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By creating an account, you agree to Study. Explore over 4, video courses. Find a degree that fits your goals. We'll define the two methods of data analysis, quantitative and qualitative, and look at each of their various techniques. The lesson will then conclude with a summary and a quiz. Start Your Free Trial Today. An error occurred trying to load this video.
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What is Data Analysis? Importance of Training in the Hospitality Industry. Path to Competitive Advantage. How to Evaluate a Marketing Plan. Steps in the Material Requirements Planning Process. Limits to Generalization of a Research Study. Effective Communication in the Workplace: UExcel Workplace Communications with Computers: TExES Mathematics High School Algebra I: Holt McDougal Algebra 2: High School Algebra II: In this lesson, we'll learn about data analysis.
A Beginning Look at Data Analysis Let's imagine that you have just enrolled in your first college course. Methods of Data Analysis Okay, you have decided to prove that public school is better than private school, but now you need to figure out how you will collect the information and data needed to support that idea. Qualitative Data Analysis Techniques Qualitative research works with descriptions and characteristics.
Let's look at some of the more common qualitative research techniques: Want to learn more? Select a subject to preview related courses: Quantitative Data Analysis Techniques Quantitative research uses numbers. Let's look at some of the techniques: Lesson Summary Data analysis has two prominent methods: Unlock Your Education See for yourself why 30 million people use Study.
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Your goal is required. Email Email is required. 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. 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:
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.
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 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 . A simple summary for introduction to quantitative data analysis. It is made for research methodology sub-topic.
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 edupdf.gatative research focuses on gathering numerical data . Data analysis has two prominent methods: qualitative research and quantitative research. Each method has their own techniques. Each method .