The level of measurement can influence the type of analysis you can use. There are four levels of measurement:. Interval data — data is continuous and has a logical order, data has standardized differences between values, but no natural zero.
Ratio data — data is continuous, ordered, has standardized differences between values, and a natural zero. Once you have identified your levels of measurement, you can begin using some of the quantitative data analysis procedures outlined below. Due to sample size restrictions, the types of quantitative methods at your disposal are limited. However, there are several procedures you can use to determine what narrative your data is telling.
Below you will learn how about:. The first thing you should do with your data is tabulate your results for the different variables in your data set. This process will give you a comprehensive picture of what your data looks like and assist you in identifying patterns. The best ways to do this are by constructing frequency and percent distributions.
A frequency distribution is an organized tabulation of the number of individuals or scores located in each category see the table below. From the table, you can see that 15 of the students surveyed who participated in the summer program reported being satisfied with the experience. A percent distribution displays the proportion of participants who are represented within each category see below.
The most common descriptives used are:. Depending on the level of measurement, you may not be able to run descriptives for all variables in your dataset. The mode most commonly occurring value is 3, a report of satisfaction. By looking at the table below, you can clearly see that the demographic makeup of each program city is different. You can also disaggregate the data by subcategories within a variable. This allows you to take a deeper look at the units that make up that category.
In the table below, we explore this subcategory of participants more in-depth. From these results it may be inferred that the Boston program is not meeting the needs of its students of color.
This result is masked when you report the average satisfaction level of all participants in the program is 2. In addition to the basic methods described above there are a variety of more complicated analytical procedures that you can perform with your data. These types of analyses generally require computer software e. We provide basic descriptions of each method but encourage you to seek additional information e. For more information on quantitative data analysis, see the following sources: A correlation is a statistical calculation which describes the nature of the relationship between two variables i.
An important thing to remember when using correlations is that a correlation does not explain causation. A correlation merely indicates that a relationship or pattern exists, but it does not mean that one variable is the cause of the other. Although a distinction is commonly drawn between qualitative and quantitative aspects of scientific investigation, it has been argued that the two go hand in hand. Although quantitative investigation of the world has existed since people first began to record events or objects that had been counted, the modern idea of quantitative processes have their roots in Auguste Comte 's positivist framework.
Positivist scholars like Comte believed only scientific methods rather than previous spiritual explanations for human behavior could advance. Quantitative methods are an integral component of the five angles of analysis fostered by the data percolation methodology,  which also includes qualitative methods, reviews of the literature including scholarly , interviews with experts and computer simulation, and which forms an extension of data triangulation.
Quantitative methods have limitations. These studies do not provide reasoning behind participants' responses, they often do not reach underrepresented populations, and they may span long periods in order to collect the data. Statistics is the most widely used branch of mathematics in quantitative research outside of the physical sciences, and also finds applications within the physical sciences, such as in statistical mechanics. Statistical methods are used extensively within fields such as economics, social sciences and biology.
Quantitative research using statistical methods starts with the collection of data, based on the hypothesis or theory. Usually a big sample of data is collected — this would require verification, validation and recording before the analysis can take place.
Causal relationships are studied by manipulating factors thought to influence the phenomena of interest while controlling other variables relevant to the experimental outcomes.
In the field of health, for example, researchers might measure and study the relationship between dietary intake and measurable physiological effects such as weight loss, controlling for other key variables such as exercise. Quantitatively based opinion surveys are widely used in the media, with statistics such as the proportion of respondents in favor of a position commonly reported. In opinion surveys, respondents are asked a set of structured questions and their responses are tabulated.
In the field of climate science, researchers compile and compare statistics such as temperature or atmospheric concentrations of carbon dioxide. Empirical relationships and associations are also frequently studied by using some form of general linear model , non-linear model, or by using factor analysis. A fundamental principle in quantitative research is that correlation does not imply causation , although some such as Clive Granger suggest that a series of correlations can imply a degree of causality.
This principle follows from the fact that it is always possible a spurious relationship exists for variables between which covariance is found in some degree. Associations may be examined between any combination of continuous and categorical variables using methods of statistics. Views regarding the role of measurement in quantitative research are somewhat divergent. 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. This is because accepting a theory based on results of quantitative data could prove to be a natural phenomenon. He argued that such abnormalities are interesting when done during the process of obtaining data, as seen below:. In classical physics, the theory and definitions which underpin measurement are generally deterministic in nature.
In contrast, probabilistic measurement models known as the Rasch model and Item response theory models are generally employed in the social sciences.
Psychometrics is the field of study concerned with the theory and technique for measuring social and psychological attributes and phenomena. This field is central to much quantitative research that is undertaken within the social sciences. Quantitative research may involve the use of proxies as stand-ins for other quantities that cannot be directly measured. Tree-ring width, for example, is considered a reliable proxy of ambient environmental conditions such as the warmth of growing seasons or amount of rainfall.
Although scientists cannot directly measure the temperature of past years, tree-ring width and other climate proxies have been used to provide a semi-quantitative record of average temperature in the Northern Hemisphere back to A. When used in this way, the proxy record tree ring width, say only reconstructs a certain amount of the variance of the original record.
The proxy may be calibrated for example, during the period of the instrumental record to determine how much variation is captured, including whether both short and long term variation is revealed. In the case of tree-ring width, different species in different places may show more or less sensitivity to, say, rainfall or temperature: In most physical and biological sciences , the use of either quantitative or qualitative methods is uncontroversial, and each is used when appropriate.
In the social sciences, particularly in sociology , social anthropology and psychology , the use of one or other type of method can be a matter of controversy and even ideology, with particular schools of thought within each discipline favouring one type of method and pouring scorn on to the other. The majority tendency throughout the history of social science, however, is to use eclectic approaches-by combining both methods.
Qualitative methods might be used to understand the meaning of the conclusions produced by quantitative methods.
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, ) John W. Creswell defined quantitative research as an inquiry into a social or human problem.
Analyze Quantitative Data. Quantitative data analysis is helpful in evaluation because it provides quantifiable and easy to understand results. Quantitative data can be analyzed in a variety of different ways. Due to sample size restrictions, the types of quantitative methods at your disposal are limited. However, there are several.
In quantitative data analysis you are expected to turn raw numbers into meaningful data through the application of rational and critical thinking. Quantitative data analysis may include the calculation of frequencies of variables and differences between variables. A quantitative approach is usually. Quantitative Research. Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational ismagop.gatative research focuses on gathering numerical data and generalizing it across groups of people or to explain a.
A simple summary for introduction to quantitative data analysis. It is made for research methodology sub-topic. Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques. Quantitative research focuses on gathering.