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Lesson 3: Experimental Research Designs

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PhD Research Seminar Series: Valid Research Designs

Andrea M. Landis, PhD, RN UW LEAH

Experimental and Quasi-Experimental Research

Defining Characteristics

GROUP-LEVEL DESIGNS Chapter 9.

Experimental Research Designs

Correlation AND EXPERIMENTAL DESIGN

Chapter 9 Experimental Research Gay, Mills, and Airasian

McGraw-Hill © 2006 The McGraw-Hill Companies, Inc. All rights reserved. Experimental Research Chapter Thirteen.

Experimental Research

EVALUATING YOUR RESEARCH DESIGN EDRS 5305 EDUCATIONAL RESEARCH & STATISTICS.

Experimental Research Take some action and observe its effects Take some action and observe its effects Extension of natural science to social science.

Experimental Design The Gold Standard?.

I want to test a wound treatment or educational program but I have no funding or resources, How do I do it? Implementing & evaluating wound research conducted.

Experimental and Quasi-Experimental Designs

I want to test a wound treatment or educational program in my clinical setting with patient groups that are convenient or that already exist, How do I.

Research Design for Quantitative Studies

Day 6: Non-Experimental & Experimental Design

Copyright ©2008 by Pearson Education, Inc. Pearson Prentice Hall Upper Saddle River, NJ Foundations of Nursing Research, 5e By Rose Marie Nieswiadomy.

Techniques of research control: -Extraneous variables (confounding) are: The variables which could have an unwanted effect on the dependent variable under.

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Experimental Research Designs - PowerPoint PPT Presentation

Experimental Research Designs

Note: bring measurement plan. in small groups. read each others measurement plans ... following the treatment, all participants are measured on the dependent variable ... – powerpoint ppt presentation.

• Note Bring measurement plan
• Read each others measurement plans
• How is/are the IV(s) measured?
• How is/are the DV(s) measured?
• How do the variables vary?
• Has the writer addressed reliability?
• Has the writer addressed validity?
• Can demonstrate cause-and-effect very convincingly
• Very stringent research design requirements
• Experimental design requires
• Random assignment to groups (experimental and control)
• Independent treatment variable that can be applied to the experimental group
• Dependent variable that can be measured in all groups
• Used in place of experimental research when random assignment to groups is not feasible
• Otherwise, very similar to true experimental research
• Explores the possibility of cause-and-effect relationships when experimental and quasi-experimental approaches are not feasible
• Used when manipulation of the independent variable is not ethical or is not possible
• External validityextent to which the results can be generalized to other groups or settings
• Population validitydegree of similarity among sample used, population from which it came, and target population
• Ecological validityphysical or emotional situation or setting that may have been unique to the experiment
• If the treatment effects can be obtained only under a limited set of conditions or only by the original researcher the findings have low ecological validity.
• Internal validityextent to which differences on the dependent variable are a direct result of the manipulation of the independent variable
• Historywhen factors other than treatment can exert influence over the results problematic over time
• Maturationwhen changes occur in dependent variable that may be due to natural developmental changes problematic over time
• Testingalso known as pretest sensitization pretest may give clues to treatment or posttest and may result in improved posttest scores
• Instrumentation Nature of outcome measure has changed.
• Regression Tendency of extreme scores to be nearer to the mean at retest
• Implementation-A group treated in an unintentional differential manner.
• Attitude-Hawthorne effect, compensatory rivalry.
• Differential selection of participantsparticipant s are not selected/assigned randomly
• Attrition (mortality)loss of participants
• Experimental treatment diffusion Control conditions receive experimental treatment.
• Commonly used experimental design notation
• X1 treatment group
• X2 control/comparison group
• O observation (pretest, posttest, etc.)
• R random assignment
• Single-group pretest-treatment-posttest design
• Technically, a pre-experimental design (only one group therefore, no random assignment exists)
• Overall, a weak design
• Two-group treatment-posttest-only design
• Here, we have random assignment to experimental, control groups
• A better design, but still weakcannot be sure that groups were equivalent to begin with
• Two-group pretest-treatment-posttest design
• A substantially improved designpreviously identified errors have been reduced
• Solomon four-group design
• A much improved designhow??
• One serious drawbackrequires twice as many participants
• Factorial designs
• Incorporates two or more factors
• Enables researcher to detect differential differences (effects apparent only on certain combinations of levels of independent variables)
• Single-participant measurement-treatment-measureme nt designs
• Purpose is to monitor effects on one subject
• Results can be generalized only with great caution
• Posttest-only design with nonequivalent groups
• Uses two groups from same population
• Questions must be addressed regarding equivalency of groups prior to introduction of treatment
• Pretest-posttest design with nonequivalent groups
• A stronger designpretest may be used to establish group equivalency
• Cause-and-effect relationship is hypothesized
• Participants are randomly assigned (experimental) or nonrandomly assigned (quasi-experimental)
• Application of an experimental treatment by researcher
• Following the treatment, all participants are measured on the dependent variable
• Data are usually quantitative and analyzed by looking for significant differences on the dependent variable
• Major recommendations for defining and operationalizing the instructional approach
• Avoid the nominal fallacy by carefully labeling and describing the independent variables
• Search for unanticipated effects that may be produced by the intervention
• Address assessment of implementation using standard checklists and in-depth methods
• Carefully document what happens in comparison classrooms
• Recommendations for probing the nature of the independent variable
• Provide a thorough description of samples
• Strive for random assignment
• Explore other alternative designs, such as formative or design experiments
• Quasi-experiments need to be critically reviewed
• Pretest variables should not show large differences (.5sd)
• Thorough sample description and analysis of comparison groups is essential.
• Recommendations regarding the use of dependent measures
• Select some measures that are not aligned tightly to the intervention
• Ensure that all measures are not experimenter developed and that some have been validated in prior research.
• Seek a balance between global and specific measures
• Look at intervention research as an opportunity to really build understanding of measures
• The importance of replication
• Researchers not interested in development of the independent variable should be involved
• What information does the public want from a School Report Card? (Adapted from Osowski)
• Does dual language instruction result in academic achievement?
• Chapter Eleven
• Refer to certain procedures that allow researchers to make inferences about a population based on data obtained from a sample.
• Obtaining a random sample is desirable since it ensures that this sample is representative of a larger population.
• The better a sample represents a population, the more researchers will be able to make inferences.
• Making inferences about populations is what Inferential Statistics are all about.
• It is reasonable to assume that each sample will give you a fairly accurate picture of its population.
• However, samples are not likely to be identical to their parent populations.
• This difference between a sample and its population is known as Sampling Error.
• Furthermore, no two samples will be identical in all their characteristics.
• There are times where large collections of random samples do pattern themselves in ways that will allow researchers to predict accurately some characteristics of the population from which the sample was taken.
• A sampling distribution of means is a frequency distribution resulting from plotting the means of a very large number of samples from the same population
• The standard deviation of a sampling distribution of means is called the Standard Error of the Mean (SEM).
• If you can accurately estimate the mean and the standard deviation of the sampling distribution, you can determine whether it is likely or not that a particular sample mean could be obtained from the population.
• To estimate the SEM, divide the SD of the sample by the square root of the sample size minus one.
• A Confidence Interval is a region extending both above and below a sample statistic within which a population parameter may be said to fall with a specified probability of being wrong.
• SEMs can be used to determine boundaries or limits, within which the population mean lies.
• If a confidence interval is 95, there would be a probability that 5 out of 100 (population mean) would fall outside the boundaries or limits.

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QUANTITATIVE RESEARCH METHODS AND DESIGN

AN OVERVIEW OF THE QUANTITIATIVE RESEARCH METHODS. EXPERIMENTAL RESEARCH. SINGE SUBJECT RESEARCH. CORRELATIONAL RESEARCH. CAUSAL COMPARATIVE RESEARCH. DESCRIPTIVE RESEARCH. RESEARCH TOOL AND OBJECTIVES

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30+ Best Research Presentation Templates for PowerPoint (PPT)

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Experimental RESEARCH DESIGNS

Feb 10, 2012

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Experimental RESEARCH DESIGNS. This presentation was prepared by Del Siegle. Some of the material is from an earlier presentation by Scott Brown. Experimental Research Designs have Two Purposes:. …to provide answers to research questions ...to control variance (differences).

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• experimental treatment diffusion
• student sensitivity
• internal validity
• selection bias differential selection
• minimizing error variance
• research hypothesis

Presentation Transcript

ExperimentalRESEARCHDESIGNS This presentation was prepared by Del Siegle. Some of the material is from an earlier presentation by Scott Brown.

Experimental Research Designs have Two Purposes: • …to provide answers to research questions • ...to control variance (differences)

The main function of the experimental research design is to control variance. • Principle: maximize systematic variance, control extraneous systematic variance, and minimize error variance. In other words control variance.

Therefore the researcher attempts to: • maximize the variance of the variable(s) of the research hypothesis (i.e., maximize the difference in the dependent variable [outcome] caused by maximizing the differences in the independent variable [treatment]). • control the variance of extraneous or "unwanted" variables that may have an effect on the experimental outcomes, but which he/she is not interested (limit factors other than the treatment (IV) that could be causing differences in the outcome (DV) . • minimize the error or random variance (i.e., avoid unreliable measurement instruments which have high errors of measurement ).

Maximization of Experimental Variance • experimental variance • the variance due to the manipulated (i.e., treatment) or attribute (i.e., gender) variables (IV) research precept: • design, plan and conduct research so that experimental conditions are as different as possible on the independent variable.

Control of Extraneous Variables (EV) • eliminate the variable (i.e., if you are worried about gender, only include one gender in the study). • randomization (i.e., if you randomly assign subjects to groups, the extraneous variable should be equally distributed among the groups) • build it into the design – make it a moderator variable (i.e., if you are worried about gender, build it into the analysis [2-way ANOVA]—we’ll learn about this later) • match subjects (i.e., match the characteristics of subjects and put one of each matched pair in each group) • statistically equate groups (i.e., use ANCOVA [Analysis of Covariance] to analyze the data with the extraneous variable used as a covariate—we’ll learn about this later)

Minimizing Error Variance has Two Principle Aspects: • reduction of errors of measurement through controlled conditions (i.e., standardize testing procedures) • increase in the reliability of measures (i.e., revise test instruments or find more reliable ones)

Experimental Designs Should be Developed to Ensure Internal and External Validity of the Study

Internal Validity: • Are the results of the study (DV) caused by the factors included in the study (IV) or are they caused by other factors (EV) which were not part of the study? There are 16 common threats to internal validity.

Threats to Internal Validity Subject Characteristics (Selection Bias/Differential Selection) -- The groups may have been different from the start. If you were testing instructional strategies to improve reading and one group enjoyed reading more than the other group, they may improve more in their reading because they enjoy it, rather than the instructional strategy you used.

Threats to Internal Validity Loss of Subjects (Mortality) -- All of the high or low scoring subject may have dropped out or were missing from one of the groups. If we collected posttest data on a day when the honor society was on field trip at the treatment school, the mean for the treatment group would probably be much lower than it really should have been.

Threats to Internal Validity Location Perhaps one group was at a disadvantage because of their location.  The city may have been demolishing a building next to one of the schools in our study and there are constant distractions which interfere with our treatment.

Threats to Internal Validity The testing instruments may not be scores similarly. Perhaps the person grading the posttest is fatigued and pays less attention to the last set of papers reviewed. It may be that those papers are from one of our groups and will received different scores than the earlier group's papers Instrumentation Instrument Decay

Threats to Internal Validity The subjects of one group may react differently to the data collector than the other group. A male interviewing males and females about their attitudes toward a type of math instruction may not receive the same responses from females as a female interviewing females would. Data Collector Characteristics

Threats to Internal Validity The person collecting data my favors one group, or some characteristic some subject possess, over another. A principal who favors strict classroom management may rate students' attention under different teaching conditions with a bias toward one of the teaching conditions. Data Collector Bias

Threats to Internal Validity Testing The act of taking a pretest or posttest may influence the results of the experiment. Suppose we were conducting a unit to increase student sensitivity to prejudice. As a pretest we have the control and treatment groups watch Shindler's List and write a reaction essay. The pretest may have actually increased both groups' sensitivity and we find that our treatment groups didn't score any higher on a posttest given later than the control group did. If we hadn't given the pretest, we might have seen differences in the groups at the end of the study.

Threats to Internal Validity History Something may happen at one site during our study that influences the results. Perhaps a classmate dies in a car accident at the control site for a study teaching children bike safety. The control group may actually demonstrate more concern about bike safety than the treatment group.

Threats to Internal Validity There may be natural changes in the subjects that can account for the changes found in a study. A critical thinking unit may appear more effective if it taught during a time when children are developing abstract reasoning. Maturation

Threats to Internal Validity Hawthorne Effect The subjects may respond differently just because they are being studied. The name comes from a classic study in which researchers were studying the effect of lighting on worker productivity. As the intensity of the factory lights increased, so did the worker productivity. One researcher suggested that they reverse the treatment and lower the lights. The productivity of the workers continued to increase. It appears that being observed by the researchers was increasing productivity, not the intensity of the lights.

Threats to Internal Validity One group may view that it is in competition with the other group and may work harder than they would under normal circumstances. This generally is applied to the control group "taking on" the treatment group. The terms refers to the classic story of John Henry laying railroad track. John Henry Effect

Threats to Internal Validity The control group may become discouraged because it is not receiving the special attention that is given to the treatment group. They may perform lower than usual because of this. Resentful Demoralization of the Control Group

Threats to Internal Validity Regression (Statistical Regression) -- A class that scores particularly low can be expected to score slightly higher just by chance. Likewise, a class that scores particularly high, will have a tendency to score slightly lower by chance. The change in these scores may have nothing to do with the treatment.

Threats to Internal Validity The treatment may not be implemented as intended. A study where teachers are asked to use student modeling techniques may not show positive results, not because modeling techniques don't work, but because the teacher didn't implement them or didn't implement them as they were designed. Implementation

Threats to Internal Validity Someone may feel sorry for the control group because they are not receiving much attention and give them special treatment. For example, a researcher could be studying the effect of laptop computers on students' attitudes toward math. The teacher feels sorry for the class that doesn't have computers and sponsors a popcorn party during math class. The control group begins to develop a more positive attitude about mathematics. Compensatory Equalization of Treatment

Threats to Internal Validity Experimental Treatment Diffusion Sometimes the control group actually implements the treatment. If two different techniques are being tested in two different third grades in the same building, the teachers may share what they are doing. Unconsciously, the control may use of the techniques she or he learned from the treatment teacher.

Once the researchers are confident that the outcome (dependent variable) of the experiment they are designing is the result of their treatment (independent variable) [internal validity], they determine for which people or situations the results of their study apply [external validity].

External Validity: • Are the results of the study generalizable to other populations and settings? External validity comes in two forms: population and ecological.

Threats to External Validity (Population) Population Validity is the extent to which the results of a study can be generalized from the specific sample that was studied to a larger group of subjects. It involves... • ...the extent to which one can generalize from the study sample to a defined population--If the sample is drawn from an accessible population, rather than the target population, generalizing the research results from the accessible population to the target population is risky. • ...the extent to which personological variables interact with treatment effects--If the study is an experiment, it may be possible that different results might be found with students at different grades (a personological variable).

Threats to External Validity (Ecological) Ecological Validity is the extent to which the results of an experiment can be generalized from the set of environmental conditions created by the researcher to other environmental conditions (settings and conditions). There are 10 common threats to external validity.

Explicit description of the experimental treatment Threats to External Validity (Ecological) (not sufficiently described for others to replicate) If the researcher fails to adequately describe how he or she conducted a study, it is difficult to determine whether the results are applicable to other settings.

Threats to External Validity (Ecological) Multiple-treatment interference (catalyst effect)If a researcher were to apply several treatments, it is difficult to determine how well each of the treatments would work individually. It might be that only the combination of the treatments is effective.

Threats to External Validity (Ecological) Hawthorne effect (attention causes differences)Subjects perform differently because they know they are being studied. "...External validity of the experiment is jeopardized because the findings might not generalize to a situation in which researchers or others who were involved in the research are not present" (Gall, Borg, & Gall, 1996, p. 475)

Threats to External Validity (Ecological) Novelty and disruption effect (anything different makes a difference)A treatment may work because it is novel and the subjects respond to the uniqueness, rather than the actual treatment. The opposite may also occur, the treatment may not work because it is unique, but given time for the subjects to adjust to it, it might have worked.

Threats to External Validity (Ecological) • (it only works with this experimenter)The treatment might have worked because of the person implementing it. Given a different person, the treatment might not work at all. Experimenter effect

Threats to External Validity (Ecological) Pretest sensitization (pretest sets the stage)A treatment might only work if a pretest is given. Because they have taken a pretest, the subjects may be more sensitive to the treatment. Had they not taken a pretest, the treatment would not have worked.

Threats to External Validity (Ecological) Posttest sensitization (posttest helps treatment "fall into place")The posttest can become a learning experience. "For example, the posttest might cause certain ideas presented during the treatment to 'fall into place' " (p. 477). If the subjects had not taken a posttest, the treatment would not have worked.

Threats to External Validity (Ecological) Interaction of history and treatment effect (...to everything there is a time...)Not only should researchers be cautious about generalizing to other population, caution should be taken to generalize to a different time period. As time passes, the conditions under which treatments work change.

Threats to External Validity (Ecological) Measurement of the dependent variable (maybe only works with M/C tests)A treatment may only be evident with certain types of measurements. A teaching method may produce superior results when its effectiveness is tested with an essay test, but show no differences when the effectiveness is measured with a multiple choice test.

Threats to External Validity (Ecological) Interaction of time of measurement and treatment effect (it takes a while for the treatment to kick in)It may be that the treatment effect does not occur until several weeks after the end of the treatment. In this situation, a posttest at the end of the treatment would show no impact, but a posttest a month later might show an impact.

First, and foremost, an experiment must have internal validity. If the researchers cannot be certain that the results of the experiment are dependent on the treatment, it does not matter to which people or situations they wish to generalize (apply) their findings. The importance of external validity is reliant on having internal validity in much the same way that the validity of a measurement instrument is reliant on the instrument being reliable. However, the more tightly experimenters design their study, the more they limit the populations and settings to whom they can generalize their findings.

The next section will describe different research designs.

Suppose a researcher wants to study the effect of a reading program on reading achievement. She might implement the reading program with a group of students at the beginning of the school year and measure their achievement at the end of the year. X O This simple design is known as a one-shot case study design.

Unfortunately, the students’ end of year reading scores could be influenced by other instruction in school, the students’ maturation, or the treatment. We also do not know whether the students’ reading skills actually changed from the start to end of the school year. We could improve on this design by giving a pretest at the start of the study. O X O This is known as a one-group pretest-posttest design.

O O Unfortunately, the students’ end of year reading scores still could be influenced by other instruction in school, the students’ maturation, or the treatment. Our researcher may wish to have a comparison group. O X O This is a static-group pretest-posttest design.

If our researcher believes that the pretest has an impact on the results of the study, she might not include it. O X O O O This is a static-group comparison design.

Because our researcher did not pretest, she might wish to randomly assign subjects to treatment and control group. Random assignment of subject to groups should spread the variety of extraneous characteristics that subjects possess equally across both groups. R O X O R O O This is a randomized posttest-only, control group design.

O O Of course, our researcher could also include a pretest with her random assignment. R X O R O This is a randomized pretest-posttest control group design.

R O X O R O O R X O R O Occasionally researchers combine the randomized pretest-posttest control group design with the randomized posttest-only, control group design. This is a randomized Solomon four-group design.

R O O R O O R O R O and given the posttest. With the randomized Solomon four-group design, all groups are randomly assigned Two of the groups are given pretests. X One of the pretest groups is assigned to treatment and one of the non-pretest groups is assigned to treatment. X

Each of the designs described in this section has advantages and disadvantages that influence the studies internal and external validity. This presentation was prepared by Del Siegle. Some of the material is from an earlier presentation by Scott Brown.

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