Want to watch this again later? Factors That Influence Customer Loyalty. 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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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. Below you will learn how about: Data Tabulation Descriptives Disaggregating the Data Moderate and Advanced Analytical Methods The first thing you should do with your data is tabulate your results for the different variables in your data set.
This will help you determine: The most common descriptives used are: Mean — the numerical average of scores for a particular variable Minimum and maximum values — the highest and lowest value for a particular variable Median — the numerical middle point or score that cuts the distribution in half for a particular variable Calculate by: Listing the scores in order and counting the number of scores If the number of scores is odd, the median is the number that splits the distribution If the number of scores is even, calculate the mean of the middle two scores Mode — the most common number score or value for a particular variable Depending on the level of measurement, you may not be able to run descriptives for all variables in your dataset.
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 igmosb.gqtative 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 .