pre experimental research design ppt

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2 Pre-Experimental Research Designs

• Published: February 2012
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The simplest of the group research designs involve the assessment of the functioning of a single group of persons who receive social work services. These methods are called pre-experimental designs. Tightly controlled studies done in laboratory or special treatment settings are known as efficacy studies, and are used to demonstrate if a given treatment can produce positive results under ideal conditions. Outcome studies done with more clinically representative clients and therapists, in real world agency settings, are known as effectiveness studies. Ideally the latter are conducted after the former, under conditions of increasing complexity, so as to determine treatments that work well in real-world contexts. Among the pre-experimental designs are the one group posttreatment-only study and the one group pretest-posttest design. Various ways in which these designs can be strengthened are presented, along with descriptions of published articles illustrating their use in social work and other human service settings. The limitations of these designs are also discussed, as is a review of the major threats to internal validity that can inhibit causal inferences.

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Building experimental research designs: the pre-study

Published by Kerrie Barton Modified over 6 years ago

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Presentation on theme: "Building experimental research designs: the pre-study"— Presentation transcript:

Andrea M. Landis, PhD, RN UW LEAH

Introduction to Research Methodology

Validity, Sampling & Experimental Control Psych 231: Research Methods in Psychology.

RESEARCH DESIGN.

Introduction to research Research designs Dr Naiema Gaber.

Evaluating a Research Report

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CHAPTER 2 Research Methods in Industrial/Organizational Psychology

Scientific Method Chapter 1-1. What is Science?  Science – organized way of gathering and analyzing evidence about the natural world  Described as a.

The Scientific Method. Scientifically Solving a Problem Observe Define a Problem Review the Literature Observe some More Develop a Theoretical Framework.

Research in Psychology A Scientific Endeavor. Goals of Psychological Research Description of social behavior Are people who grow up in warm climates different.

Research Methods Systematic procedures for planning research, gathering and interpreting data, and reporting research findings.

CHAPTER ONE EDUCATIONAL RESEARCH. THINKING THROUGH REASONING (INDUCTIVELY) Inductive Reasoning : developing generalizations based on observation of a.

Chapter 6 Selecting a Design. Research Design The overall approach to the study that details all the major components describing how the research will.

WHAT IS THE NATURE OF SCIENCE?

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Child Care and Early Education Research Connections

Pre-experimental designs.

Pre-experiments are the simplest form of research design. In a pre-experiment either a single group or multiple groups are observed subsequent to some agent or treatment presumed to cause change.

Types of Pre-Experimental Design

One-shot case study design, one-group pretest-posttest design, static-group comparison.

A single group is studied at a single point in time after some treatment that is presumed to have caused change. The carefully studied single instance is compared to general expectations of what the case would have looked like had the treatment not occurred and to other events casually observed. No control or comparison group is employed.

A single case is observed at two time points, one before the treatment and one after the treatment. Changes in the outcome of interest are presumed to be the result of the intervention or treatment. No control or comparison group is employed.

A group that has experienced some treatment is compared with one that has not. Observed differences between the two groups are assumed to be a result of the treatment.

Validity of Results

An important drawback of pre-experimental designs is that they are subject to numerous threats to their  validity . Consequently, it is often difficult or impossible to dismiss rival hypotheses or explanations. Therefore, researchers must exercise extreme caution in interpreting and generalizing the results from pre-experimental studies.

One reason that it is often difficult to assess the validity of studies that employ a pre-experimental design is that they often do not include any control or comparison group. Without something to compare it to, it is difficult to assess the significance of an observed change in the case. The change could be the result of historical changes unrelated to the treatment, the maturation of the subject, or an artifact of the testing.

Even when pre-experimental designs identify a comparison group, it is still difficult to dismiss rival hypotheses for the observed change. This is because there is no formal way to determine whether the two groups would have been the same if it had not been for the treatment. If the treatment group and the comparison group differ after the treatment, this might be a reflection of differences in the initial recruitment to the groups or differential mortality in the experiment.

Advantages and Disadvantages

As exploratory approaches, pre-experiments can be a cost-effective way to discern whether a potential explanation is worthy of further investigation.

Disadvantages

Pre-experiments offer few advantages since it is often difficult or impossible to rule out alternative explanations. The nearly insurmountable threats to their validity are clearly the most important disadvantage of pre-experimental research designs.

• Preferences

Pre and True Experimental Research Designs - PowerPoint PPT Presentation

Pre and True Experimental Research Designs

Internal and external validity and experimental design. controlling extraneous variables ... reactive arrangements (hawthorne effect) knowledge about the experiment ... – powerpoint ppt presentation.

• Pre- and True Experimental Research Designs
• Experimental Designs
• Internal and External Validity and Experimental Design
• Controlling Extraneous Variables
• Allow statements about cause and effect
• By controlling potential sources of variance
• The simplest experimental design
• Randomly selects subjects from population
• Experimental groupreceives treatment
• Control groupdoes not receive treatment
• No randomization
• Compare same subjects before and after treatment
• No control group
• Little ability to infer cause and effect
• Characteristics
• Random assignment
• Control group
• Three typical designs
• Pretest post-test control group design
• Post-test only control group design
• Solomon four-group design
• Groups should be equivalent at beginning
• Observed differences must result from treatment
• Sample is sufficient ( 30/group)
• Pre-testing is not possible
• Disadvantages
• If randomization is not effective, groups may not be equivalent
• Cannot use pretest to assign to groups
• Many different comparisons are possible
• Internal validityThe accuracy in concluding that the outcome of an experiment is due to the independent variable
• External validityThe extent to which the results of an experiment can be generalized
• HistoryUncontrolled outside influences on participants during an experiment
• MaturationChanges due to natural development
• SelectionBiased selection of participants
• TestingSensitization due to pretest
• InstrumentationBiases due to testing procedures
• RegressionThe tendency for extreme scorers to move toward more typical performance when retested
• MortalityChanges in group composition because some participants have left the study
• Multiple treatment interferenceSeveral treatments occur simultaneously
• Reactive arrangements (Hawthorne effect)Knowledge about the experiment
• Experimenter effectsEffects due to the presence of the experimenter
• Pretest sensitizationSensitization due to pretest
• Increasing internal validity
• Randomly select participants
• Randomly assign to groups
• Use a control group
• Increasing external validity
• Careful adherence to good experimental practices!
• Too much control reduces ability to generalize
• Too little control reduces ability to make causal statements
• Attempt to find a good balance
• Variables that are not accounted for can confound an experiment
• Controlling extraneous variables
• Ignore them if they are unrelated to the dependent variable
• Randomizing helps ensure that groups are equivalent
• Ensures that subjects in each group
• Are equivalent on some characteristic
• Should be related to the dependent measure
• Expensive and time consuming
• May not be possible
• Matching on some variables establishes equivalence on others
• Select sample from a population whose members are alike on critical factors
• A statistical tool that equalizes any initial differences that might exist
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Experimental research follows strict controls of the researcher. This type of research design is popular in scientific experiments, social sciences, medical science, etc. This is more likely field research rather than theoretical. It has several steps. The researcher must develop a research question, state a testable hypothesis, decide how to control variability during the experimental process, choose or develop intervention conditions, a sample from a population to assign them to experimental conditions, and decide what empirical measures will be taken (and how data will be recorded). There is frequently a close and crucial link between the experimental design, the type of data gathered, and the statistical method that will be utilized to evaluate the data

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In this editorial discussion we reflect on the issues addressed by, and arising from, the papers in this special issue on Single-Case Experimental Design (SCED) study methodology. We identify areas of consensus and disagreement regarding the conduct and analysis of SCED studies. Despite the long history of application of SCEDs in studies of interventions in clinical and educational settings, the field is still developing. There is an emerging consensus on methodological quality criteria for many aspects of SCEDs, but disagreement on what are the most appropriate methods of SCED data analysis. Our aim is to stimulate this ongoing debate and highlight issues requiring further attention from applied researchers and methodologists. In addition we offer tentative criteria to support decision-making in relation to the selection of analytical techniques in SCED studies. Finally, we stress that large-scale interdisciplinary collaborations, such as the current Special Issue, are necessary if SCEDs are going to play a significant role in the development of the evidence base for clinical practice.

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Research designs are either experimental or non-experimental. Experimental research is conducted mostly in laboratories in the context of basic research. The principle advantage of experimental designs is that it provides the opportunity to identify cause-and-effect relationships. Non-experimental research, e.g., case studies, surveys, correlation studies, is non-manipulative observational research usually conducted in natural settings. While laboratory-controlled experimental studies tend to be higher in internal validity, non-experimental studies tend to be higher in external validity.

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Chapter 10: Experimental Research

Jan 02, 2020

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Chapter 10: Experimental Research. Objectives: Briefly state the purpose of experimental research and list the basic steps involved in conducting and controlling an experiment. Briefly define internal validity and describe eight major threats to the internal validity of an experiment.

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Chapter 10: Experimental Research • Objectives: • Briefly state the purpose of experimental research and list the basic steps involved in conducting and controlling an experiment. • Briefly define internal validity and describe eight major threats to the internal validity of an experiment.

Chapter 10: Experimental Research • Objectives: • Briefly define external validity and describe six major threats to the external validity of an experiment. • Identify and briefly describe five ways to control extraneous variables.

Chapter 10: Experimental Research • Objectives: • Define and provide examples of single-variable designs (i.e., pre-experimental, true experimental, and quasi-experimental designs) and factorial designs, and explain how they differ.

Experimental Research • Experimental research is the only type of research that can test hypotheses to establish cause-effect relations. • The researcher manipulates at least one independent variable and controls other relevant variables, and observes the effect on one or more dependent variables. • The researcher manipulates the treatment. • The researcher has control over selection and assignment.

Experimental Research • In experimental research studies the independent variable is also called the treatment, causal, or experimental variable. • In experimental research studies the dependent variable is also called the criterion, effect, or posttest variable.

Experimental Research • Experimental research is the most structured of all research. • When conducted well, experimental research studies can provide evidence for cause-effect relations. • Several experimental studies taken together can provide support for generalization of results.

Experimental Research • The experimental process • The steps in the experimental research process are the same as in other types of research. • Selecting and defining a problem • Selecting participants and measuring instruments • Preparing a research plan • Executing procedures • Analyzing the data • Formulating conclusions

Experimental Research • The experimental process • In experimental studies, the researcher controls selection and assignment. • Experimental studies often examine comparisons between or among groups. • Comparison of approaches (A versus B) • Comparison of an approach to an existing approach (A versus no A) • Comparison of different amounts of a single approach (A little of A versus a lot of A)

Experimental Research • The experimental process • In experimental research studies the group that receives the treatment is the experimental group and the group that does not receive the treatment is called the control group. • Sometimes groups are comparison groups that receive alternative treatments (e.g., two types of instruction in a content area).

Experimental Research • Manipulation and control • In experimental studies, researchers control or remove the influence of extraneous variables. • Participant variables • Organismic (e.g., age) • Intervening (e.g., interest) • Environmental variables (e.g., school or teacher effects)

Threats to Experimental Validity • Internal validity refers to the degree to which observed differences in the dependent variable are a direct result of manipulation of the independent variable and not some other variable. • Internal validity is concerned with rival explanations for an effect.

Threats to Experimental Validity • External validity, sometimes referred to as ecological validity, is the degree to which the results from a study are generalizable to other groups. • When researchers increase the internal validity of their study, they decrease their external validity.

Threats to Experimental Validity • When researchers are concerned with external validity, their ability to control important extraneous variables suffers. • When there is a choice, researchers should err on the side of control and maximize internal validity.

Threats to Internal Validity • History • Maturation • Testing • Instrumentation • Statistical regression • Differential selection of participants • Mortality • Selection-maturation and interactive effects

Threats to Internal Validity • History • Any event occurring during a study that is not part of the experimental treatment but that may effect the dependent variable represents a history threat. • Longer-lasting studies are more prone to history threats.

Threats to Internal Validity • History threat example • In a study of the effects of instructional simulations in learning chemistry content, a history threat would be demonstrated if students in the study were exposed to simulations in a different setting, such as when learning geography, while the study was being conducted.

Threats to Internal Validity • Maturation • Maturation refers to physical, intellectual, and emotional changes that naturally occur within participants over a period of time.

Threats to Internal Validity • Maturation threat example • In studies of interventions that are designed to increase children’s theory of mind, if the interventions lasted more than a couple of weeks at critical time points, participants may gain critical theory of mind awareness simply due to cognitive development and not due to the treatment.

Threats to Internal Validity • Testing • Testing as a threat to internal validity is demonstrated when taking a pretest alters the result of a posttest.

Threats to Internal Validity • Instrumentation • Instrumentation is a threat to internal validity when the instrumentation is either unreliable or is changed between pre- and posttesting.

Threats to Internal Validity • Statistical regression • Extremely high or low scores tend to regress to the mean on retesting. • Statistical regression example • If students perform poorly on a pretest it is difficult to determine if the gain in their scores is due to treatment effects.

Threats to Internal Validity • Differential selection of participants • Participants in the control and experimental groups differ in ways that influence the dependent measure.

Threats to Internal Validity • Mortality • Participants drop out of the study at differential rates across conditions.

Threats to Internal Validity • Selection can interact with other threats to internal validity (i.e., history, maturation, instrumentation). • Participants selected into the treatment and control conditions have different experiences or maturation rates or instrumentation varies across conditions.

Threats to External Validity • External validity threats can be divided into two categories: • ‘Generalizing to whom’ threats • Threats affecting groups to which the study can be generalized • ‘Generalizing to what’ threats • Threats affecting the settings, conditions, variables, and contexts to which the results can be generalized

Threats to External Validity • Pretest-treatment interaction • Multiple-treatment interference • Selection-treatment interaction • Specificity of variables • Experimenter effects • Reactive arrangements

Threats to External Validity • Reactive arrangements • Hawthorne effect: Any situation in which participants’ behavior is affected because they are in a study. • John Henry effect (Compensatory rivalry): Members of the control group compete with the experimental group.

Threats to External Validity • Reactive arrangements • Placebo effect: To combat compensatory rivalry, researchers attempt to give control groups a placebo, not the experimental treatment, but something to decrease the perception that they are in the control group. Participants should perceive they are all getting the same thing. • Novelty effect: When participants are engaged in something different this may increase attention, interest, behavior, learning, etc., just because it is something new.

Group Experimental Designs • The validity of an experiment is a function of the degree to which extraneous variables are controlled. • Randomization is the best mechanism to control for extraneous variables. • Randomization distinguishes experimental designs. • Randomization should be used whenever possible. • If groups cannot be randomly formed, variables should be held constant when at all possible (e.g., time of day, which researcher is present).

Group Experimental Designs • Participant variables can be controlled and held constant • Matching can equate groups through random assignment of pairs. • Comparing homogeneous groups allows the researcher to control for extraneous variables.

Types of Group Designs • Single-variable designs are any design that involves one manipulated variable. • Pre-experimental designs do not adequately control for extraneous variables and should be avoided. • True-experimental designs offer a very high degree of control and are always preferred designs. • Quasi-experimental designs do not control as well as experimental designs but are preferable over pre-experimental designs.

Pre-Experimental Designs • The one-shot case study involves a single group that is exposed to a treatment (X) and then posttested (O). X O • Threats to validity are not adequately controlled with this design. • Do not use this design.

Pre-Experimental Designs • The one-group pretest-posttest design involves a single group that is pretested, exposed to treatment, and then tested again. O X O • The success of the treatment is determined by comparing pretest and posttest scores. • This design does not control for history, testing, instrumentation, regression, or maturation. • Statistical regression is not controlled nor is pretest-treatment interaction.

Pre-Experimental Designs • The static-group comparison design involves at least two nonrandomly formed groups. One group receives an experimental treatment and the other group receives the traditional treatment. Both groups are posttested. X1 O X2 O

Pre-Experimental Designs • The number of groups can be expanded beyond two. • The groups are better described as comparison, not experimental and control. • This design does not control for maturation, selection effects, selection interactions, and mortality.

Pre-Experimental Designs • There is some control for history in this design. • This design is sometimes used in exploratory studies.

True Experimental Designs • The pretest-posttest control group design requires at least two groups. • Groups are formed by random assignment. • Both groups are administered a pretest, each group receives a different treatment and both groups are posttested. • The design may be extended to include additional groups.

True Experimental Designs R O X1 O R O X2 O R O X3 O • The combination of random assignment and the presence of a pretest and a control group serve to control for all threats to internal validity.

True Experimental Designs • The only potential weakness in this design is a possible interaction between the pretest and the treatment. • Researchers should report assess and report the probability of a pretest-treatment interaction.

True Experimental Designs • There are a few variations on the basic pretest-posttest control group design. • One variation includes random assignment of matched pairs to the treatment groups. • There is little advantage to this variation. • Another variation of this design involves one or more additional posttests. R O X1 O O R O X2 O O

True Experimental Designs • The posttest-only control group design is the same as the pretest-posttest control group design except that it lacks a pretest. • This design is often expanded to include more than two groups. R X1 O R X2 O

True Experimental Designs • The posttest-only control group design is best used when there is likelihood of a pretest-treatment interaction threat. • As with the pretest-posttest control group design, the addition of a matched random assignment does not represent an increased advantage.

True Experimental Designs • The Solomon Four-Group Design is a combination of the pretest-posttest control group design and the posttest-only control group design. R O X1 O R O X2 O R X1 O R X2 O

True Experimental Designs • The analysis of the Solomon four-group design is a 2 x 2 factorial analysis of variance. • This analysis tests if those who received the treatment performed differently than those who did not. • This analysis can assess if there is a testing effect. • This analysis assesses for pretest-interaction effects.

True Experimental Designs • The Solomon four-group design requires a large number of participants. • The Solomon four-group design may not always be the best design. • The design selected should be based upon potential threats and the nature of the proposed study.

Quasi-Experimental Designs • When it is not possible to assign participants to groups randomly, researchers can use quasi-experimental studies. • In the nonequivalent control group design, two or more treatment groups are pretested, administered a treatment, and posttested. O X1 O O X2 O

Quasi-Experimental Designs • The nonequivalent control group designinvolves the random assignment of groups not individuals. • The lack of random assignment introduces validity threats (e.g., regression, and selection interaction effects). • To reduce threats when using this design researchers often assure groups are as equivalent as possible (e.g., use ANCOVA).

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Pre-experimental Design: Definition, Types & Examples

• October 1, 2021

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Experimental research is conducted to analyze and understand the effect of a program or a treatment. There are three types of experimental research designs – pre-experimental designs, true experimental designs, and quasi-experimental designs . 

In this blog, we will be talking about pre-experimental designs. Let’s first explain pre-experimental research. 

What is Pre-experimental Research?

As the name suggests, pre- experimental research happens even before the true experiment starts. This is done to determine the researchers’ intervention on a group of people. This will help them tell if the investment of cost and time for conducting a true experiment is worth a while. Hence, pre-experimental research is a preliminary step to justify the presence of the researcher’s intervention. 

The pre-experimental approach helps give some sort of guarantee that the experiment can be a full-scale successful study. 

What is Pre-experimental Design?

The pre-experimental design includes one or more than one experimental groups to be observed against certain treatments. It is the simplest form of research design that follows the basic steps in experiments. 

The pre-experimental design does not have a comparison group. This means that while a researcher can claim that participants who received certain treatment have experienced a change, they cannot conclude that the change was caused by the treatment itself. 

The research design can still be useful for exploratory research to test the feasibility for further study. 

Let us understand how pre-experimental design is different from the true and quasi-experiments:

The above table tells us pretty much about the working of the pre-experimental designs. So we can say that it is actually to test treatment, and check whether it has the potential to cause a change or not. For the same reasons, it is advised to perform pre-experiments to define the potential of a true experiment.

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Types of Pre-experimental Designs

Assuming now you have a better understanding of what the whole pre-experimental design concept is, it is time to move forward and look at its types and their working:

One-shot case study design

• This design practices the treatment of a single group.
• It only takes a single measurement after the experiment.
• A one-shot case study design only analyses post-test results.

The one-shot case study compares the post-test results to the expected results. It makes clear what the result is and how the case would have looked if the treatment wasn’t done. 

A team leader wants to implement a new soft skills program in the firm. The employees can be measured at the end of the first month to see the improvement in their soft skills. The team leader will know the impact of the program on the employees.

One-group pretest-posttest design

• Like the previous one, this design also works on just one experimental group.
• But this one takes two measures into account. 
• A pre-test and a post-test are conducted. 

As the name suggests, it includes one group and conducts pre-test and post-test on it. The pre-test will tell how the group was before they were put under treatment. Whereas post-test determines the changes in the group after the treatment. 

This sounds like a true experiment , but being a pre-experiment design, it does not have any control group. 

Following the previous example, the team leader here will conduct two tests. One before the soft skill program implementation to know the level of employees before they were put through the training. And a post-test to know their status after the training.

Now that he has a frame of reference, he knows exactly how the program helped the employees. 

Static-group comparison

• This compares two experimental groups.
• One group is exposed to the treatment.
• The other group is not exposed to the treatment.
• The difference between the two groups is the result of the experiment.

As the name suggests, it has two groups, which means it involves a control group too. 

In static-group comparison design, the two groups are observed as one goes through the treatment while the other does not. They are then compared to each other to determine the outcome of the treatment.

The team lead decides one group of employees to get the soft skills training while the other group remains as a control group and is not exposed to any program. He then compares both the groups and finds out the treatment group has evolved in their soft skills more than the control group. 

Due to such working, static-group comparison design is generally perceived as a quasi-experimental design too. 

Characteristics of Pre-experimental Designs

In this section, let us point down the characteristics of pre-experimental design:

• Generally uses only one group for treatment which makes observation simple and easy.
• Validates the experiment in the preliminary phase itself. 
• Pre-experimental design tells the researchers how their intervention will affect the whole study. 
• As they are conducted in the beginning, pre-experimental designs give evidence for or against their intervention.
• It does not involve the randomization of the participants. 
• It generally does not involve the control group, but in some cases where there is a need for studying the control group against the treatment group, static-group comparison comes into the picture. 
• The pre-experimental design gives an idea about how the treatment is going to work in case of actual true experiments.  

Validity of results in Pre-experimental Designs

Validity means a level to which data or results reflect the accuracy of reality. And in the case of pre-experimental research design, it is a tough catch. The reason being testing a hypothesis or dissolving a problem can be quite a difficult task, let’s say close to impossible. This being said, researchers find it challenging to generalize the results they got from the pre-experimental design, over the actual experiment. 

As pre-experimental design generally does not have any comparison groups to compete for the results with, that makes it pretty obvious for the researchers to go through the trouble of believing its results. Without comparison, it is hard to tell how significant or valid the result is. Because there is a chance that the result occurred due to some uncalled changes in the treatment, maturation of the group, or is it just sheer chance. 

Let’s say all the above parameters work just in favor of your experiment, you even have a control group to compare it with, but that still leaves us with one problem. And that is what “kind” of groups we get for the true experiments. It is possible that the subjects in your pre-experimental design were a lot different from the subjects you have for the true experiment. If this is the case, even if your treatment is constant, there is still going to be a change in your results. 

Advantages of Pre-experimental Designs

• Cost-effective due to its easy process. 
• Very simple to conduct.
• Efficient to conduct in the natural environment. 
• It is also suitable for beginners. 
• Involves less human intervention. 
• Determines how your treatment is going to affect the true experiment. 

Disadvantages of Pre-experimental Designs

• It is a weak design to determine causal relationships between variables. 
• Does not have any control over the research. 
• Possess a high threat to internal validity. 
• Researchers find it tough to examine the results’ integrity. 
• The absence of a control group makes the results less reliable. 

This sums up the basics of pre-experimental design and how it differs from other experimental research designs. Curious to learn how you can use survey software to conduct your experimental research, book a meeting with us . 

Pre-experimental design is a research method that happens before the true experiment and determines how the researcher’s intervention will affect the experiment.

An example of a pre-experimental design would be a gym trainer implementing a new training schedule for a trainee.

Characteristics of pre-experimental design include its ability to determine the significance of treatment even before the true experiment is performed.

Researchers want to know how their intervention is going to affect the experiment. So even before the true experiment starts, they carry out a pre-experimental research design to determine the possible results of the true experiment.

The pre-experimental design deals with the treatment’s effect on the experiment and is carried out even before the true experiment takes place. While a true experiment is an actual experiment, it is important to conduct its pre-experiment first to see how the intervention is going to affect the experiment.

The true experimental design carries out the pre-test and post-test on both the treatment group as well as a control group. whereas in pre-experimental design, control group and pre-test are options. it does not always have the presence of those two and helps the researcher determine how the real experiment is going to happen.

The main difference between a pre-experimental design and a quasi-experimental design is that pre-experimental design does not use control groups and quasi-experimental design does. Quasi always makes use of the pre-test post-test model of result comparison while pre-experimental design mostly doesn’t.

Non-experimental research methods majorly fall into three categories namely: Cross-sectional research, correlational research and observational research.

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