True Experimental Design PDF

True Experimental Design Group Assignment Submitted by Sadia Mustafa Muqaddas jamil Oreeba asghar Muniba sadaf MSCP-1 13-11-2024  Introduction to Experimental Design Experimental design is a stru...

True Experimental Design Group Assignment Submitted by Sadia Mustafa Muqaddas jamil Oreeba asghar Muniba sadaf MSCP-1 13-11-2024  Introduction to Experimental Design Experimental design is a structured approach to investigating cause-and-effect relationships. By manipulating one or more independent variables and observing the resulting changes in dependent variables, researchers can draw conclusions about how these variables interact. The primary purpose of experimental design is to ensure the reliability, validity, and replicability of results by minimizing biases and controlling extraneous variables. Key components of experimental design include: Independent Variable (IV): The variable that is intentionally manipulated. Dependent Variable (DV): The outcome or response measured to assess the effect of the IV. Control Variables: Factors that are kept constant to prevent them from influencing the DV. Randomization: Ensures that participants are assigned to experimental conditions by chance, reducing selection bias. Replication: Repeating the experiment to verify its results. Types of Experimental Design There are three basic types of experimental design 1. Pre-Experimental Design Definition: These are basic designs with minimal control over variables. They do not include randomization or a proper control group, making them less rigorous. Example: A single group is exposed to a treatment, and the outcome is measured (e.g., a teacher tests a new method with one class and evaluates their performance). 2. True Experimental Design: Definition: The most rigorous design, characterized by random assignment and the use of control groups. It provides strong internal validity and robust causal inference.  Example: Two groups are randomly assigned; one receives a new drug, and the other a placebo, and their health outcomes are compared. 3. Quasi-Experimental Design Definition: Similar to true experiments but lacks randomization. These designs are often used when random assignment is not feasible due to practical or ethical constraints. Example: Comparing test scores of students from two schools, one of which implements a new teaching strategy while the other continues with traditional methods. Difference Between True and Quasi-Experimental Design 1. Randomization: True experimental designs involve random assignment of participants to experimental and control groups, ensuring that each participant has an equal chance of being placed in any group. This reduces selection bias. Quasi-experimental designs do not use randomization, so the groups may differ systematically, introducing potential biases. 2. Control Group: True experiments always include a control group, providing a clear basis for comparing the treatment effect. Quasi-experiments may have a control or comparison group, but the lack of randomization weakens the ability to attribute differences in outcomes solely to the treatment. 3. Causal Inference: True experiments provide strong evidence for causality due to their rigorous design and control of confounding variables. In quasi-experiments, causal inference is less robust because potential con-founders may not be fully controlled. 4. Internal Validity:  True experiments have high internal validity, meaning the observed effects can be confidently attributed to the independent variable. Quasi-experiments have lower internal validity since other variables might influence the results. True experimental designs are often used in controlled environments, such as laboratories or clinical trials, where randomization is feasible. Quasi-experimental designs are more practical for real-world settings, like schools or workplaces, where random assignment might not be possible. A true experiment is a type of scientific study that aims to establish cause-and-effect relationships between variables. It involves the manipulation of an independent variable to observe its effect on a dependent variable, with the control of other extraneous factors to ensure accurate results. True experiments are often conducted in controlled environments, allowing researchers to determine the precise effects of specific variables. Characteristics of a True Experiment 1. Random Assignment: Participants are randomly assigned to different groups or conditions to eliminate selection bias, ensuring that differences between groups are due to the manipulation of the independent variable rather than pre-existing differences among participants. -Example: In a study examining the effect of a new teaching method on math performance, students are randomly assigned to either the experimental group (receiving the new teaching method) or the control group (traditional teaching). This random assignment helps ensure that any difference in math performance can be attributed to the teaching method. 2. Manipulation of the Independent Variable: The researcher actively changes or manipulates the independent variable to observe its effect on the dependent variable. -Example: A researcher studying the impact of sleep on cognitive performance manipulates the amount of sleep participants receive, assigning one group to sleep for 8 hours and another for 4 hours. The manipulation allows researchers to observe how changes in sleep duration impact cognitive test results.  3. Control Group: A group that does not receive the treatment or manipulation, used as a baseline for comparison to determine if the independent variable truly caused any change in the dependent variable. Example: In drug efficacy studies, a control group receives a placebo while the experimental group receives the actual drug. Any observed effects in the experimental group can then be attributed to the drug rather than other factors. 4. Control of Extraneous Variables: All other variables besides the independent variable are controlled or held constant to ensure they do not influence the dependent variable. Example: In a study on the effects of noise on concentration, researchers ensure that lighting, temperature, and other environmental factors remain the same across all conditions. This control minimizes the influence of extraneous factors on the results. Criteria for a True Experiment 1. Causation: True experiments aim to establish a causal relationship by showing that changes in the independent variable directly cause changes in the dependent variable. 2. Internal Validity: High internal validity is required to ensure that results are due to the independent variable rather than external or confounding variables. 3. Replicability: True experiments should be designed in a way that other researchers can replicate them under similar conditions to validate the results. Examples Medical Trials: Testing the efficacy of a new drug on blood pressure where participants are randomly assigned to either a treatment group (receiving the drug) or a control group (receiving a placebo). Researchers can observe the drug’s effects on blood pressure by comparing the two groups. Educational Research: An experiment to determine the impact of a new instructional approach on student learning outcomes, with random assignment of students to an experimental group (new approach) and a control group (traditional approach). This helps establish whether the instructional method has a direct impact on learning. 7 Steps to conduct a true experimental research It’s important to understand the steps/guidelines of research in order to maintain research integrity and gather valid and reliable data. We have explained 7 steps to conducting this research in detail. i. Identify the research objective. ii. Identify independent and dependent variables. iii. Define and group the population. iv. Conduct Pre-tests. v. Conduct the research. vi. Conduct post-tests. vii. Analyse the collected data. Now let’s explore these seven steps in true experimental design. 1) Identify the research objective: Identify the variables which you need to analyze for a cause-and-effect relationship. Deliberate which particular relationship study will help you make effective decisions and frame this research objective in one of the following manners: Determination of the impact of X on Y Studying how the usage/application of X causes Y 2) Identify independent and dependent variables: Establish clarity as to what would be your controlling/independent variable and what variable would change and would be observed by the researcher. In the above samples, for research purposes, X is an independent variable & Y is a dependent variable.  3) Define and group the population: Define the targeted audience for the true experimental design. It is out of this target audience that a sample needs to be selected for accurate research to be carried out. It is imperative that the target population gets defined in as much detail as possible. To narrow the field of view, a random selection of individuals from the population is carried out. These are the selected respondents that help the researcher in answering their research questions. Post their selection, this sample of individuals gets randomly subdivided into control and experimental groups. 4) Conduct Pre-tests: Before commencing with the actual study, pre-tests are to be carried out wherever necessary. These pre-tests take an assessment of the condition of the respondent so that an effective comparison between the pre and post-tests reveals the change brought about by the research. 5) Conduct the research: Implement your experimental procedure with the experimental group created in the previous step in the true experimental design. Provide the necessary instructions and solve any doubts or queries that the participants might have. Monitor their practices and track their progress. Ensure that the intervention is being properly complied with, otherwise, the results can be tainted. 6) Conduct post-tests: Gauge the impact that the intervention has had on the experimental group and compare it with the pre-tests. This is particularly important since the pre-test serves as a starting point from where all the changes that have been measured in the post-test, are the effect of the experimental intervention. So for example: If the pre-test in the above example shows that a particular customer service employee was able to solve 10 customer problems in two hours and the post-test conducted after a month of 2-hour workouts every day shows a boost of 5 additional customer problems being solved within those 2 hours, the additional 5 customer service calls that the employee makes is the result of the additional productivity gained by the employee as a result of putting in the requisite time. 7) Analyse the collected data: Use appropriate statistical tools to derive inferences from the data observed and collected. Correlational data analysis tools and tests of significance are highly effective relationship- based studies and so are highly applicable for true experimental research. This step also includes differentiating between the pre and the post-tests for scoping in on the impact that the independent variable has had on the dependent variable. A contrast between the control group and the experimental groups sheds light on the change brought about within the span of the experiment and how much change is brought intentionally and is not caused by chance Types of True Experimental Design The research design is categorized into three types based on the way you should conduct the research. Each type has its own procedure and guidelines, which you should be aware of to achieve reliable data. The three types are: 1) Post-test-only control group design. 2) Pre-test post-test control group design. 3) Solomon four group control design. 1) Post-test-only control group design: In this type of true experimental research, the control as well as the experimental group that has been formed using random allocation, are not tested before applying the experimental methodology. This is so as to avoid affecting the quality of the study. The participants are always on the lookout to identify the purpose and criteria for assessment. Pre-test conveys to them the basis on which they are being judged which can allow them to modify their end responses, compromising the quality of the entire research process. However, this can hinder your ability to establish a comparison between the pre-experiment and post-experiment conditions which weighs in on the changes that have taken place over the course of the research. 2) Pre-test post-test control group design: It is a modification of the post-test control group design with an additional test carried out before the implementation of the experimental methodology. This two-way testing method can help in noticing significant changes brought in the research groups as a result of the experimental intervention. There is no guarantee that the results present the true picture as post-testing can be affected due to the exposure of the respondents to the pre-test. 3) Solomon four group control design: This type of true experimental design involves the random distribution of sample members into 4 groups. These groups consist of 2 control groups that are not subjected to the experiments and changes and 2 experimental groups that the experimental methodology applies to. Out of these 4 groups, one control and one experimental group is used for pre-testing while all four groups are subjected to post-tests. This way researcher gets to establish pre-test post-test contrast while there remains another set of respondents that have not been exposed to pre-tests and so, provide genuine post-test responses, thus, accounting for testing effects.What are the advantages of true experimental design? Let’s take a look at some advantages that make this research design conclusive and accurate research. Advantages of true experimental design Concrete method of research: The statistical nature of the experimental design makes it highly credible and accurate. The data collected from the research is subjected to statistical tools. This makes the results easy to understand, objective and actionable. This makes it a better alternative to observation-based studies that are subjective and difficult to make inferences from. Easy to understand and replicate: Since the research provides hard figures and a precise representation of the entire process, the results presented become easily comprehensible for any stakeholder. Further, it becomes easier for future researchers conducting studies around the same subject to get a grasp of prior takes on the same and replicate its results to supplement their own research. Establishes comparison: The presence of a control group in true experimental research allows researchers to compare and contrast. The degree to which a methodology is applied to a group can be studied with respect to the end result as a frame of reference. Conclusive: The research combines observational and statistical analysis to generate informed conclusions. This directs the flow of follow-up actions in a definite direction, thus, making the research process fruitful. What are the disadvantages of true experimental design? We should also learn about the disadvantages it can pose in research to help you determine when and how you should use this type of research. Expensive: This research design is costly. It takes a lot of investment in recruiting and managing a large number of participants which is necessary for the sample to be representative. The high resource investment makes it highly important for the researcher to plan each aspect of the process to its minute details. Too idealistic: The research takes place in a completely controlled environment. Such a scenario is not representative of real-world situations and so the results may not be authentic. This is one of the main limitation why open-field research is preferred over lab research, wherein the researcher can influence the study. Time-consuming: Setting up and conducting a true experiment is highly time-consuming. This is because of the processes like recruiting a large enough sample, gathering respondent data, random distribution into groups, monitoring the process over a span of time, tracking changes, and making adjustments. The amount of processes, although essential to the entire model, is not a feasible option to go for when the results are required in the near future.  Reference Campbell, D. T., & Stanley, J. C. (1963). *Experimental and Quasi-Experimental Designs for Research*. Houghton Mifflin. Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). *Experimental and Quasi-Experimental Designs for Generalized Causal Inference*. Wadsworth Cengage Learning. McBurney, D. H., & White, T. L. (2010). *Research Methods*. Cengage Learning.