The most common type of graph for quantitative data is the histogram. A histogram is a bar graph that is constructed by arranging the data into ranges. For instance, it can be used to create a bar graph to show the number of sales per month for products in different price ranges.
Since quantitative analysis tools cannot be used for qualitative data, a business cannot use a histogram for categorizing products in groups based on names, colors or locations. Linear regressions are a popular quantitative analysis tool that is used to determine the relationship between two sets of related data. If the analyst determines the data to have a strong correlation, the data can be graphed so that predictions can be made. For example, if there is a strong correlation between the number of daily Web site visitors and the advertisement revenue, then the analyst can determine how many visitors per month are needed for the Web site to earn a targeted amount of advertisement revenue.
When needing to make forecasts based on the results of several variables, a multiple regression analysis can be done through the use of more advanced calculations. 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: 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. 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.
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 .