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# Data Analysis: Techniques & Methods

## Analyze Quantitative Data

❶Password confirm does not match password. This means, that there is usually a substantial amount of mathematics used with a quantitative study.

## A Beginning Look at Data Analysis

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: 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.

Tracking involves tracking the behavior or actions of participants. A great example of this are websites that track customers that visit their sites. Experiments can be customized for the type of research product.

They are a way to test some factor. An example might be taking children from a public school and placing them in a private school for a day. Data analysis has two prominent methods: Each method has their own techniques.

Interviews and observations are forms of qualitative research, while experiments and surveys are quantitative research. To unlock this lesson you must be a Study. Did you know… We have over college courses that prepare you to earn credit by exam that is accepted by over 1, colleges and universities. You can test out of the first two years of college and save thousands off your degree.

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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.

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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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## Main Topics

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.

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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.