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Analyze Quantitative Data

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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. Like correlations, causation can not be inferred from regression. Quantitative Analysis in Evaluation Before you begin your analysis, you must identify the level of measurement associated with the quantitative data.

There are four levels of measurement: T-shirt size small, medium, large Example: Fahrenheit degrees Remember that ratios are meaningless for interval data. You cannot say, for example, that one day is twice as hot as another day. Items measured on a Likert scale — rank your satisfaction on scale of For example — 10 inches is twice as long as 5 inches This ratio hold true regardless of which scale the object is being measured in e. Below you will learn how about: Data Tabulation Descriptives Disaggregating the Data Moderate and Advanced Analytical Methods The first thing you should do with your data is tabulate your results for the different variables in your data set.

This will help you determine: The most common descriptives used are: Mean — the numerical average of scores for a particular variable Minimum and maximum values — the highest and lowest value for a particular variable Median — the numerical middle point or score that cuts the distribution in half for a particular variable Calculate by: Listing the scores in order and counting the number of scores If the number of scores is odd, the median is the number that splits the distribution If the number of scores is even, calculate the mean of the middle two scores Mode — the most common number score or value for a particular variable Depending on the level of measurement, you may not be able to run descriptives for all variables in your dataset.

After tabulating the data, you can continue to explore the data by disaggregating it across different variables and subcategories of variables. Crosstabs allow you to disaggregate the data across multiple categories.

Correlation Regression Analysis of variance These types of analyses generally require computer software e. Correlation A correlation is a statistical calculation which describes the nature of the relationship between two variables i. Analysis of Variance An analysis of variance ANOVA is used to determine whether the difference in means averages for two groups is statistically significant. Regression Regression is an extension of correlation and is used to determine whether one variable is a predictor of another variable.

The three main types of research design methods are qualitative, quantitative and mixed methods. The focus of this set of modules is qualitative research. However, the following introductory video, A Brief Introduction to Research Design, offers a brief explanation of each method and a comparison. 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: There are a variety of quantitative methods and sampling techniques that will be discussed in detail in the other modules in this unit. However, following are examples of research questions where quantitative methods may be appropriately applied: The collection of numerical data through quantitative research methods lends itself well to large variety of research questions.

The following modules in this series will explore when to choose quantitative methods, how to write a good research question, types of quantitative methods, data analysis, ethics and many other topics that will lead to better understanding of quantitative research. Introduction to Quantitative Research — The following PDF is an article that provides an introduction to quantitative research. Overview of Quantitative Research — Following is a narrated PowerPoint presentation that provides an excellent overview of quantitative research and the issues that will be discussed in these Research Ready modules.

A Basic Introduction — Following is a link to a Slideshare presentation that offers and overview to quantitative research and data. Strengths and Weaknesses of Quantitative Research — This link provides lists of the strengths and weaknesses of quantitative research.

Research Methods — This resource describes the basic characteristics of quantitative research and gives an overview of methodologies, as well as a comparison with qualitative research. Qualitative and Quantitative Research — The link below contains a chart that describes the characteristics of quantitative research and compares them the characteristics of qualitative research.


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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.

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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.

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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 techniques. 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.

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Quantitative Research Definition: Quantitative research, in marketing, is a stimulating and highly educational technique to gather information from existing and potential customers using sampling methods and sending out online surveys, online polls, questionnaires etc., the results of which can be. Data analysis has two prominent methods: qualitative research and quantitative research. Each method has their own techniques. Each method has their own techniques.