Here research refers to activities aimed at obtaining new knowledge about the world, in the case of the social sciences the social world of people and their institutions and interactions. Here we are concerned solely with empirical research, where such knowledge is based on information obtained by observing what goes on in that world.
This unit briefly presented below, some basic or fundamental quantitative research concepts used in applied quantitative Analysis. This understanding is required for analysing.
3.1.1 Background, Definition and Analyzing Quantitative Research a) Background and Definition
In natural and social sciences, and sometimes in other fields, quantitative research is the systematic empirical investigation of observable phenomena via statistical, mathematical, or computational techniques. The objective of quantitative research is to develop and employ mathematical models, theories, and hypotheses pertaining to phenomena. The process of measurement is central to quantitative research because it provides the fundamental connection between empirical observation and mathematical expression of quantitative relationships. 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 widely used in psychology, economics, demography, sociology, marketing, community health, health & human development, gender studies, and political science; and less frequently in anthropology and history. Research in mathematical sciences, such as physics, is also "quantitative" by definition, though this use of the term differs in context. In the social sciences, the term relates to empirical methods originating
152
in both philosophical positivism and the history of statistics, in contrast with qualitative research methods.
In another definition, quantitative research refers to research conducted using numerical data. This research can be descriptive in nature or analyzed using inferential statistical methods for the purpose of hypothesis testing. This differs from qualitative research which tends to be exploratory in nature, using information that is not believed to be naturally quantifiable.
Applied research refers to a study conducted not for the purpose of mere curiosity, but for immediate application to a real-world problem. With that being said, applied quantitative research refers to research that provides statistical conclusions with the intentions of answering a specific question or solving a specific problem. The design of most quantitative studies also helps to ensure that personal bias does not impact the data.
Quantitative data can be analyzed in several ways.
Once a researcher has written the research question, the next step is to determine the appropriate research methodology necessary to study the question. The three main types of research design methods are qualitative, quantitative and mixed methods. The focus of this set of units is quantitative research.
Quantitative methods are used to examine the relationship between variables with the primary goal being to analyze and represent that relationship mathematically through statistical analysis. This is the type of research approach most commonly used in scientific research problems. Following is a list of characteristics and advantages of using quantitative methods:
i. The data collected is numeric, allowing for collection of data from a large sample size.
ii. Statistical analysis allows for greater objectivity when reviewing results and therefore, results are independent of the researcher.
iii. Numerical results can be displayed in graphs, charts, tables and other formats that allow for better interpretation.
iv. Data analysis is less time-consuming and can often be done using statistical software.
v. Results can be generalized if the data are based on random samples and the sample size was sufficient.
vi. Data collection methods can be relatively quick, depending on the type of data being collected.
vii. Numerical quantitative data may be viewed as more credible and reliable, especially to policy makers, decision makers, and administrators.
153
There are a variety of quantitative methods and sampling techniques that will be discussed in detail in the other units in this unit. However, following are examples of research questions where quantitative methods may be appropriately applied:
What is the difference in the number of calories consumed between male and female high school students?
What percentage of married couples seek couples counseling?
What are the top 5 factors that influence a student‘s choice of college or university?
b) Analyzing Quantitative Research
The first step in quantitative data analysis is to identify the levels or scales of measurement as nominal, ordinal, interval or ratio. See the Research Ready: Scales of Measurement unit for more information on the scales of measurement. This is an important first step because it will help you determine how best to organize the data. The data can typically be entered into a spreadsheet and organized or ―coded‖ in some way that begins to give meaning to the data.
The next step would be to use descriptive statistics to summarize or ―describe‖ the data. It can be difficult to identify patterns or visualize what the data is showing if you are just looking at raw data. Following is a list of commonly used descriptive statistics:
Frequencies – a count of the number of times a particular score or value is found in the data set
Percentages – used to express a set of scores or values as a percentage of the whole
Mean – numerical average of the scores or values for a particular variable
Median – the numerical midpoint of the scores or values that is at the center of the distribution of the scores
Mode – the most common score or value for a particular variable
Minimum and maximum values (range) – the highest and lowest values or scores for any variable
It is now apparent why determining the scale of measurement is important before beginning to utilize descriptive statistics. For example, nominal scales where data is coded, as in the case of gender, would not have a mean score. Therefore, you must first use the scale of measurement to determine what type of descriptive statistic may be appropriate. The results are then expressed as exact numbers and allow you to begin to give meaning to the data. For some studies, descriptive statistics may be sufficient if you do not need to generalize the results to a larger population. For example, if you are comparing the percentage of teenagers that smoke in private versus public high schools, descriptive statistics may be sufficient.
154
However, if you want to utilize the data to make inferences or predictions about the population, you will need to go another step farther and use inferential statistics.
Inferential statistics examine the differences and relationships between two or more samples of the population. These are more complex analyses and are looking for significant differences between variables and the sample groups of the population.
Inferential statistics allow you test hypotheses and generalize results to population as whole. Following is a list of basic inferential statistical tests:
Correlation – seeks to describe the nature of a relationship between two variables, such as strong, negative positive, weak, or statistically significant. If a correlation is found, it indicates a relationship or pattern, but keep in mind that it does not indicate or imply causation
Analysis of Variance (ANOVA) – tries to determine whether or not the means of two sampled groups is statistically significant or due to random chance. For example, the test scores of two groups of students are examined and proven to be significantly different. The ANOVA will tell you if the difference is significant, but it does not speculate regarding ―why‖.
Regression – used to determine whether one variable is a predictor of another variable. For example, a regression analysis may indicate to you whether or not participating in a test preparation program results in higher ECONOMICS scores for high school students.
SELF-ASSESSMENT EXERCISE
What are the steps involved in analysing quantitative data?
3.1.2 Differences between Quantitative & Qualitative Research
Research is a systematic investigation that aims to generate knowledge about a particular phenomenon. However, the nature of this knowledge varies and reflects your study objectives. Some study objectives seek to make standardised and systematic comparisons, others seek to study a phenomenon or situation in detail. These different intentions require different approaches and methods, which are typically categorised as either quantitative or qualitative. You have probably already made decisions about using qualitative or quantitative data for monitoring and evaluation. Perhaps you have had to choose between using a questionnaire or conducting a focus group discussion in order to gather data for a particular indicator.