Research Design Types PDF

TYPES OF EXPERIMENTAL RESEARCH DESIGN  Experimental research is a study conducted with a scientific approach using two sets of variables. The first set acts as a constant, which you use to measure the differences of the second set. Quantitative research methods, for example, are experim...

TYPES OF EXPERIMENTAL RESEARCH DESIGN  Experimental research is a study conducted with a scientific approach using two sets of variables. The first set acts as a constant, which you use to measure the differences of the second set. Quantitative research methods, for example, are experimental. Single Group Design (SGD) – this design utilizes no control Fish Meal Yield of Mudcrab (kg) Treatment Replications group with single treatment with two or more levels. 1 2 3 T1 - 5% Example: Effect of fish meal as T2 – 10% supplement feed for mud crab T3 – 15% cultured in a fishpond Two-group design – two comparable groups are Group Number of Mean (X) tomato fruits employed as experimental and control groups, or two Experimental comparable groups are both Group (Organic experimental group Frtilizer) Control Example: Effectiveness of using (Inorganic Fertilizer) organic and inorganic fertilizer in growing tomatoes Two-pair group design – an Participa Dishwashing liquid elaboration of the two-group nts/ Control Group Experimental Group design wherein there are two Respond (Without water (With water control groups and two ents dilution) dilution) experimental groups Brand X Brand Y Brand X Brand Y 1 Example: Acceptability of 2 dishwashing liquid with or 3 without water dilution 4 5 Parallel group design – two or more groups are used as the same time Control Group Experimental Group with only a single variable (Control Commercial Bangus Tilapia Group) manipulated or changed. The experimental group varies while 1 1 2 the parallel group serves as control for comparative purposes. X X X Example: Acceptability of canned X X X commercial liver spread, milkfish bone meal spread and tilapia bone meal spread. Complete Randomized Design Supplemen Yield of tilapia (kg) (CRD) – a group of test plants or tal feed per Replicates animals is studied only once but in compartm subsequent treatment is applied to ent determine the cause change. There 1 2 3 is no control in this design, but the subjects will undergo T1 (5%) randomization procedures T2 (10%) Example: Yield of tilapia using different levels of supplemental T3 ( 15%) feeds Randomized Complete Block Design Treatment Yield of tilapia (kg) (RCBD) – this design uses group of Replicates plants and animals as subject of the study which are studied once but subsequent treatments applied and 1 2 3 replicated to determine the cause of T1 (5%) change. There is control group in this design and the subjects will undergo T2 (10%) randomization process. T3 ( 15%) Example: Effectiveness of supplemental field in growing tilapia. Control Correlational design – this design is used to determine the Treatmen Weight (kg) Length (cm) ts relationship of two dependent variables (X and Y) on how they Brand X are manipulated by the independent variable. Example: The correlation of Brand Y weight and length of cultured tilapia using Brand X and Brand Y supplemental feeds Pre-test Post-test group design – this design involves the experimental group Group Means of the Test (X) and the control group which are Pre test Post test carefully selected through randomization procedures. Both groups Control Group are given pre-test at the beginning of the (Traditional study and post-test at the end of the learning) study. The control group in this design is isolated from all experimental influence. Experimental Example: Effectiveness of Problem- Group Based Learning approach in teaching (Problem-based Research. Learning) PRINCIPLES AND SIGNIFICANCE OF RESEARCH DESIGN A research design is... the conceptual structure within which research is conducted. constitutes the blueprint for the collection, measurement, and analysis of data. Answers the following questions (Crotty, 1998) 1. What is the study about and, what type of data is required? 2. What is the purpose of the study? 3. What are the sources of needed data? Answers the following questions (Crotty, 1998) 4. What should be the place or area of the study? 5. What time, approximately, is required for the study? Answers the following questions (Crotty, 1998) 6. What should be the number of materials or number of cases for the study? 7. What type of sampling should be used? Answers the following questions (Crotty, 1998) 8. What method of data collections would be appropriate? 9. How will data be analyzed? 10. What should be the approximate expenditure?  (Santosh, 2021) 1. Principle of flexibility – The design can be improved while conducting the research. 2. Principle of timeliness – The tasks/ procedures embedded can be completed within the allotted time frame.  (Santosh, 2021) 3. Principle of replication – The design used by one researcher can be employed by other researchers as well. It helps to test the design in the different context and helps to theorize the findings.  (Santosh, 2021) 4. Principle of objective – The research design is to be developed in such a way that the findings of the research are specific and clear. The result is not deemed out of pure lack or chances.  (Santosh, 2021) 5. Principle of generalizability – The research design is to be developed in such a way that the findings can be generalized in the large population even though the findings are developed from the study of the sample.  (Santosh, 2021) 6. Principle of reliability – The design depends on reliability, consistencies, dependability, and stability. The results are the same or consistent when same tools are used in the same sample.  (Santosh, 2021) 7. Principle of validity – It is concerned with the integrity of the conclusions that are generated. Formulating a research design helps the researcher to make correct decisions in each and every step of the study. It helps to identify the major and minor tasks of the study. It makes the research study effective and interesting by providing minute details at each step of the research process. Based on the design of experiments (research design), a researcher can easily frame the objectives of the research work. It helps the researcher to complete all the tasks even with limited resources in a better way. A good research design helps the researcher to complete the objectives of the study in a given time and facilitates getting the best solution for the research problems. SAMPLING TECHNIQUES  Sampling may be defined as the method of getting a representative portion of a population. Population is the aggregate or total of objects, persons, families, species, or orders of plants or animals.  Sampling is applicable if the population of the study is too large especially the 7Ms – manpower, money, materials, machinery, methods, moment, and marketing of the researcher – are limited. However, the use of total population is advisable if the number of subjects is less than 100.  If the population is equal to or more than 100, it is advisable to get the sample in order to be effective, efficient and economical in gathering data, provided however, that the sample is a representative cross-section of the population and is scientifically selected. It saves time, money, and effort. It is more effective. It is faster, cheaper, and economical. It is more accurate. It gives more comprehensive. Sample data involve more care in preparing detailed subclassification due to small number of subjects. If the sampling plan is not correctly designed and followed, the results may be misleading and can be erroneous.  𝑵 n= 𝟐 𝟏+𝑵𝒆 where n = sample size N = population size e = margin of error (level of significance) A group of researchers will conduct a survey to find out the opinion of residents of a particular community regarding the oil price hike. If there are 10 000 residents in the community and the researchers plan to use a sample using 10% margin of error, what should the sample size be? Suppose that in Example 1, the researchers would like to use 5% margin of error. What should be the size of the sample?  Sampling technique is a procedure used to determine the individuals or members of a sample. Suppose a guidance counselor of a certain school wants to determine the average weekly allowance of the students, if there are 2000 students in the school and the guidance counselor decided to use only 100 students as a sample, who will be included in the sample? Non-Probability Sampling Probability Sampling  Non-probability sampling is a sampling technique wherein members of the sample are drawn from the population based on the judgement of the researchers. Every unit of population does not get an equal chance of participation in the investigation. The result of a study using this sample technique are relatively biased. This technique lacks objectivity of selection; hence it is sometimes called subjective sampling. Inferences made based on the sample obtained using this technique are not so reliable.  Convenience Sampling As the name implies, it is used because of the convenience it offers to the researcher. For example, a researcher who wishes to investigate the most popular noontime show may just interview the respondents through the telephone. → The result of this interview will be biased because the opinions of those without telephone will not be included. Convenience Sampling is a non-probability sampling technique where samples are selected from the population only because they are conveniently available to the researcher. Researchers choose these samples just because they are easy to recruit, and the researcher did not consider selecting a sample that represents the entire population.  Quota Sampling The proportions of the various subgroups in the population are determined and the sample is drawn to have the same percentage in it. To illustrate this, let us suppose that we want to determine the teenagers’ most favorite brand of T-shirt. If there are 1000 female and 1000 male teenagers in the population and we want to draw 150 members for our sample, we can select 75 female and 75 male teenagers from the population without using randomization.  Purposive Sampling researchers select the samples based purely on the researcher’s knowledge and credibility. In other words, researchers choose only those people who they deem fit to participate in the research study. Let us suppose that the target is to find out the effectivity of a certain kind of shampoo. Of course, bald fellows will not be included in the sample.  Snowball Sampling helps researchers find a sample when they are difficult to locate. Researchers use this technique when the sample size is small and not easily available. This sampling system works like the referral program. Once the researchers find suitable subjects, he asks them for assistance to seek similar subjects to form a considerably good size sample.  Probability sampling is a sampling technique wherein each member or element of the population has an equal chance of being selected as members of the sample. Notice from the definition that when use probability sampling, it is important that we have a complete list of the members of the population. In addition, probability sampling is a sampling without bias because selection of members of the sample is not predetermined.  Simple Random Sampling A simple random sample is a sample selected in such a way that every possible sample of the same size is equally likely to be chosen. Drawing three names from a hat containing all the names of the students in the class. Any group of three names is as equally likely as picking any other group of three names.  Systematic Sampling There is not equal probability of every element been included. Elements are selected at a regular interval. The interval maybe in terms of time, space or order.  Stratified Sampling Stratified random sampling allows researchers to obtain a sample population that best represents the entire population being studied. Stratified random sampling involves dividing the entire population into homogeneous groups called strata.  Stratified Sampling Stratified random sampling differs from simple random sampling, which involves the random selection of data from an entire population, so each possible sample is equally likely to occur. A stratified random sample is obtained by separating the population into mutually exclusive sets, or strata, and then drawing simple random samples from each stratum.  Cluster Sampling The group of elements residing in one geographical region is called as cluster. This sampling technique is used when the elements of population are spread over a wide geographical area. Multi-Stage Sampling It is a sampling technique where two or more probability techniques are combined. It is used when elements of population are spread over a wide geographical region, and it is not possible to obtain a representative sample with only one aforementioned technique.