Research Designs - Definition, Purpose, and Designs (PDF)

Unit 3 Research Designs Meaning A research design is a strategy for answering research questions using empirical data. When a research is carried-out, it follows a definite pattern or plan of action throughout the procedure. This definite pattern or plan of action is called "research design"...

Unit 3 Research Designs Meaning A research design is a strategy for answering research questions using empirical data. When a research is carried-out, it follows a definite pattern or plan of action throughout the procedure. This definite pattern or plan of action is called "research design". It is a map that guides the researcher in collecting and analyzing the data. In other words, research design acts as a blueprint that is followed throughout the research work. Purpose of a good research design 1. Guiding the research process: Research design provides a roadmap for researchers, outlining the steps and procedures needed to collect and analyze data. It helps ensure that the study is well-organized and systematic. 2. Ensuring validity and reliability: By carefully designing the research, including selecting appropriate methods and techniques, researchers aim to minimize bias and errors that could affect the accuracy and quality of the results. A well-designed study increases the validity and reliability of the findings. 3. Maximizing efficiency: Research design helps researchers make efficient use of resources, including time, money, and effort. It allows them to plan and allocate resources effectively based on the requirements of the study. 4. Facilitating replication and generalization: A well-designed research study makes it easier for other researchers to replicate the study, verifying the findings and extending knowledge in the field. Additionally, a robust research design enhances the generalizability of the results to the broader population or similar contexts. Principles and features of research 1. Clarity of purpose: Clearly define the research objectives, questions, or hypotheses that the study aims to address. This ensures focus and guides the design decisions throughout the research process. 2. Rigor and validity: Establish robust methods and procedures to ensure the reliability and validity of the findings. This involves using appropriate sampling techniques, minimizing bias, and employing reliable and valid measures or instruments. 3. Adequate sample size: Determine an appropriate sample size that is statistically representative and provides sufficient power to detect meaningful effects or relationships. The sample size should be based on the research objectives and the statistical analysis planned for the study. 4. Proper selection of participants: Ensure that the participants or subjects for the study are relevant to the research question and possess the necessary characteristics. Consider factors such as demographics, inclusion/exclusion criteria, and sampling methods to obtain a representative sample. 5. Clear and appropriate data collection methods: Select data collection methods and tools that align with the research objectives and are suitable for gathering the required information. This may involve surveys, interviews, observations, experiments, or a combination of methods. 6. Ethical considerations: Adhere to ethical guidelines and obtain necessary approvals from relevant ethical review boards or committees. Protect the rights and well-being of participants, maintain confidentiality, and obtain informed consent. 7. Adequate data analysis plan: Define a clear plan for analyzing the collected data, including appropriate statistical techniques and procedures. This ensures accurate interpretation and meaningful conclusions. 8. Time and resource management: Plan and allocate resources effectively, including time, funding, and human resources, to ensure the efficient execution of the research study. 9. Flexibility and adaptability: Design the research study to accommodate unforeseen challenges or changes that may arise during the research process. This allows for adjustments without compromising the integrity or validity of the study. 10. Communication and dissemination: Plan for the effective communication and dissemination of research findings to relevant audiences, such as academic communities, policymakers, or the general public. This includes publishing in peer-reviewed journals, presenting at conferences, or preparing reports. Non Experimental Designs Non-experimental research design is a type of research design that does not involve the manipulation of an independent variable or the control of extraneous variables. Unlike experimental designs, non-experimental designs do not have a treatment or intervention that is applied by the researcher. Instead, these designs focus on observing and describing relationships or differences without introducing specific interventions. Difference between experimental and non-experimental research Experimental research involves changing variables and randomly assigning conditions to participants. As it can determine the cause, experimental research designs are used for research in medicine, biology, and social science. Experimental research designs have strict standards for control and establishing validity. Although they may need many resources, they can lead to very interesting results. Non-experimental research, on the other hand, is usually descriptive or correlational without any explicit changes done by the researcher. The situation is described as it is or it describe a relationship between variables. Without any control, it is difficult to determine causal effects. The validity remains a concern in this type of research. I.​ Observational design Scientific observation is made under precisely defined conditions, in a systematic and objective manner, and with careful record keeping. Methods of Observational research can be classified as follows:  a.​ Direct observation methods Researchers often observe behavior while it occurs—that is, through direct observation. Direct observational methods can be classified as “observation without intervention” or “observation with intervention.” 1.​ Observation without Intervention (Naturalistic observation) The goals of naturalistic observation are to describe behavior as it normally occurs and to examine relationships among variables.  Naturalistic observation helps to establish the external validity of laboratory findings. When ethical and moral considerations prevent experimental control, naturalistic observation is an important research strategy.. Although it is not easy to define a natural setting precisely (see Bickman, 1976), we can consider a natural setting one in which behavior ordinarily occurs and that has not been arranged specifically for the purpose of observing behavior. For eg: 1.Observing athletes in an Olympic competition is a naturalistic observation. 2. Hartup (1974), chose naturalistic observation to investigate the frequency and types of aggression exhibited by preschoolers in a St. Paul, Minnesota, children’s center. 2. Observation with Intervention Most psychological research uses observation with intervention. The two methods of observation with intervention are participant observation, structured observation Drawbacks of Naturalistic observation​ Researcher does not have control over the variables.​ Ethical concerns – invasion of privacy i.Participant Observation- In participant observation, observers play a dual role: They observe people’s behavior and they participate actively in the situation they are observing. In undisguised participant observation, individuals who are being observed know that the observer is present for the purpose of collecting information about their behavior. This method is used frequently by anthropologists who seek to understand the culture and behavior of groups by living and working with members of the group. In disguised participant observation, those who are being observed do not know that they are being observed. Participant observation allows an observer to gain access to a situation that is not usually open to scientific observation. For example, a researcher analyzing hate crimes against African Americans entered various “White racist Internet chat rooms” while posing as a a racist person curious to understand the group’s activities. (Glaser, Dixit, & Green, 2002). The given example illustrates a disguised participant observation. Drawbacks of participant observation: Observer Bias- The observer may lose out on scientific objectivity. The researcher may get carried away by their feelings about what is being observed or their expectations about the study.For eg: Let us consider a researcher who is involved in a participant observation of racist chat rooms. The researcher may get extremely emotionally affected by the conversations in the chatroom and consequently be unable to objectively observe the situation. Researcher's influence may change participant behaviour​- It is likely that the participant observer will have to interact with people, make decisions, initiate activities, assume responsibilities etc. By participating in the situation, do observers change the participants and events? If people do not act as they normally would because of the participant observer, it is difficult to generalize results to other situations. Reactivity (incase of undisguised participant observation)​- Knowing that they are being observed can change the way people behave. Expectancy effect-The researcher may get carried away by their expectations about the study based on hypotheses or results of previous studies. ii.Structured observation-researchers intervene to exert some control over the events they are observing. Often used by clinical and developmental psychologists, structured observations are set up to record behaviors that may be difficult to observe using naturalistic observation. Jean Piaget is perhaps most notable for his use of these methods. In many of Piaget’s studies, a child is first given a problem to solve and then given several variations of the problem to test the limits of the child’s understanding. These structured observations have provided a wealth of information regarding children’s cognition and are the basis for Piaget’s “stage theory” of intellectual development Drawbacks of structured observation Reactivity​- Knowing that they are being observed can change the way people behave. Observer Bias​- The researcher may not be objective in their interpretation of data, allowing their personal opinions and biases to affect the reporting. Expectancy effect-The researcher may get carried away by their expectations about the study. B. Indirect Observation An important advantage of indirect observational methods is that they are nonreactive. Indirect, or unobtrusive, observations can be obtained by examining physical traces and archival records. i.Physical Traces​ ​Physical traces are the remnants, fragments, and products of past behavior.​ 1.Use Traces - the physical evidence that results from the use (or nonuse) of an item.​ Natural-use traces are observed without any intervention by a researcher and reflect naturally occurring events. ​ Controlled-use traces result from some intervention by a researcher.​ Eg: A study on textbook usage by Friedman and Wilson (1975) studied controlled use traces by placing tiny glue seals in textbooks. At the end of the course, they examined the textbooks to determine how many seals had been broken and where the broken seals were located. Because they controlled the presence of glue seals in the books, this would be an example of a controlled-use trace. These investigators also analyzed the frequency and nature of underlining in the textbooks, a natural-use measure because underlining is typically associated with textbook use and is a natural use trace. 2. Products- Products are the creations, constructions, or other artifacts of behavior.​ Eg: Rozin et al. hypothesized that the French eat less and hence have lesser incidence of obesity and heart disease than Americans though the food they consume is high in fat. They examined food products, specifically portion sizes, in both countries to test this hypothesis. They found that American restaurant portions were on average 25% greater than in comparable French restaurants, and that portion sizes on American supermarket shelves were generally larger. Their observation of products supported their hypothesis. ii.Archival records​ Archival records are the public and private documents describing the activities of individuals, groups, institutions, and governments, and comprise running records and records of specific, episodic events.​ 1.Running records- Records that are continuously kept and updated are referred to as running records.​ For example, in the French paradox experiment, researchers also examined archival records to test their hypothesis. They examined restaurant guides in two cities, Philadelphia and Paris, and recorded the number of references to “all-you-can-eat” buffets. 2.Records for running episodes i.Episodic records - describe specific events or episodes​ Eg: Marriage certificates, college degrees Drawbacks of indirect observation Bias can be introduced in the way use- traces are laid down and the manner in which traces survive over time. For example, Does the number of cans found in recycling containers at a university reflect students’ preferences for certain brands or simply what is available in campus vending machines? Do product sizes on supermarket shelves in America and France reflect different family sizes in the two countries or preferences for portion sizes? Whenever possible, researchers need to obtain supplementary evidence for the validity of physical traces (see Webb et al., 1981). Alternative hypotheses for observations of physical traces must be considered and care must also be taken when comparing results across studies to make sure that measures are defined similarly. Two problems are selective deposit and selective survival. These problems occur because there are biases in how archives are produced. Selective deposit occurs when some information is selected to be deposited in archives, but other information is not. For example, consider a high school yearbook. Not all activities, events, and groups are selected to appear in the yearbook. This introduces bias. Selective survival arises when records are missing or incomplete (something an investigator may not even be aware of). Researchers must consider whether some records “survived,” whereas others did not. For example, Family photo albums may “mysteriously” lose photos of individuals now divorced or photos from “fat years.” Cross sectional design Cross-sectional studies are observational studies that analyze data from a population at a single point in time. They are often used to measure the prevalence of health outcomes, understand determinants of health, and describe features of a population. Unlike other types of observational studies, cross-sectional studies do not follow individuals up over time. They are usually inexpensive and easy to conduct. Cross-sectional studies have been mainly used to understand the prevalence of a disease in clinical research. In a cross-sectional study,researchers typically describe the distribution of variables in a population. They may assess the prevalence of a disease or association of an exposure to an outcome in a population. Cross-sectional studies can be classified as descriptive or analytical, depending on whether the outcome variable is assessed for potential associations with exposures or risk factors. Descriptive cross-sectional studies simply characterize the prevalence of one or multiple health outcomes in a specified population. In analytical cross-sectional studies, investigators collect data for both exposures to risk factors and outcomes at one specific point in time They compare outcome differences between exposed and unexposed subjects. The exposures and outcomes are measured simultaneously; therefore, it is difficult to determine whether the exposures preceded or followed the outcomes in an analytical cross-sectional study. Strengths of cross sectional designs Relatively quick and inexpensive to conduct No ethical difficulties Multiple outcomes and exposures can be studied. Easy for generating hypotheses Many findings can be used to create an in-depth research study Weaknesses Unable to measure the incidence of a disease , only prevalence is measured since the collection of data occurs at a single time point. Difficult to make a causal inference Associations identified might be difficult to interpret. Unable to investigate the temporal relation between outcomes and risk factors. Not good for studying rare diseases Susceptible to biases such as nonresponse bias and recall bias Non response bias - when questionnaires are circulated, many people do not answer,the population that answers may be qualitatively different from the ones who do not. Recall bias - participants may not accurately remember past events while answering the questionnaire or interview. Experimental Research Design Basic principles of experimental design There are three essential principles of experimental design. These principles make a legitimate test of significance possible A. Randomization in experimental design: As the name itself suggests, this principle adopts a random approach to the assignment of treatments to experimental groups of people. It is viewed as one of the most reliable mechanisms, mostly due to its ability to generate an equal probability for every experimental group to receive any of the available treatments. This principle eliminates any variations or bias, thus making the experimental research a more authentic and bona fide one. B. The Principle of Replication: According to the Replication Principle, the experiment is repeated more than once, as the name implies. Thus, every treatment is applied to many experimental units rather than one. By doing so, the accuracy of the experiments is increased. The whole experiment can even be repeated several times for a better outcome. It is introduced to increase the precision of a study—in other words, the precision with which the main impact and interactions can be accessed. C. Principle of Local Control: It has been seen that all extraneous sources of variation are not eliminated by randomization and replication, i.e., they are actually unfit to control the extraneous sources of variation.  We wish to choose a design in such a manner that all extraneous sources of variation are brought under regulation. For fulfilling this purpose, we make use of "local control," a term referring to techniques like balancing. The main objective of local control is to increase the efficiency and precision of experimental design by decreasing experimental error. Steps in experimentation (Little and Hills 1978): 1. Identify the Research Problem Clearly define the problem or question you want to study. Ensure it is specific, measurable, and researchable. Example: "Does sleep deprivation impair memory performance?" 2. Conduct a Literature Review Review existing research on the topic to identify gaps and refine your hypothesis. This helps justify the study's significance and informs your methodology. 3. Formulate a Hypothesis Develop a testable prediction about the relationship between variables. Example: "Individuals who sleep less than 4 hours will have poorer memory performance compared to those who sleep 8 hours." 4. Identify Variables Independent Variable (IV): The variable manipulated by the researcher (e.g., hours of sleep). Dependent Variable (DV): The outcome measured (e.g., memory test scores). Control Variables: Factors kept constant to ensure the IV is the only thing affecting the DV. 5. Select the Experimental Design Decide on the structure of your study: Between-Subjects Design: Different groups experience different levels of the IV. Within-Subjects Design: The same participants experience all levels of the IV. 6. Recruit Participants Define your target population and sampling method. Random sampling or random assignment helps ensure generalizability and reduces bias. 7. Obtain Ethical Approval Seek approval from an ethics board (e.g., IRB). Ensure participant consent, confidentiality, and right to withdraw at any time. 8. Conduct a Pilot Study (Optional) Test your procedure with a small group to identify and resolve any issues before the main experiment. 9. Collect Data Execute the experiment according to your design, ensuring consistency and control. Use the appropriate techniques to control extraneous variables. Randomly assign participants to experimental and control groups. 10. Analyze Data Use statistical tests (e.g., t-tests, ANOVA, regression) to determine if the results support the hypothesis. 11. Draw Conclusions Interpret findings and assess whether they align with the hypothesis. Consider limitations and potential alternative explanations. 12. Report Results Share findings in a structured format, such as a research paper or presentation. Include an introduction, method, results, and discussion section. 13. Replicate the Study Replication ensures reliability and validity of the findings. Between groups design In between groups design an independent variable is tested between subjects. ​ A.Randomised groups design If the independent variable is manipulated and assignment happens through simple random assignment, the design will be called an independent groups design or randomised groups design. There could be two groups designs and more than two group designs. Example: Studying the Effect of Sleep on Memory Retention Method:​ Independent Variable: Amount of sleep (4 hours vs. 8 hours).​ Dependent Variable: Score on a memory test.​ Participants: 40 people, divided into two groups of 20 each. Procedure:​ Group 1 is instructed to sleep for only 4 hours the night before a memory test.​ Group 2 is instructed to sleep for 8 hours the night before the same test.​ The next day, both groups take the same memory test.​ The researcher compares the test scores between the two groups to determine whether the amount of sleep significantly impacts memory retention.​ In this design, each participant is part of only one group and experiences only one condition (either 4 hours or 8 hours of sleep). This ensures that the groups are independent of each other. A common procedure for carrying out random assignment is block randomization.​ Block randomization balances subject characteristics and potential confoundings that occur during the time in which the experiment is conducted, and it creates groups of equal size. Suppose we have an experiment with five conditions (labeled, for convenience, as A, B, C, D, and E). One “block” is made up of a random order of all five conditions​ In block randomization, we assign subjects to conditions one block at a time. In our example with five conditions, five subjects would be needed to complete the first block with one subject in each condition.  and so on.. B. Matched groups design or Randomised block design In this design, a matching procedure is used to match groups. Example: Studying the Effect of Music on Math Test Performance Method:​ Independent Variable: Study condition (studying with music vs. studying in silence).​ Dependent Variable: Score on a math test.​ Participants: 30 students.​ Procedure:​ Matching Participants:​ All participants take a pretest measuring their math ability.​ Based on the pretest scores, participants are matched in pairs with similar math abilities. 2.Group Assignment:​ Within each pair, one student is randomly assigned to the "studying with music" group, and the other to the "studying in silence" group.​ 3.Test Phase:​ The students study under their assigned condition and then take a math test.​ 4.Comparison:​ Test scores of students in the "music" group are compared with their matched partners in the "silence" group.​ Key Feature:​ By matching participants based on math ability, this design controls for prior math skills, reducing variability that could interfere with detecting the true effect of the independent variable (music vs. silence). Selection of the Matching Variable As discussed above, in the matched-groups design subjects are measured on the basis of the matching variable prior to the introduction of the experimental variable. The question is: How is one to select a matching variable? The most important characteristic on the basis of which a matching variable can be selected is its ability to yield a high correlation with the dependent variable. If the matching variable is found to be highly correlated with the scores on experimental task or with scores on the dependent variable, the matching may be regarded as successful. On the other hand, if the matching variable yields a poor correlation or no correlation with the dependent variable scores, the matching is not regarded as successful. Now, the question arises: How can the experimenter find a matching variable which highly correlates with the dependent variable? One obvious answer is to use the dependent variable itself as the matching variable. For example, in a teaching intervention to improve students’ math scores, performance in math exam can itself be used as a matching variable. Using the dependent variable as matching variable may sometimes create a practice effect. For example, if the researcher uses the same set of numerical problems for matching and as dependent variable, the subject maybe already knowing the answers, so a different set of problems can be used for matching to counter this problem. In matching, an independent task may be used as the matching variable but the relationship between the independent task and the dependent variable should have been established in previous researches or investigations. It may, therefore, be said that a matched-groups design should not be used in the situation where the matching variable does not yield or expect to yield a high correlation with the dependent variable. Methods of Matching Having selected a matching variable and obtained a set of scores earned by all subjects on the matching variable, the next step is to match them. There are two ways of matching: matching by pairs and matching on the basis of mean and standard deviation. Matching by Pairs: One very convenient technique is matching by pairs. On the basis of the obtained scores by the subjects, the experimenter matches subjects in a way that each subject has a corresponding partner in the matched group or groups. An example may be given to illustrate matching by pairs. Suppose the experimenter wants to make a comparative study of the lecture method and the demonstration method upon problem-solving behaviour. There are 14 subjects available for the purpose. If the experimenter is to use the randomized-groups design, he may randomly assign 14 subjects into two groups, irrespective of what he knows about the subjects. But in using the matched-groups design, he must, first, match them on the matching variable. Let us suppose that intelligence test scores are used as the matching variable. All subjects are administered the intelligence test and their scores are obtained, which have been shown in the table below. Scores obtained by subjects in intelligence test: Now, the experimenter wants to construct two such groups (one for the lecture method and another for the demonstration method), which are equal on the intelligence test scores. For this the experimenter chooses subjects who have equal scores. Thus subject no. 1 is paired with subject no. 9; subject no. 2 is paired with 12, and so on, and in this way, seven pairs are formed. Matched scores on the basis of scores on the matching variable: Each pair, thus formed, constitutes a block. From these blocks, or pairmates, two groups are randomly formed. By a simple toss of a coin, the experimenter may determine that subject no. 1 goes to the group to be taught by the lecture method and subject no. 9 goes to the group to be taught by the demonstration method. There is, however, one limitation of the technique of matching by pair. If the experimenter insists on precise matching, the persons with deviant scores on the matching variable are likely to be eliminated. Say, for example, that subject no. 1 and 9 are paired because each has an equal score, that is, 70. Suppose, for the time being, that any one of this pair has 65 instead of 70. Now, in such a situation these two subjects cannot be paired. Not only this, both the subjects would be dropped from the original group because they have deviant scores in the sense that there are no matching partners for them in the group. A situation like this is not uncommon when the experimenter is dealing with a larger group. This has a natural effect of shortening the original group. The problem created by this situation is, however, not serious unless the experimenter is faced with the task of very accurate generalization regarding the population from which the sample was drawn. As a partial solution of the problem, the experimenter may predetermine certain allowances. For example, he may predetermine an allowance for the difference of 5 scores and then, subject nos. 1 and 9 may be paired together despite the difference of the 5 scores on the matching variable between them. Matching by mean and Standard deviation -Matching is sometimes done based on mean and standard deviation too. C.Latin square design A researcher is studying the effects of four different teaching methods (A,B,C,D) on students' memory retention. However, two factors may influence the outcomes: 1.​ Time of day (morning, afternoon, evening, night). 2.​ Order of presentation of the teaching methods (since earlier or later sessions may affect performance). The objective of the researcher is to determine which teaching method is most effective while controlling for time of day and presentation order. Factors controlled in this study are : ○​ Time of day: Each method is tested during all times of the day (morning, afternoon, evening, night). ○​ Order of presentation: Each method is presented in all session orders (1st, 2nd, 3rd, 4th). 2.​ Procedure: ○​ Randomly assign participants into groups. ○​ Each group participates in one "row" of the square. ○​ Test memory retention after each teaching session. 3.​ Analysis: ○​ Compare memory retention scores across teaching methods. ○​ Account for the time-of-day effect and order-of-presentation effect. D. Factorial design (Between group designs for more than two independent variables) A factorial design is widely used in psychology to examine how two or more independent variables (factors) influence a dependent variable and how these variables interact.  Example: Effect of Sleep and Study Method on Test Performance Independent Variables (Factors):​ Sleep Duration (Factor A, 2 levels):​ A1: 4 hours of sleep (low sleep)​ A2: 8 hours of sleep (adequate sleep)​ Study Method (Factor B, 2 levels):​ B1: Rote memorization​ B2: Conceptual learning​ Dependent Variable:​Test performance (e.g., test score out of 100). Experimental Setup:​ This is a 2 × 2 factorial design because there are 2 factors (Sleep Duration and Study Method) with 2 levels each. There are 2×2=4 combinations Sleep (A) Study Method (B) Combination Description A1 (4 B1 (Rote (A1, B1) Low sleep + rote study hours) memorization) A1 (4 B2 (Conceptual Low sleep + conceptual (A1, B2) hours) learning) study A2 (8 B1 (Rote Adequate sleep + rote (A2, B1) hours) memorization) study A2 (8 B2 (Conceptual Adequate sleep + (A2, B2) hours) learning) conceptual study Procedure:​ Recruit participants (e.g., 40 individuals).​Randomly assign participants to one of the four treatment combinations.​Conduct the study:​Control their sleep duration before the test (either 4 or 8 hours).​ Assign them a study method (rote memorization or conceptual learning).​ Measure their test scores after the study session. Analysis:​ Main Effects:​ Does sleep duration (A) affect test performance?​ Does study method (B) affect test performance?​ Interaction Effect:​ Does the effect of study method depend on the sleep duration?​ For example:​ If conceptual learning improves scores only when participants have 8 hours of sleep, there is a significant interaction. 2.Within group Design/Repeated measures design In within-group designs (also called repeated measures designs), the same participants are exposed to all conditions of the experiment. These designs are particularly useful for reducing variability caused by individual differences, as each participant acts as their own control. Within-group designs can be classified as complete or incomplete, depending on how participants experience the conditions. Example: Studying the Effect of Teaching method A,B,C on test performance Method:​ Independent Variable: Teaching method A,B,C Dependent Variable: Scores in test Procedure:​ Condition 1: Teaching method A Participants are taught using teaching method A and write a test after the teaching is done. Condition 2 : Teaching method B Participants are taught using teaching method B and write a test afterwards. Condition 3 : Teaching method C Participants taught using teaching method C and write a test afterwards. To make sure that we control for practice effect, we have to ensure that the material taught under each teaching method is different but of the same level of difficulty. a.​ Complete Within-Group Design ​ Each participant experiences every condition multiple times. ​ Conditions are repeated in such a way that they are counterbalanced within each participant to control for order and sequence effects. ​ Common counterbalancing methods: ○​ ABBA counterbalancing: The conditions are presented in one order (e.g., A, B) and then in the reverse order (B, A). ○​ Block randomization: Conditions are randomized within blocks, and the blocks are repeated multiple times. ​ Advantage: Controls for practice effects and fatigue within each participant. ​ Example: Incomplete Within-Group Design ​ Each participant experiences each condition only once or a limited number of times. ​ The conditions are counterbalanced across participants, not within each participant Example: Pre Experimental designs There are three designs, which actually do not qualify for the experimental designs because they do not provide a control group or the equivalent of a control group. Such designs do not adequately control threats of internal validity. These designs are called pre-experimental designs because they incorporate the least basic elements of an experimental design. Because these designs are inadequate in themselves, they are also sometimes referred to as non-designs. The following are three pre-experimental designs. A.​One-shot Case Study The one-shot case study may be diagrammed as indicated below: Χ Ο As its name implies, in a one-shot case study the treatment X is given to a single group and subsequently, an observation O is made to assess the effects of treatment upon the group. This design has two important limitations. One is that it does not provide a control group and another is that it does not give any information regarding the members who are given the treatment. In view of these limitations there is little justification for concluding that X caused O. Let us take an example. Suppose the principal of a college introduces the practice of giving monetary reward (X) to students who regularly attend their classes. After this practice has been in operation for a year, the principal observes that the students attend their classes regularly and disruptive activities in the classroom are minimized (O). On this basis the principal concludes that with the practice of giving monetary reward the absenteeism and the disruptive activities in the classroom are reduced. This conclusion is, however, dubious because the principal does not know (a) whether or not factors other than monetary reward have contributed to the observed change in behaviour; and (b) whether there was a statistically significant change in the observed behaviour relative to their past behaviour. B.​One-group Pretest-Posttest Design This design is an improvement over the above design because the effects of treatment (X) are judged by making a comparison between pretest and posttest scores. However, no control is used in this design. The design may be diagrammed as shown below: O1 X O2 For example, suppose the principal of a college wants to study the effect of movies in changing the attitude of a group of students. He first obtains some initial measures of attitude (O1) and then, for an hour the students may be asked to see the film (X), which intends to bring a change in attitude. Subsequently, a measure of attitude change may be obtained (O2). This design provides some information about two extraneous variables (that is, selection and experimental mortality), which are likely to endanger the internal validity of the experiment. The pretest scores indicate the initial state of the selected subjects and posttest scores indicate the state after the intervention of the subjects.However, the extraneous variables or the threats of internal invalidity like history, testing effect etc are not controlled by this design. C.​Static-group Comparison (or Intact-group Comparison) In this design two groups are taken. One group (O1) experiences the experimental treatment (X) and another group does not experience the experimental treatment (O2). Subsequently, these two groups are compared. The design may be diagrammed as shown below: The dashed line indicates that the experimental and control group have not been randomized. Thus in this design a control group (the group receiving no treatment) is used as a source of comparison for the treatment-receiving group or the experimental group. Because a control group is used, threats of internal invalidity like history, testing and instrumentation are controlled. One of the main demerits of this design is that the subjects of the control group and the experimental group are neither selected at random nor are they assigned randomly to the groups. Thus two groups are not equivalent (as there are no pretest scores) and hence the factor of selection is not controlled. As the samples are not randomly drawn, external validity is also threatened. Pre experimental designs should be avoided. Quasi Experimental Designs A quasi-experiment is one that applies an experimental interpretation to results that do not meet all the requirements of a true experiment. It means when the situation is such that the experimenter has some control over the manipulation of independent variables but fails to arrange for the other basic requirement of a true experiment, that is, creating equivalent groups. Thus, a quasi-experiment is basically an attempt to stimulate the true experiment and, therefore, has also been called a compromise design. Quasi-experimental designs are partly like true experimental designs. They control some but not all extraneous variables, which give threats to the internal validity of the experiment. In such a design we don't truly manipulate the independent variable. Such variables are called 'quasi-independent variables' and studies that employ them are called quasi-experiments where we lay out the design and compare the scores between the different conditions as in a true experiment, but we only appear to administer the variable. In a quasi-experiment, the researcher cannot randomly assign subjects to be exposed to a particular condition. Instead, subjects are assigned to a particular condition because they already qualify for that condition due to some inherent characteristics. Age, sex, race, background experiences or personality characteristics are some of the examples of quasi-independent variables. Such designs are better than pre-experimental designs, although they are not as adequate as the true experimental designs. In a way, then, the quasi-experimental designs are somewhere in between the pre-experimental designs and the true-experimental designs. A.​Time-Series Design Sometimes it happens that a control group or a comparison group cannot be included in an experiment because of the situation in which the experiment is being conducted. Still, the experimenter wants to have a design, which may exercise a better control over the extraneous variables. The time-series design is one such design, which can be followed in the situation described above. This design can be diagrammed as indicated below: O1O2O3O4 x O5O6O7O8 It is obvious from the above diagram that a series of pretests are given to the group. Subsequently, the treatment (X) is given and a series of posttests are given to the same group. This design differs from the single group pretest-posttest design because instead of giving a single pretest and posttest, a series of pretests and posttests are given. The extraneous variables like maturation, testing, selection and experimental mortality are well controlled. However, a variable like history is not controlled because the subjects are exposed daily to the different kinds of stimulation beyond those under the control of the experimenter. Research Question: Does implementing a new traffic law reduce the number of road accidents? Steps: 1.​ Baseline Data Collection (Pre-Intervention):​ The number of road accidents is recorded monthly for 12 months before the new traffic law is implemented.​ Example data: ○​ January: 200 accidents ○​ February: 190 accidents ○​... ○​ December: 210 accidents  2.​ Intervention:​ The new traffic law is introduced in January of the next year. 3.​ Post-Intervention Data Collection:​ The number of road accidents is recorded monthly for 12 months after the new traffic law is implemented.​ Example data: ○​ January: 180 accidents ○​ February: 160 accidents ○​... ○​ December: 140 accidents Design Overview: Time Event Data Collected Period Months Pre-Intervention Monthly accident 1–12 (Baseline) data Month 13 Intervention (New Traffic Law) Months Post-Intervention Monthly accident 14–24 data B.​Equivalent Time-Samples Design The equivalent time-samples design is an extension of the time-series design with the repeated introduction of the treatment or the experimental variable. Like the time-series design, in equivalent time-samples design a single group is used and the group is exposed to repeated treatments in some systematic way. The design may be diagrammed as indicated below: X1O1 X0O1 X1O1 X0O1 Research Question: Does playing background classical music in a library improve students' concentration? Steps: Participants: A group of 30 college students who frequently study in a library is observed. Design Alternation: Over a 4-week period, the presence or absence of classical music alternates daily in the library. Day 1: Classical music is played. Day 2: No music is played. Day 3: Classical music is played. Day 4: No music is played. Repeat this alternation for the entire 4-week period. Measurement: Students' concentration levels are measured daily (e.g., by observing time-on-task behavior or using a concentration test). Analysis: Compare students' average concentration scores on days with music to days without music. If concentration is consistently higher on music days, the intervention is effective. Key Characteristics: Same Participants: The same group is observed across all conditions to control for individual differences. Repeated Measures: Measurements are taken multiple times under both intervention (music) and non-intervention (no music) conditions. Temporal Control: The alternation of conditions helps control for external factors that might influence the outcome (e.g., fatigue, time of day). Advantages: Eliminates the need for a control group because each participant serves as their own control. Allows for the observation of trends over time. Limitations: Potential carryover effects: Residual effects of the intervention (e.g., lingering mood improvement from the music) might influence the non-intervention periods. Requires careful scheduling to ensure that external variables (e.g., noise, lighting) are consistent across conditions. C.Non equivalent control group In psychological and educational research, often the experimenter is faced with a situation in which he has to work with intact groups (that is, groups whose membership is prefixed and cannot be altered by the experimenter). For example, a teacher may provide the experimenter two classes -section A and section B for research and may not agree with the students being shuffled. In such a situation the experimenter has to accept them as intact groups. When reconstitution of subjects is not allowed, the experimenter cannot randomly assign them to the control group and the experimental group and hence, their equivalence is suspected. In working with such intact non-equivalent groups, the non-equivalent control group design is recommended. This design may be diagrammed as shown below: O1 X O2 --------------- O3 O4 The above design is similar to the pretest-posttest control group design except for random assignment of subjects to the experimental and the control conditions, which occur in case of the pretest-posttest control group design.. As the experimenter is not allowed to randomly assign the groups to the experimental and the control condition, their equivalence is not granted. This creates difficulty in controlling a variable like selection. However, this difficulty can be overcome by comparing the intact groups on pretests, that is, on O1, and O3. For the satisfaction of the experimenter on the criterion of equivalence, the intact groups can be compared on any control variables relevant to selection and potentially relevant to the treatment such as age, sex, intelligence, socio-economic status, etc. The statistical analysis consists of comparing the mean gain score of the treatment group (O2-O1) to the mean gain score of the non-treatment group (O4-O3). Consider an example to illustrate this design. Suppose the experimenter wants to know the effect of a week's training intended to improve the map-drawing skill among students of a geography class. For this purpose, the principal of a school provides the experimenter with two classes of geography, each consisting of 20 students. The principal does not permit reconstitution of these two groups in any way and requests the experimenter to handle them as intact groups. Following the request of the principal, the experimenter decides to use the non-equivalent control group. Which of these two groups would act as a control group and as an experimental group was decided randomly by flipping a coin by the experimenter. The experimenter, however, ascertained the equivalence of these two intact groups on age, sex, presence of physical disorders, and so on, and was satisfied that these groups could be regarded as equivalent in all these respects. Both groups were administered the map-drawing test as pretest measures. Subsequently, the experimental group was given training in map-drawing work (X) and the control group was not given any such training. After that, both groups were re-administered the map-drawing (O2 and O4). The mean gain scores (posttest minus pretest) of the two groups were compared to see if there is a statistically significant difference. D.Separate-Sample Pretest-Posttest Design The separate-sample pretest-posttest design is specially suited to those situations in which the experimenter cannot assign treatments to all subjects at a time. Hence, he is forced to select a sample and administer the treatment. Then, again another sample is taken and the same treatment is repeated. Consider a situation in which there are 2,000 persons who are to be trained but the experimenter cannot train more than 200 persons at a time. In such a situation the training programme has to run continuously each time with a new set of persons and at the same time the experimenter cannot withhold treatment from any person. Consequently, he cannot assign persons to training conditions and non-training conditions. As such no true experimental design can be applied here. To deal with such a situation a separate-sample pretest-posttest design may be adopted. For this purpose, the experimenter decides to take a one-group pretest-posttest design and repeat it as shown below: O1 X O2 —------------- O3 X O4 If the one-group pretest-posttest design is used without repetition, one may say that a variable like history is not controlled because the subjects may be influenced by several other events occurring simultaneously with the treatment (X) that might have produced O2. But when that design is repeated thus constituting a separate-sample pretest-posttest design, the variable like history is controlled because it is less likely that the same events would have occurred simultaneously to the subjects on both administrations. If O2 exceeds O1, and O4 exceeds O3, we have direct evidence for concluding this. Despite this, the separate-sample pretest-posttest design fails to control the three factors of internal invalidity such as testing effects, maturation and interaction of selection and maturation. E. Patched up design In a patched-up design the experimenter starts with an inadequate design and then adds some features so that recurrent factors producing invalidity may be maximally controlled. The patched-up design, shown below is a combination of two different pre-experimental designs, neither of which is adequate in itself, but which become adequate when combined. Usually, the experimenter selects such inadequate designs, that in combination their weaknesses are overcome and their strengths increased. X O1 O2 X O3 F. Longitudinal design In longitudinal design the researcher usually measures a group of subjects in order to observe the effect of passage of time. Such designs are confounded by extraneous events that occur during the study and they may not generalize over time. For eg: The researcher may like to study vocabulary development in a group of female children by testing them yearly from ages five to ten. G. Cross sectional design Cross-Sectional design: Cross-sectional design is a between-subjects quasi-experiment in which the researcher observes the subjects from different ages or at different points in temporal sequence. For eg: the researcher may select a cross-section of ages, testing the vocabulary of a group of 5-year olds, another group of 6-year olds, and so on. Another example -A researcher may want to study the impact of COVID on students who graduated during the pandemic. For the purpose, he or she chooses students who graduated during the years of COVID, i.e.2019-2022 and those who graduated before -2016,2018 and those who graduated after those years- 2023, 2024. A longitudinal study is time consuming and needs a lot of resources. Attrition is also high. Cross sectional designs can counter this by giving a snapshot of different groups placed at different points in a temporal sequence. The drawback in this design is the lack of generalisability due to cohort effect. For example, if a researcher is studying intellectual development across the lifespan using a cross sectional study, he/she will choose groups belonging to various age groups and compare between them. The drawback here is that these groups may be very different from each other as they are from different generations which grew up in different circumstances.We cannot conclude that the group whose members are 5 at the time of the study will, in the future show the same pattern of intellectual development at age 75 as the members of the group who is 75 at the time of the study. The conditions which the two groups have been born into are different. H. Cohort design In a cohort design the researcher conducts a longitudinal study of several groups, each from a different generation. Suppose for example, the researcher studies vocabulary development on a generation of children when they are five years old in 1990 and continues till they are 10 in 2000, in another generation of children beginning when they are of five years in 1995 and continues till they are ten in 2005, and in another generation of children beginning when they are of five years in 2000 and continues till they are ten in 2005. This method tests for both age wise differences within each generation and generation wise differences between the three generations. If there are significant mean differences between the three generations, we say there is a cohort effect- the changes are due to a generation’s unique history. True experimental designs There are three experimental designs, which are called true experimental designs. In these designs the control group and the experimental groups are formed and their equivalence is established through randomization. These designs are called true experimental designs because all the factors or variables contributing to internal invalidity are controlled. Although true experimental designs are the strongest type of designs, in some situations it is difficult to conduct experiments based upon such designs. Three true experimental designs are presented as mentioned below: A.​Posttest Only, Equivalent-group Design This design is the most effective and useful true experimental design, which minimizes the threats to the experimental validity. The design can be diagrammed as shown below: R X O1 R O1 In the above design there are two groups. One group is given the treatment (X), usually called the experimental group. The experimental group, and the other group is not given any treatment called the control; use of a control group automatically controls the two extraneous variables, namely, history and maturation. Both groups are formed on the basis of random assignment of the subjects and hence, they are equivalent. Not only that, subjects of both groups are initially randomly drawn from the population (R). This fact controls selection and experimental mortality. Besides these, in this design no pretest is needed for either group, which saves time and money. As both groups are tested after the experimental group has received the treatment, the most appropriate statistical tests would be those tests, which make a comparison between the mean of O1 and O2.Thus either t test or ANOVA is used as the appropriate statistical test. Let us take an example. Suppose the experimenter, with the help of the table of random numbers, selects 50 students out of a total of 500 students. Subsequently, these 50 students are randomly assigned to two groups. The experimenter is interested in evaluating the effect of punishment over retention of a verbal task. The hypothesis is that punishment enhances the retention score. One group is given punishment (X) while learning a task, and another group receives no such punishment while learning a task. Subsequently, both groups are given the test of retention. A simple comparison of mean retention scores of the two groups, either through the t test or ANOVA, would provide the basis for rejecting or failing to reject the null hypothesis. Example of a Post-Test Only True Experimental Design Research Question: Does a new teaching method improve students' math performance compared to traditional teaching methods? Steps: Random Assignment: A total of 50 students are randomly assigned into two groups: Experimental Group (New teaching method) Control Group (Traditional teaching method) Intervention: Experimental Group: Students are taught using the new teaching method for 4 weeks. Control Group: Students are taught using traditional methods for the same duration. Post-Test: At the end of the 4 weeks, both groups take the same math test designed to assess their understanding of the material. Analysis: The mean scores of the two groups on the math test are compared to determine if the new teaching method resulted in better performance. B.​Pretest-Posttest Control Group Design This design is similar to the previous one except for the fact that it also makes a provision for pretest for both groups before experimental and control treatments are administered. The design may be diagrammed as shown below: RO1 X O2 RO3 O4 It is obvious from the above diagram that the design has two groups. One group receives the treatment (X) and another group receives no such treatment. By use of the control group this design controls some sources of internal invalidity like history, maturation and statistical regression. As subjects are randomly assigned to the control group and the experimental group, the variables like selection and experimental mortality, posing threats to internal validity, are also controlled. It is also obvious from the above diagram that both groups are given a pretest and a posttest. The very element of pretest, however, introduces one basic limitation. In this design there is no control over the gain on the posttest due to the experience on the pretest (called testing effect), which may reduce the internal validity of the experiment. Not only this, there is no control over the possible sensitization to the treatment, which a subject might develop due to pretest experience, thus reducing the external validity of the experiment. For eg: if the researcher is using a teaching method as the treatment, the pre test can give the participant an idea of what to focus on in the class affecting the post test scores. Another example, could be that of a mindfulness intervention study. If the researcher is studying the effect of mindfulness on anxiety, the pre test may give away the objective of the mindfulness intervention. In other words, it can be said that interaction of testing and treatment (a source of external invalidity is not controlled. The data in the pretest-posttest control group design can be statistically analyzed by making a comparison of gain scores for the control group and the experimental group. In other words, mean gain score of O2-O1, can be compared with the mean gain score of O4-O3 so that it can be easily ascertained whether or not the treatment has a differential effect on the groups. If the groups are wholly equivalent, the posttest means, that is, O2 and O4, can also be compared for ascertaining the impact of treatment upon the groups. Example of a Pre-Test Post-Test True Experimental Design: Research Question: Does a new mindfulness training program improve students' academic performance? Steps: Random Assignment: A group of 60 high school students is randomly assigned into two groups: Experimental Group (Mindfulness Program) Control Group (No intervention or standard activity) Pre-Test: All participants take the same academic performance test before the intervention to establish baseline scores. Intervention: Experimental Group: Participates in a 4-week mindfulness training program. Control Group: Continues their regular school activities without any additional intervention. Post-Test: After the 4-week period, all participants retake the same academic performance test. Analysis: Compare the pre-test and post-test scores of both groups. If the experimental group shows a significantly greater improvement in academic performance compared to the control group, the intervention (mindfulness training) is deemed effective. C.​Solomon Four-Group Design The Solomon four-group design developed by Solomon (1949) is really a combination of the two equivalent-groups design described above, namely, the posttest-only design and pretest-posttest design, and represents the first direct attempt to control the threats of external validity. This design may be diagrammed as shown below: R O1 X O2 R O3 O4 R X O5 R O6 It is clear from this diagram that in this design four groups are randomly set by the experimenter. As a matter of fact, in this design, two simultaneous experiments are conducted and hence, the advantages of replication are available here. This design makes it possible to evaluate the main effects of testing as well as the interaction of testing, maturation and history, thus increasing the external validity or generalizability. Example of a Solomon Four-Group Design Research Question: Does a new stress management program reduce college students' stress levels? Steps: Random Assignment: A sample of 80 college students is randomly divided into four groups: Group 1 (Pre-Test + Experimental): Receives a pre-test, the stress management program, and a post-test. Group 2 (Pre-Test + Control): Receives a pre-test, no program (control), and a post-test. Group 3 (Experimental Only): Receives only the stress management program and a post-test. Group 4 (Control Only): Receives no program and only a post-test. Intervention: Groups 1 and 3 participate in the 6-week stress management program. Groups 2 and 4 do not receive any intervention. Testing: Pre-Test: Groups 1 and 2 take a pre-test to measure baseline stress levels. Post-Test: All four groups take the same stress measurement test after the intervention. Analysis: Compare Group 1 vs. Group 2: To assess the effect of the intervention when a pre-test is included. Compare Group 3 vs. Group 4: To assess the effect of the intervention without a pre-test. Compare Group 1 vs. Group 3: To determine if the pre-test influenced the results. Compare Group 2 vs. Group 4: To check if the pre-test alone affects outcomes. Ex post facto design In ex post-facto research, it can be said that the experimenter, instead of creating a treatment, evaluates the effects of a naturalistically occurring treatment after that treatment has occurred. He or she tries to relate the outcome (or the dependent variable measure) with already occurred treatments. In ex post-facto research the researcher gives the treatment not by manipulation but by selection. Because the independent variables are handled by selection in ex post-facto research, sometimes it is difficult to find out the cause-effect relationship between the dependent variable and the independent variable. There are two common types of ex post facto design, namely, correlational design and criterion-group design. a.​ Correlational Design A correlational approach is one in which the experimenter collects two or more sets of data from the same group of subjects so that the relationship between the two subsequent sets of data can be determined. he correlational design may be diagrammed as follows: O1 O2 Suppose the researcher wants to investigate the relationship between the intelligence and problem-solving ability of a group of children randomly taken from Class VI. For this purpose, the experimenter will administer the measure (or test) of intelligence (O1,) and subsequently, a test of problem-solving ability will be administered (O2). Thus the researcher will have two sets of data before him. He may apply appropriate correlational techniques depending upon the nature of the data. If the obtained correlation coefficient is positive and significant, the researcher may conclude that the higher the intelligence, the greater the ability to solve a problem. In fact, a strong and significant relationship between O1, and O2 suggests one of three possible meanings. 1. The variable measured by O1, has caused O2. 2. The variable measured by O2 has caused O1. 3. A third or unmeasured variable has caused both O1, and O2. But as the experimenter has not manipulated the variable, it is difficult to say which of the above three interpretations accounts for the obtained relationship. Whenever there occurs a weak relationship between O1, and O2, all the above three meanings are rejected. The correlational design, thus, cannot be regarded as an adequate design for establishing a cause-effect relationship among variables. Criterion-group Design In the criterion-group design, as its name implies, the experimenter tries to ascertain what has caused the particular state of condition by contrasting the characteristics of the group which possesses the criterion behaviour with those who do not. The criterion-group design may be diagrammed as shown below: C O1 O2 OR O1 C O2 O3 C O4 In the above diagram the letter C indicates the selection of an experience according to the criterion fixed by the experimenter. A close scrutiny of the design reveals that this is very similar to the correlational design and pre-experimental design, particularly the static-group comparison design. The criterion-group design may, therefore, be used in situations where intact groups of criterion and non-criterion subjects are available or where subjects with criterion experiences are randomly selected from a larger group of subjects all qualifying on the criterion variable. Let us take an example. Suppose the experimenter is interested in knowing the origins of divergent thinking among a group of 50 students randomly selected from Class IX. In other words, the purpose of the experimenter is to explore what are the factors, experience, types of personality, etc., that may cause divergent thinking among the children. As a first step, the experimenter will administer a test of divergent thinking to the group of students. On the basis of the test scores he would identify two criterion groups-one group consisting of those students who score higher on the test and another group consisting of those students who score lower on the test. Subsequently, the experimenter may try to find out the general environment into which they were reared, the attitudes and education of their parents, etc., by taking a structured interview of their parents. Because the information or experiences gained by the experimenter on the basis of the interview have not been manipulated in any way, it is difficult for the experimenter to conclude that a particular set of experiences has caused the divergent thinking. However, this criterion-group study definitely provides some testable hypotheses in terms of the potential causes, which by using a quasi-experimental design or true experimental design may be tested in a more scientific way. Other designs I.​ Exploratory designs An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome. The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue. Goals of exploratory research 1.It is a useful approach for gaining background information on a particular topic. 2.Exploratory research is flexible and can address research questions of all types (what, why, how). 3.Provides an opportunity to define new terms and clarify existing concepts. 4.Exploratory research is often used to generate formal hypotheses and develop more precise research problems for further studies. 5.In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated. II.​ Descriptive designs Descriptive Research Design Descriptive research design is a method focused on systematically describing a phenomenon, situation, or population. It aims to answer the "what" rather than the "why" of a subject by providing detailed information and a clear picture of the existing conditions or characteristics without manipulating variables. Key Features of Descriptive Research Design: Objective: To describe and document the aspects or characteristics of a phenomenon or population. To identify patterns, relationships, and trends in data. Methods Used: Descriptive research relies on both qualitative and quantitative methods, including: Surveys and Questionnaires: For collecting large amounts of data from participants. Observation: For recording behaviors, events, or situations in a natural setting. Case Studies: For an in-depth analysis of a single entity or group. Content Analysis: For analyzing text, media, or communication patterns. Focus on "What": Descriptive research answers questions like: What are the characteristics of X? How often does X occur? What is the current status of X? Structured Approach: Descriptive research is often more structured than exploratory research, with clearly defined variables and objectives.. III. Evaluation designs  Evaluation research entails carrying out a structured assessment of the value of resources committed to a project or specific goal. It often adopts social research methods to gather and analyze useful information about organizational processes and products. Research Environment: Evaluation research is conducted in the real world; that is, within the context of an organization. Research Outcome: Evaluation research is employed for strategic decision making in organizations. Research Focus: Evaluation research is primarily concerned with measuring the outcomes of a process rather than the process itself. Research Goal: The goal of program evaluation is to determine whether a process has yielded the desired result(s). This type of research protects the interests of stakeholders in the organization. It often represents a middle-ground between pure and applied research.  Evaluation research is both detailed and continuous. It pays attention to performative processes rather than descriptions. Research Process: This research design utilizes qualitative and quantitative research methods to gather relevant data about a product or action-based strategy. These methods include observation, tests, and surveys. IV. Action research Action research is a research method that aims to simultaneously investigate and solve an issue. In other words, as its name suggests, action research conducts research and takes action at the same time. Types of action research There are 2 common types of action research: participatory action research and practical action research. 1. Participatory action research emphasizes that participants should be members of the community being studied, empowering those directly affected by outcomes of said research. In this method, participants are effectively co-researchers, with their lived experiences considered formative to the research process. 2. Practical action research focuses more on how research is conducted and is designed to address and solve specific issues.  Both types of action research are more focused on increasing the capacity and ability of future practitioners than contributing to a theoretical body of knowledge.