of four drugs upon reaction time to a set of tasks using five subjects. Subject Drug 1 Drug 2 Drug 3 Drug 4 1 30 28 16 34 2 14 18 10 22 3 24 20 18 30 4 38 34 20 44 5 26 28 14 30 Discussion One way of categorizing experimental designs is as between subject or within subject.
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Experimental Design (Pdf 427Kb)
CHAPTER 8 1 EXPERIMENTAL DESIGN LEARNING OBJECTIVES 2 ✓ Define confounding variable, and describe how confounding variables are related to internal validity ✓ Describe the posttest-only design and the pretest- posttest design, including the advantages and disadvantages of each design ✓ Contrast an independent groups (between-subjects) design with a repeated measures (within-subjects) design ✓ Summarize the advantages and disadvantages of using a repeated measures design ✓ Describe a matched pairs design, including reasons to use this design 2 CONFOUNDING 3 ✓ Confounding variable: Varies along with the independent variable ✓ A goal of conducting a randomized experiment is to make a causal conclusion about the effect of an independent variable on a dependent variable. ✓ In order to make a causal conclusion, one must be able to rule out all other explanations for the effect on the dependent variable. ✓ Confounding occurs when the effects of the independent variable and an uncontrolled variable are intertwined ✓ Therefore, one cannot determine which variable is responsible for the effect 3 CONFOUNDING 4 ✓ Confounding variable ✓ Example: Testing was conducted on a new cold medicine with 200 volunteer subjects - 100 men and 100 women. The men receive the drug, and the women do not. At the end of the test period, the men report fewer colds. In this experiment many variables are confounded. Gender is confounded with drug use. Perhaps, men are less vulnerable to the particular cold virus circulating during the experiment, and the new medicine had no effect at all. Or perhaps the men experienced a placebo effect. This experiment could be strengthened with a few controls. Women and men could be randomly assigned to treatments.
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Chapter Fourteen Repeated-Measures Designs
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Study Designs and Their Outcomes
© Jones & Bartlett Learning, LLC © Jones & Bartlett Learning, LLC NOT FOR SALE OR DISTRIBUTION NOT FOR SALE OR DISTRIBUTION © Jones & Bartlett Learning, LLC © Jones & Bartlett Learning, LLC CHAPTERNOT FOR SALE 3 OR DISTRIBUTION NOT FOR SALE OR DISTRIBUTION © JonesStudy & Bartlett Designs Learning, LLC and Their Outcomes© Jones & Bartlett Learning, LLC NOT FOR SALE OR DISTRIBUTION NOT FOR SALE OR DISTRIBUTION “Natural selection is a mechanism for generating an exceedingly high degree of improbability.” —Sir Ronald Aylmer Fisher Peter Wludyka © Jones & Bartlett Learning, LLC © Jones & Bartlett Learning, LLC NOT FOR SALE OR DISTRIBUTION NOT FOR SALE OR DISTRIBUTION OBJECTIVES ______________________________________________________________________________ • Define research design, research study, and research protocol. • Identify the major features of a research study. • Identify© Jonesthe four types& Bartlett of designs Learning,discussed in this LLC chapter. © Jones & Bartlett Learning, LLC • DescribeNOT nonexperimental FOR SALE designs, OR DISTRIBUTIONincluding cohort, case-control, and cross-sectionalNOT studies. FOR SALE OR DISTRIBUTION • Describe the types of epidemiological parameters that can be estimated with exposed cohort, case-control, and cross-sectional studies along with the role, appropriateness, and interpreta- tion of relative risk and odds ratios in the context of design choice. • Define true experimental design and describe its role in assessing cause-and-effect relation- © Jones &ships Bartlett along with Learning, definitions LLCof and discussion of the role of ©internal Jones and &external Bartlett validity Learning, in LLC NOT FOR evaluatingSALE OR designs. DISTRIBUTION NOT FOR SALE OR DISTRIBUTION • Describe commonly used experimental designs, including randomized controlled trials (RCTs), after-only (post-test only) designs, the Solomon four-group design, crossover designs, and factorial designs.
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One-Way Correlated Samples Design Advantages and Limitations
12 - 1 Chapter 12. Experimental Design: One-Way Correlated Samples Design Advantages and Limitations Natural Pairs Matched Pairs Repeated Measures Thinking Critically About Everyday Information Comparing Two Groups Comparing t Test to ANOVA Correlated Samples t Test Correlated Samples ANOVA Comparing More Than Two Groups Case Analysis General Summary Detailed Summary Key Terms Review Questions/Exercises 12 - 2 Advantages and Limitations In the previous chapter, we included the powerful technique of random assignment in our research design to reduce systematic error (confounding variables). The assignment of participants to groups in a random fashion is one of the best ways to equate the groups on both known and unknown factors prior to administration of the independent variable. However, as we noted, there is no guarantee that they will be equated. To enhance experimental control, you may want to guarantee that one or more variables are equated among your treatment levels, and you may not want to rely on random assignment to establish that equality. Remember that any variable that varies systematically among your treatment levels and is not an independent variable is a confounder that can mask the effect of the independent variable. For example, in the previous chapter we discussed random assignment of children to two groups in a TV violence study. Random assignment, by chance, could result in having more boys in one group and more girls in the other group. If gender of the child is related to the dependent variable (aggressiveness), then we have created a confounding variable that will result in systematic error. As we will see, one advantage of the correlated samples designs discussed in this chapter is the reduction of systematic error between the treatment conditions.
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Chapter 13. Experimental Design: Multiple Independent Variables
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Understanding Repeated-Measures ANOVA
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