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. 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. Analyzing Quantitative Data — The following link discusses the use of several types of descriptive statistics to analyze quantitative data. Analyze Data — This website discusses how to determine the type of data analysis needed, descriptive statistics, inferential statistics, and useful software packages.
Descriptive and Inferential Statistics — This resources provides an overview of these types of statistical analyses and how they are used. This pin will expire , on Change. This pin never expires. Select an expiration date. About Us Contact Us. Search Community Search Community. Analyzing Quantitative Research The following module provides an overview of quantitative data analysis, including a discussion of the necessary steps and types of statistical analyses. List the steps involved in analyzing quantitative data.
Define and provide examples of descriptive statistical analyses. Define and provide examples of inferential statistical analyses. 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.
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 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. 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 ACT scores for high school students. It is important to note that regression analysis are like correlations in that causation cannot be inferred from the analyses. Quantitative Data Analysis from Asma Muhamad.
Resource Links Evaluation Toolkit — Analyze Quantitative Data — This resource provides an overview of four key methods for analyzing quantitative data. Page Options Share Email Link. Share Facebook Twitter LinkedIn. Pinning this post will make it stay at the top of its channel and widgets.
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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.
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.
A survey or any other quantitative research method applied to these respondents and the involvement of statistics, conducting and analyzing results is quite straightforward and less time-consuming. Wider scope of data analysis: Due to the statistics, this research method provides a wide scope of data collection. Analyzing Quantitative Research. The following module provides an overview of quantitative data analysis, including a discussion of the necessary steps and types of statistical analyses. Learning Objectives: List the steps involved in analyzing quantitative data. Define and provide examples of descriptive statistical analyses.
A simple summary for introduction to quantitative data analysis. It is made for research methodology sub-topic. 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.