Hypothesis testing is used by businesses when determining the probability of an event happening under specific conditions. It is generally done by collecting customer data from surveys and then using hypothesis testing quantitative analysis tools to determine the likelihood of a member of the general population to have the same response or characteristic. The accuracy of hypothesis testing depends largely on the size of the sample population, randomly selecting from the population, accuracy of the questions, and errors in collecting the information.
This is most commonly used by marketers to test a new product or gain insight into public opinion about current offerings. One of our editors will review your suggestion and make changes if warranted. Note that depending on the number of suggestions we receive, this can take anywhere from a few hours to a few days.
Thank you for helping to improve wiseGEEK! View slideshow of images above. Watch the Did-You-Know slideshow. Carrieanne Larmore Edited By: This Day in History. The Star Spangled Banner poem was written. 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:. Finally, the type of data analysis will also depend on the number of variables in the study.
Studies may be univariate, bivariate or multivariate in nature. The following Slideshare presentation, Quantitative Data Analysis explains the use of appropriate statistical analyses in relation to the number of variables being examined. Evaluation Toolkit — Analyze Quantitative Data — This resource provides an overview of four key methods for analyzing quantitative data. 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. 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.
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 simple summary for introduction to quantitative data analysis. It is made for research methodology sub-topic. Jun 09, · The collection of information in quantitative research is what sets it apart from other types. Quantitative research is focused specifically on numerical information, also known as ‘data.’ Because the research requires its conductor to use mathematical analysis to investigate what is being observed, the information collected must be in Author: April Klazema.
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. Quantitative Data Types and Tests. Quantitative Data Types and Tests. Skip to content; quantitative data is that which can be expressed numerically and is associated with a measurement scale; not all numbers constitute quantitative data (e.g. tax file number!) Analysis of variance (ANOVA): tests for differences between the .