This is a research Methodology Class
You have participated in an introductory research class. You have been exposed to new concepts and new ideas. It is important to recognize that you have taken an important step in understanding social science research. The information you have learned will allow you to be wiser consumers of research in the future.
Complete a 2 page, APA formatted summary reflecting on what you have learned about research in the past 5 weeks. Include the following:
An explanation of what you have learned about hypotheses, research proposals and ethics.
This should be a combination of facts and personal experiences.
Devote at least one paragraph to hypotheses, research proposals and ethics.
The experimental design is the final type of quantitative design we will examine. Experimental design has long been known as the “gold standard” in psychological research. This type of design does attempt to make predictions based on statistical procedures. The experimental design consists of several components that may or may not be shared with other research designs. One such component is the hypothesis.
The hypothesis is the question that the research design is attempting to answer. It is written in the form or a “null” hypothesis and an alternative hypothesis. The null hypothesis suggests there is not a “statistically significant” relationship between two variables of interest. When we “accept” the null hypothesis, we say that we “fail to reject” the null hypothesis. The reason for this is that we never fully accept anything in the scientific method. For example, for thousands of years, people thought the earth was flat and the evidence seemed to support that. It was not until recently in human history that this was sufficiently challenged to conclude the earth was not flat. So, for every experimental study, we have at least one null and one alternative hypothesis.
The variable that we are trying to measure is the “dependent variable” meaning it is dependent on the “independent” variable. We manipulate the independent variable and measure any changes in the dependent variable. We generally divide a study into at least two groups, one being the control group and the other being the experimental group. We do not do anything to the control group, just make an initial measurement and then a follow-up measurement. With the experimental group, we make an initial measurement, apply the treatment and then measure for any changes. We can compare the changes to the initial change (within groups) or measure the changes in comparison to the control group (between groups).
Sometimes the fact that a person is taking part in an experiment may lead to changes in behavior or some other variable that we are measuring. We call this the placebo effect. We design our experiments by attempting to control for them. For example, the individual or groups may not know if they are receiving treatment or not. This where deceptions enter the design. If the subject knew if they were getting treatment or a placebo, that may impact how they react. There are ethical principles that we must follow when designing research. The role of the Institutional Review Board will be covered in more depth in later units.
Also important is the sample for experimental methods. We will cover this in later modules as well. Generally, is it not feasible to look at all individuals in a population. This may be cost-prohibitive or just unrealistic. For this reason. we take a “sample” of the population. There are many different types of sampling that we will cover later in the modules but the “gold standard” in sampling for experimental research is the random sample. This means that everyone in the population we are studying has an equal chance of being included in the sample. We test the sample, and this allows us to make informed conclusions about the population or it gives us some ability to make inferences (again using inferential statistics).
Many types of experimental designs are beyond the scope of this class. We will be examining a simple experimental model to determine if there one variable will lead to changes in another variable. We are measuring different “variables” to see if a change in one causes a change in another. There are variables that “get in the way” when we are trying to measure changes in two variables in complex organisms. We call these confounding variables. We try to design our studies to reduce the impact of the confounding variables as much as possible. Therefore, many experimental design studies have been completed in the lab where it is generally easier to control for the influences of these variables. This is one of the critiques of quantitative research is that it leads to artificial situations that we attempt to generalize to real-world situations.
Experimental designs use inferential statistics to determine if changes in one variable lead to changes in another. This the basis of experimental design. Designs quickly become complex as we attempt to control for confounding variables, etc. Generally, experimental designs are costly and time-consuming so we attempt to answer as many research hypotheses as we can.
The research proposal includes three distinct chapters, the introduction, the literature review and the methods section. The proposal will eventually evolve into the study, meaning that the initial three chapters will be expanded as the study progresses. The research proposal is the outline for the study to follow.
The first chapter of the research proposal is the Introduction. This is a brief outline of the problem and a rationale for why it is worthwhile studying. This will include some brief references to past research studies.
The second chapter is the Literature Review. This sectional will be briefer in the proposal than in the final research report. The Literature Review begins broad in perspective and then narrows. It starts with a rather wide review of the relevant literature and then quickly but logically narrows to more specific research that is directly relevant to the proposed study. The narrowed research will be related to the final research question. Research that is exact to the proposal may not be available, thus justifying the need for the study.
The third and final chapter in the research proposal is perhaps the most important. This is the Methods section and discusses such issues as the recruiting and retention of participants, the care and treatment of subjects, the securing or research data, informed consent of the participants and a general description of how data will be analyzed. This is perhaps the most important chapter for the IRB as it discusses how the participants will be cared for.
The proposal is then submitted to the Institutional Review Board (IRB) for review. Before any research can begin, the Institutional Review Board must accept the proposal.
The IRB is made up of a committee of individuals whose responsibility is to review the research proposal and examine several factors to ensure the ethical treatment of human and animal participants. The IRB determines if the design of the research proposal treats the human and animal participants ethically. In approving the proposal, the IRB must determine if the risk to the participants outweighs the potential benefits of completing the study. If the study is approved several accepted guidelines must be in place to protect participants.
The IRB can accept, reject or suggest revisions before a proposal is accepted. If the proposal is accepted, the researcher can begin the study. If the proposal is rejected, the researcher cannot complete the research. Often the IRB will accept the proposal after the researcher has revised the proposal. When the revisions have been completed, the proposal is resubmitted to the IRB. This process may take place several times before the proposal is finally accepted.
There are several different types of sampling. Although it is beyond the scope of this course to cover all of them, we will examine a variety of different types. The first type of sampling we will discuss is called random sampling. Random sampling means that all individuals in a population have an equal chance of being selected for a sample. When we decide to use a random sampling method, there are several different techniques we can use for determining the individuals who are selected for the sample.
One technique for random sampling is a random generator. This is a computer-generated list of random numbers. We can assign each member of a population with a number and then use the random generator to determine which individuals to include in the sample.
Let’s assume we have a population of 1000 college students and we want a sample size of 30. We could assign each student a number and then use a random generator to determine which number with which to start. We could then decide we want to use every 30th numbered student after the first one to get our random sample. Theoretically, each of our population members has an equal chance of being included in the sample.
Another type of sampling we can use is called systematic sampling. In systematic sampling the individuals are “counted off” and a predetermined interval of numbers is chosen, and those corresponding individuals are included in the sample. For example, individuals could be counted off and every fourth one chosen for the sample.
For systematic sampling with our college student example, we could number all of the students and pick every 20th individual as a member of our sample. This would give us a sample size of about 50.
Convenience sampling is another type of sampling. This type of sampling is not considered to be as high of quality as other types. In convenience sampling, the researcher selects the first individuals who are identified.
For example, if a researcher were studying homeless people, the first homeless individuals encountered would be those included in the sample.
Another type of sampling is called cluster sampling. This is accomplished by dividing the population into groups. This can be done geographically or based on other criteria. These groups are called clusters or blocks. The clusters are randomly selected, and the identified criteria from the block are used.
For example, let’s assume we want to study the political opinions of a small mountain community. We have access to ten such communities but determine that we cannot study all of them due to geographical issues. We could randomly pick one community (group or cluster) and study it. This is an example of cluster sampling.
The final type of sampling we will discuss is called stratified sampling. The population is divided into groups based on some criteria such as age, gender, race, etc. A sample is then taken from each of these strata using either random, systematic, or convenience sampling.
A stratified example may be that designing a sample group of young, female college students. We could establish groups based on age, gender and education level. Our sample could be collected from each of our groups (strata) using random, systematic or convenience sampling. Using one of these three techniques we could then choose our sample from the strata that we established earlier.
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