It is predictive in nature and typically used when significant knowledge already exists on the subject which allows the prediction to be made.
Data is then collected, analyzed, and used to support or negate the hypothesis, arriving at a definite conclusion at the end of the research.
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It is always written as a statement and should be developed before any data is collected.
A complete hypothesis should include: the variables, the population, and the predicted relationship between the variables.
Commonly used in quantitative research, but not qualitative research which often seeks answers to open-ended questions.
Examples: A company wellness program will decrease the number sick days claimed by employees. Consuming vitamin C supplements will reduce the incidence of the common cold in teenagers.
SELF ASSESSMENT EXERISE
What is the relationship between hypothesis and research question?
4.2 Variables and Operational Definitions
The goal of quantitative research is to examine the relationships between variables. A variable is a characteristic or attribute of interest in the research study that can take on different values and is not constant. Variables may be straightforward and easy to measure including characteristics such as gender, weight, height, age, size, and time.
Other variables may be more complex and more difficult to measure. Examples of these types of variables may include socioeconomic status, attitudes, achievement, education level, and performance.
This unit will focus on five types of variables: independent, dependent, extraneous, moderator and mediator variables. The two primary types of variables are dependent and independent variables. An independent variable is the variable manipulated or changed by the researcher. The independent variable affects or determines the values of dependent variable. The dependent variable is sometimes referred to as the outcome variable because the resulting outcome of manipulating the independent variable is typically the focus of the research study. The dependent variable is the one that the researcher is attempting to predict or explain. The distinction between independent and dependent variables is especially important when studying cause-effect relationships.
Following are two examples:
A researcher wants to study the effectiveness of different dosages of a particular antibiotic in clearing an infection.
o The independent variable - varying dosages of antibiotic.
o The dependent variable - the presence or absence of infection following a specific time period.
The researcher plans to study the relationship between the amount of time spent in a study group and test scores.
o The independent variable – number of hours spent in a study group.
o The dependent variable – test scores.
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Extraneous variables, sometimes referred to as nuisance or confounding variables, are not the variables of primary interest. However, they are believed to be related to the independent or dependent variable and therefore, may impact the results. Researchers should attempt to control extraneous variables in order to attain meaningful results. If they cannot be controlled, extraneous variables should at least be considered when interpreting results.
A moderator variable is a variable that interacts with the independent variable and may influence the strength of the relationship between the independent and dependent variables. This variable is measured and taken into consideration, making it different than an extraneous variable. For example, if studying the relationship between exercise and weight loss, the number of calories consumed maybe a moderating variable.
Mediating variables, commonly referred to as intervening variables, are processes that may not be observable but link the independent and dependent variables. An instructor may have a new teaching approach for a mathematical concept and plans to study the use of this approach and its relationship to test scores. The differing levels at which students in the class are able to process abstract mathematical concepts is a mediating variable.
While it is important to identify, understand, and consider the variables within a study, the researcher must also consider the measurement of those variables and the types of values that may be collected. When measuring the values of variables, there are two main classifications: categorical and quantitative variables. Categorical variables are those that express a qualitative attribute and do not express a numerical ordering. These variables refer to different types or categories of phenomenon or characteristic. Some examples would include gender, eye colour, race, religion, payment method, or social status.
Quantitative variables vary in degree or amount and are expressed using numerical ordering. Height, weight, shoe size, income, and test scores are quantitative variables.
The specific way in which a variable is measured in a particular study is called the operational definition. It is critical to operationally define a variable in order to lend credibility to the methodology and to ensure the reproducibility of the results. Another study may measure the same variable differently. The operational definition also helps to control the variable by making the measurement constant. Therefore, when it comes to operational definitions of a variable, the more detailed the definition is, the better. For example, if the researcher was planning to weigh research subjects, there would several constructs that should be spelled out including what the subjects were to wear, whether or not they would wear shoes, what type of scale was being used, and time of day. It may also be important to define the measurement of the outcome. For example, if a study was examining the relationship of swimming on overall fitness, the researcher would need to define how the outcome of overall fitness would be measured.
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SELF-ASSESSMENT EXERCISE
What are the key variables in saying whether the information is important or not?
4.0 CONCLUSION
Quantitative research is often contrasted with qualitative research, which purports to be focused more on discovering underlying meanings and patterns of relationships, including classifications of types of phenomena and entities, in a manner that does not involve mathematical models. Qualitative research, on the other hand, inquires deeply into specific experiences, with the intention of describing and exploring meaning through text, narrative, or visual-based data, by developing themes exclusive to that set of participants. Quantitative research is generally closely affiliated with ideas from 'the scientific method', which can include: the generation of models, theories and hypotheses;
the development of instruments and methods for measurement; experimental control and manipulation of variables; collection of empirical data; modeling and analysis of data.
Measurement is often regarded as being only a means by which observations are expressed numerically in order to investigate causal relations or associations. However, it has been argued that measurement often plays a more important role in quantitative research. For example, within quantitative research, the results that are shown can prove to be strange. This is because accepting a theory based on results of quantitative data could prove to be a natural phenomenon
5.0 SUMMARY
This unit briefly presented some basic or fundamental quantitative research concepts.
This unit discusses the basics of measurement and scales of measurement commonly used in quantitative research. You now hopefully have a better understanding of the difference between quantitative and qualitative. The unit shows that measurement and scales, provides an excellent overview of measurement terminology. The four scales of measurements, a concept map of when to use each type of scale, specific examples and information concerning the development of a scale. In this unit, the four approaches to quantitative research are described and examples are provided. The type of data analysis will also depend on the number of variables in the study. Studies may be univariate, bivariate or multivariate in nature. Validity is seen by many as being the primary issue that should be examined. The unit explains the different types of variables in quantitative research and discusses operational definitions of variables. Identifying and defining variables is a critical first step in a research study and will impact the validity and reliability of the study.
6.0 TUTOR-MARKED ASSIGNMENT
i. Understanding the Differences between Constructs, Variables, and Operational Definitions
ii. Discuss the importance of validity, reliability, falsifiability, generalizability, and reproducibility in a quantitative study.
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iii. Discuss and provide examples of inferential statistical analyses in applied quantitative analysis
7.0 REFERENCES/FURTHER READINGS
Abramson, J. H., & Abramson, Z. H. (2008). Scales of Measurement. Research Methods in Community Medicine: Surveys, Epidemiological Research, Programme Evaluation, Clinical Trials, Sixth Edition, 125-132.
Adcock, R. (2001). Measurement validity: A shared standard for qualitative and quantitative research. In American Political Science Association (Vol. 95, No. 03, pp. 529-546). Cambridge University Press.
Bennett, J. A. (2000). Mediator and moderator variables in nursing research: Conceptual and statistical differences. Research in nursing & health, 23(5), 415-420.
Bernard, H. R., & Bernard, H. R. (2012). Social Research Methods: Qualitative and Quantitative Approaches. Sage.
Blaikie, N. (2003). Analysing quantitative data: From description to explanation. Sage.
Bryman, A., & Cramer, D. (1994). Quantitative data analysis for social scientists (rev.
Taylor & Frances/Routledge.
Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods approaches. Sage Publications, Incorporated.
Monitoring, Evaluation, Accountability and Learning (MEAL), Methods of data collection and analysis, Save the child, Open University
Neuman, W. L., & Robson, K. (2004). Basics of social research. Pearson.
Onwuegbuzie, A. J. (2000). Expanding the Framework of Internal and External Validity in Quantitative Research.
Punch, K. F. (2013). Introduction to social research: Quantitative and qualitative approaches. Sage.
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UNIT 2 QUANTITATIVE DATA CONCEPTS