Quantitative Research Methods

Quantitative means quantity which implies that there is something that can be counted. Quantitative research has been defined in many ways. It is the kind of research that involves the tallying, manipulation or systematic aggregation of quantities of data (Henning, 1986) John W. Creswell defined quantitative research as an inquiry into a social or human problem based on testing a theory composed a theory composed of variables, measured with numbers, and analysed with statistical procedures in order to determine whether the predictive generalisations of the theory holds true. (Creswell, 2003) On the other hand, another author defined quantitative research as the collection of numerical data in order to explain, predict and/or control phenomena of interest.

Quantitative research is explaining phenomena by collecting numerical data that are analysed using mathematically based methods (Aliaga & Gunderson, 2000) Quantitative research can also be said to be a research based on traditional scientific methods, which generates numerical data and usually seeks to establish causal relationships (or association) between two or more variables, using statistical methods to test the strength and significance of the relationships (A dictionary of Nursing, 2008) Simply put, quantitative research reflects the philosophy that everything can be described according to some type of numerical system. It uses numerical analysis; in essence this approach reduces the data into numbers, for instance the percentage of teenage mothers in Lagos. The research knows in advance what he/she is looking for and all aspects of the study are carefully designed before the data is collected. Quantitative research methods reflect the philosophy that everything can be described according to some type of numerical system for instance The height of a person (In metres)

The weight of a person ( In kilograms)
The age of a person ( in years and months)
Quantitative Research is used to determine exact figures and facts. It is used to determine ‘precise measurements’ of things; it is also used to answer questions such as how much, how often, how many, when, and who (Cooper and Schindler, 2006). As quantitative research is essentially about collecting numerical data to explain a particular attribute, particular questions are more suited to be answered using quantitative methods for example I. How many males get a first class degree at university compared to females? II. What percentage of teachers and school leaders belong to ethnic minority groups? From the questions above, it can be seen that quantitative research methods emphasise on objective measurements and numerical analysis of data and generalising the results. It should be noted that many data that do not naturally appear in quantitative form can be collected in a quantitative way. This done by designing research instruments aimed at specifically converting phenomena that do not naturally exist in quantitative form into quantitative data, which can then be analysed statistically. Examples of this are attitudes and beliefs

Also a researcher can collect data on a wide number of phenomena, and make them quantitative through data collection instruments, which in turn implies that quantitative research methods are quite flexible. A common misconception about quantitative research methods that puts a lot of people off doing quantitative research is that the researcher needs to be an expert in mathematics and statistics; this is not entirely true because computer software allows the analysis to be done quickly and relatively easily. The process of quantitative Research

The researcher will have one or more hypothesis. These are the questions that they want to address, which include predictions about possible relationships between the things, they want to investigate (Variables), In order to find answers to these questions, the researcher will also have various instruments and materials (e.g. paper or computer tests, observation) and a clearly defined plan of action. Data is collected by various means following a strict procedure and prepared for Statistical Analysis. Nowadays this is carried out with the aid of sophisticated statistical computer packages. The analysis enables the researcher to determine to what extent, there is a relationship between two or more variables. This could be a simple association (for example people who exercise on a daily basis have lower blood pressure) or a causal relationships (for example daily exercise actually leads to lower blood pressure) Statistical analysis permits the researcher to discover complex causal relationships and to determine to what extent one variable influences another. The results of statistical analysis are presented in a standard way, the end result being a Probability value. Reasons for selecting a quantitative paradigm

Researcher’s world view
A researcher’s comfort with ontological, epistemological, axiological, rhetorical, and methodological assumptions of the quantitative paradigm Training and Experience of the researcher
Technical Writing Skills, computer statistical skills, library skills Researcher’s Psychological attributes
Comfort with rules and guidelines for conducting research; low tolerance for ambiguity; time for a study short duration Nature of the problem
Previously studied by other researchers so that body of literature exists, known variables; existing theories Audience of the study
Individuals accustomed to/ supportive of quantitative studies

Peculiarities of Quantitative Research
I. Deductive or Top-down: The researcher test hypothesis and theory with data. It involves the systematic creation of a hypothesis and subjecting it to an empirical test. The main emphasis of quantitative research is on deductive reasoning which tends to move from the general to the specific, the validity of conclusions is shown to be dependent on one or more premises (prior statements, findings or conditions) being valid. For example Aristotle’s famous example of deductive reasoning was: All men are mortal, Socrates is a man, and therefore Socrates is mortal. If the premises of an arguments are inaccurate, then the argument is inaccurate. II. The results are based on larger sample sizes that are representative of the populations. It tends to be associated with large-scales studies and with a specific focus, often condensing information from a large number of specific occurrences to search for general description. In quantitative research methods, the researcher rarely has access to all members of a particular group.

However, they are usually interested in being able to make inferences from their study about the larger groups; for this reason, it is important that the people involved in the study are a representative sample of the wider population/group. For example, generalisations about psychiatrists should be based on a study involving psychiatrists and not one based on medical students. III. All aspects of the study are carefully defined before data is collected, this means that terms must be defined by the steps or operations used to measure them. Such a procedures is necessary to eliminate any confusion in meaning and communications. Consider the statement “Anxiety causes students to score poorly in tests “; one might ask, what is meant by anxiety? Stating that anxiety refers to being tense or some other term only adds to the confusion. However, stating that anxiety refers to a score over a criterion level on an anxiety scale, enables others to realise what you mean by anxiety. IV. It tends to be associated with researcher detachment, producing objective data.

Objectivity is very important in quantitative research, consequently researchers take great care to avoid their own presence, behaviour, or attitude affecting the results (for example by changing the situation being studied or causing participants to behave differently.) They also critically examine their methods and conclusions for any possible bias. Researchers go to great lengths to ensure that they are measuring what they claim to be measuring. V. It is usually based upon numerical measurements and thus tends to use numbers and statistical methods as they research indicators and tools. VI. It tends to be associated with pre-determined research design, using measurement and analysis in a systematic and logically ordered fashion that may be replicated relatively easily by other researchers. To be replicable, the data obtained in an experiments must be reliable; that is, the same result must be obtained, if the study is repeated by another researcher. If observations are not repeatable, our descriptions and explanations are thought to be unreliable. VII. The researcher attempts to study behaviour under controlled conditions.

This is the most important characteristic because it enables the researchers to identify the causes of his or her observations. Experiments are conducted in an attempt to answer certain questions; they represent attempts to identify why something happens, what causes some event or under what conditions does an event occur. Control is necessary in order to provide unambiguous answers to such questions. Controlled inquiry is absolutely essential to this because without it, the cause of an effect cannot be isolated. VIII. The research can be carried out independent of the researcher. The researcher does not have to be as involved as the qualitative researcher and similar results are obtained no matter who carries out the study. IX. Quantitative research methods are cheaper to implement and are standardised so comparisons can be easily made and the size of the effect can usually be measured. Other characteristics include

It tends to be associated with analysis
Facts are value-free and unbiased
It establishes relationships and causation
The data used are variables
The final report is normally a statistical report (with correlations, comparisons of means, and reporting of statistical significance of findings) it is easier to compile into chart or graph. Types of Quantitative Research Methods

1. Experimental Research Methods: With quantitative research being rooted in the scientific method, it is structured in an experimental fashion. This is especially true in the natural sciences, where they try to prove causes and effects. This is a study where an effort is made to identify and impose control over all other variables except one; an independent variable is manipulated to determine the effects on the dependent variable. Subjects are randomly assigned to experimental treatments rather than identified in naturally occurring groups. Examples include The effect of a new treatment plan on breast cancer

The effect of positive reinforcement on attitude toward school 2. Quasi Experiments/ Causal – Comparative Research Methods Attempts to establish cause-effect relationships among the variables, this is similar to experimental research but with some key differences, an independent variable is identified but not manipulated; and the effects of the independent variable on the dependent variable are measured The researcher does not randomly assign groups and must use ones that are naturally formed or pre-existing groups. When analysis and conclusions are made, determining causes must be done carefully, as other variables, both known and unknown could still affect the outcomes. Examples include The effect of preschool attendance on social maturity at the end of the first grade The effect of part time employment on the achievement of high school students.

3. Descriptive or Non- experimental Research Method: seeks to describe the current status of an identified variable. This research is defined to provide systematic information about a phenomenon. The researcher does not usually begin with a hypothesis but is likely to develop one after collecting data; the analysis and synthesis of the data provide the best hypothesis. Example A description of how second grade students spend their time during summer vacation. A description of the kinds of physical activities that typically occur in nursing homes, and how frequently each occurs. 4. Correlational Research: this attempts to determine the extent of a relationship between two or more variables using statistical data; relationships between and among a number of facts are sought and interpreted. This type of research will recognise trends and patterns in data, but it does not go so far in its analysis to prove causes for these observed patterns. Examples The relationship between intelligence and self esteem.