All research projects are based around variables. A variable is the characteristic or attribute of an individual, group, educational system, or the environment that is of interest in a research study. Variables can be straightforward and easy to measure, such as gender, age, or course of study. Other variables are more complex, such as socioeconomic status, academic achievement, or attitude toward school. Variables may also include an aspect of the educational system, such as a specific teaching method or counseling program. Characteristics of the environment may also be variables, such as the amount of school funding or availability of computers. Therefore, once the general research topic has been identified, the researcher should identify the key variables of interest.
For example, a researcher is interested in low levels of literacy. Literacy itself is still a broad topic. In most instances, the broad topic and general variables need to be specifically identified. For example, the researcher needs to identify specific variables that define literacy: reading fluency (the ability to read a text out loud), reading comprehension (understanding what is read), vocabulary, interest in reading, etc. If a researcher is interested in motivation, what specific motivation variables are of interest: external motivation, goals, need for achievement, etc? Reading other research studies about your chosen topic will help you better identify the specific variables of interest.
Identifying the key variables is important for the following reasons:
- The key variables provide focus when writing the Introduction section.
- The key variables are the major terms to use when searching for research articles for the Literature Review.
- The key variables are the terms to be operationally defined if an Operational Definition of Terms section is necessary.
- The key variables provide focus to the Methods section.
- The Instrument will measure the key variables. These key variables must be directly measured or manipulated for the research study to be valid.
Research Design After the key variables have been identified, the researcher needs to identify how those variables will be studied, which is the heart of the research design. There are four primary research designs:
- Descriptive: Describes the current state of variables. For example, a descriptive study might examine teachers' knowledge of literacy development. This is a descriptive study because it simply describes the current state of teachers' knowledge of literacy development.
- Causal Comparative: Examines the effect of one variable that cannot be manipulated on other variables. An example would be the effect of gender on examination malpractice. A researcher cannot manipulate a person's gender, so instead males and females are compared on their examination malpractice behavior. Because the variable of interest cannot be manipulated, causal comparative studies (sometimes also called ex post facto) ccompare two groups that differ on the independent variable (e.g., gender) on the dependent variable (e.g., examination malpractice). Thus, the key identifying factor of a causal comparative study is that it compares two or more groups on a different variable.
- Correlational: Describes the relationship between variables. Correlational studies must examine two variables that have continuous values. For example, academic achievement is a continuous variable because students' scores have a wide range of values - oftentimes from 0 to 100. However, gender is not a continuous variable because there are only two categories that gender can have: male and female. A correlational study might examine the relationship between motivation and academic achievement - both continuous variables. Note that in a correlational design, both variables must be studied within the same group of individuals. In other words, it is acceptable to study the relationship between academic achievement and motivation in students because the two variables (academic achievement and motivation) are in the same group of individuals (students). However, it is extremely difficult to study two variables in two groups of people, such as the relationship between teacher motivation and student achievement. Here, the two variables are compared between two groups: teachers and students. I strongly advise against this latter type of study.
- Experimental and Quasi-Experimental: Examines the effect of a variable that the researcher manipulates on other variables. An experimental or quasi-experimental study might examine the effect of telling stories on children's literacy skills. In this case, the researcher will "manipulate" the variable of telling stories by placing half of the children in a treatment group that listens to stories and the other half of children in a control group that gets the ordinary literacy instruction. The difference between an experimental design and quasi-experimental design is described in Step 4: Research Design.
Descriptive studies are the most simple research design and provide the least amount of information about improving education. Therefore, descriptive studies should only be conducted for first degree and diploma projects. Only in special cases should a Masters thesis be descriptive. Doctoral dissertations should aim for experimental or quasi-experimental studies.
Once the key variables and the research design have been identified, the rest of the study falls into place.
- The purpose, research questions, and hypotheses will be written about the variables based on the research design.
- The Instruments will be developed to measure the key variables and the Instruments section in Chapter 3 is written to describe the instruments.
- The Procedures section describes the treatment for experimental studies and/or how the instrument will be administered.
- The Method of Data Analysis describes how the data is summarized and tested based on the research questions and hypotheses.
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Copyright 2012, Katrina A. Korb, All Rights Reserved
One of the key factors in developing and testing scientific theories is the identification and operational definition of the variables that describe phenomena observed in the real world. Variables are measured in a research study, and they can have more than one value. There are several types of variables of interest to the researcher. Independent variables are stimuli that are manipulated in order to determine their effect on the value of dependent variables. Extraneous variables are variables that affect the value of the dependent variable but that are not related to the question under investigation in the study. Intervening variables are variables that occur between the manipulation of the independent variable and the measurement of the dependent variable and that contaminate the relationship between the two. In order to be of use in scientific research, variables need to be operationally defined so that they can be measured and their effects analyzed.
Sociologists attempt to make sense out the world by observing the behavior of people within society, developing theories to explain this behavior, translating their theories into working hypotheses that can be tested, and conducting empirical research to test whether or not their theories are supported. Based on the results of the research, they then either accept or revise their theories in a continuing attempt to explain the world around them. One of the key factors in this process is the identification and operational definition of variables -- traits, characteristics, or other measurable factors that can have different values -- that impact the phenomenon of interest.
One is primarily interested in two types of variables: independent variables and dependent variables. The independent variable is the variable that is being manipulated by the researcher. For example, Dr. Harvey has a theory that the way that people dress affects how they are treated by others in the workplace. He believes that if people dress as if they are successful professionals (e.g., well-groomed, business attire), they will be treated that way and receive a disproportionately high percentage of raises, promotions, high performance appraisals, and other recognition. The independent variable in this theory is whether or not people dress like successful professionals. This is the variable that Dr. Harvey will manipulate in his research study to determine how it affects the way that people are treated in the workplace. The second major variable of interest to researchers is the dependent variable. The dependent variable (so called because its value depends on which level of the independent variable the subject received) is the response to the independent variable. In Dr. Harvey's research study, the dependent variable is the way that people are treated in the workplace. Dr. Harvey's theory is that the value of this variable (i.e., whether or not people receive recognition in the workplace) is dependent on how they dress.
Concepts such as "dressing as if one is a successful professional" and "how one is treated in the workplace," however, are rather nebulous and open to different interpretations. To one person, "professional attire" may be a power suit with white shirt and tie while to another it may be a clean polo shirt with Bermuda shorts rather than cutoffs. Therefore, to be of use to researchers, variables need to be operationally defined in such a way that they can be tested and statistically analyzed. An operational definition is a definition that is stated in terms that can be observed and measured. To turn his question into a hypothesis, Dr. Harvey needs to operationally define both the independent and dependent variables. For example, he may decide that "dressing professionally" means that the person wears a dark suit with a white shirt and tie for men and a dark suit with white blouse and pearls for women. Of course, this is not the only definition of "professional dress" possible. Business casual, blazers and slacks, or any number of other possibilities is also possible. However, since it is typically impossible to consider the entire range of possibilities in one research study, Dr. Harvey will have to restrict his study to include only those values of the independent variable that are of most interest. Similarly, Dr. Harvey will have to operationally define what it means not to dress professionally in the workplace (e.g., jeans and a t-shirt). These definitions, of course, restrict Dr. Harvey's hypothesis. His results will not really answer the question about "professional" versus "not professional" attire, but only about the difference in treatment that people wearing power suits receive from those who wear casual attire.
Based on this discussion, it would seem that Dr. Harvey would be well off to pick multiple operational definitions for the independent variable. Although in some ways this is true, operationally defining a variable can be a tricky proposition. The goal of operationally defining variables is not just so that they can be tested in research, but to adequately and accurately define them so that they completely represent the underlying concept as much as possible. For example, as discussed above, the concept of "dressing professionally" means different things to different people. These differences affect not only the persons who need to decide how to dress for success, but also the persons who judge them based on the clothing choices. For example, if one's boss is "old-fashioned" and dresses in a suit and tie, dressing in a suit and tie would be more likely to impress this person even if the standard for "business attire" for that company was jeans and a polo shirt.
In addition, Dr. Harvey will have to operationally define what he means by "how one is treated in the workplace." Operational definitions of this dependent variable could include the supervisor's performance appraisal ratings of the individual, the average time it takes before the person receives a raise or bonus, or whatever other factors Dr. Harvey thinks are indicative of success. Some statistical techniques allow researchers to design experiments where to test multiple conditions of both the independent and dependent variables (e.g., power suit, blazer and slacks, business casual, and casual clothing). However, given the infinite variety of human nature and behavior, it is unlikely that he will be able to include every possible condition in his operational definitions.
Operationally defining dependent variables in human research can be a complicated process. For example, the construct underlying the variable "success in the workplace" is a nebulous and complex concept. Unless one is willing to wait for the end of the subject's career and look back to determine the value of the ultimate criterion of how successful that person was in the end, one can only estimate the ultimate criterion of success in the workplace using one or more predictor measures that one can operationally define. The underlying criterion is a dependent or predicted measure that is used to judge the effectiveness of persons, organizations, treatments, or predictors. However, one does not truly know whether or not a person is successful until s/he retires and can look back on the entire career. Practically, however, this is typically not possible in social science research. Rather than choosing an ultimate criterion of success such as success at the point of retirement, it is typically necessary instead to pick an intermediate criterion of success such as how many promotions one receives within a given period of time, how many (or how large) the raises are that the person receives during that same time period, or the performance appraisal...