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. An analysis of variance ANOVA is used to determine whether the difference in means averages for two groups is statistically significant.
For example, an analysis of variance will help you determine if the high school grades of those students who participated in the summer program are significantly different from the grades of students who did not participate in the program. Regression is an extension of correlation and is used to determine whether one variable is a predictor of another variable. A regression can be used to determine how strong the relationship is between your intervention and your outcome variables. More importantly, a regression will tell you whether a variable e.
A variable can have a positive or negative influence, and the strength of the effect can be weak or strong. 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. About Us Contact Us. Search Community Search Community. An Overview of Quantitative Research This modules provides a basic overview of quantitative research, including its key characteristics and advantages.
Describe the uses of quantitative research design. Provide examples of when quantitative research methodology should be used. Discuss the strengths and weaknesses of quantitative research.
The data collected is numeric, allowing for collection of data from a large sample size. Statistical analysis allows for greater objectivity when reviewing results and therefore, results are independent of the researcher. Numerical results can be displayed in graphs, charts, tables and other formats that allow for better interpretation.
Data analysis is less time-consuming and can often be done using statistical software. Results can be generalized if the data are based on random samples and the sample size was sufficient. Data collection methods can be relatively quick, depending on the type of data being collected. Numerical quantitative data may be viewed as more credible and reliable, especially to policy makers, decision makers, and administrators.
How often do college students between the ages of access Facebook? 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? How many organized sports activities has the average 10 year old child competed in? Planning, conducting, and evaluating quantitative. Qualitative, quantitative, and mixed methods approaches. Basics of social research. Qualitative and quantitative approaches. The lancet , , Real world research Vol.
Page Options Share Email Link.
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. In this section, you will learn about the most common quantitative analysis procedures that are used in small program evaluation.
Quantitative methods are research techniques that are used to gather quantitative data — information dealing with numbers and anything that is measurable e.g. Statistics, tables and graphs, are often used to present the results of these methods.
Quantitative Data Analysis Techniques for Data-Driven Marketing Posted by Jiafeng Li on April 12, in Market Research 10 Comments Hard data means nothing to marketers without the proper tools to interpret and analyze that data. My e-book, The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step approach contains a detailed, yet simple explanation of quantitative data analysis methods. The e-book explains all stages of the research process starting from the selection of the research area to .
Data analysis has two prominent methods: qualitative research and quantitative research. Each method has their own techniques. Each method has their own techniques. 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 numerical data and generalizing it across groups.