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Data Analysis: Techniques & Methods

A Beginning Look at Data Analysis

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1/19 Quantitative data analysis

Before you begin your analysis, you must identify the level of measurement associated with the quantitative data. The level of measurement can influence the type of analysis you can use. There are four levels of measurement:. Interval data — data is continuous and has a logical order, data has standardized differences between values, but no natural zero.

Ratio data — data is continuous, ordered, has standardized differences between values, and a natural zero. Once you have identified your levels of measurement, you can begin using some of the quantitative data analysis procedures outlined below.

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. 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. Immediately, you have an opinion of which system you feel is better, but you realize that conducting research is not about your own personal opinion.

Research is about gathering data that you can analyze and use to come to some sort of conclusion. So, before you begin your data collection, you realize that you have a lot to learn about the various methods and techniques of gathering data.

Before we look at the methods and techniques of data analysis, lets first define what data analysis is. Data analysis is the collecting and organizing of data so that a researcher can come to a conclusion. Data analysis allows one to answer questions, solve problems, and derive important information. So, for your assignment, you now know that the purpose of your assignment is to gather enough information to come to a conclusion about which school system is better.

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. There are two methods that a researcher can pursue: Qualitative research revolves around describing characteristics. It does not use numbers. A good way to remember qualitative research is to think of quality. Quantitative research is the opposite of qualitative research because its prime focus is numbers.

Quantitative research is all about quantity. Qualitative research works with descriptions and characteristics. Let's look at some of the more common qualitative research techniques:.

Participant observation is when the researcher becomes part of the group that they are observing. This technique can take a long period of time because the researcher needs to be accepted into the group so that they observe data that is natural.

Get FREE access for 5 days, just create an account. Direct observation is where the researcher is strictly observing and not taking part in any of the activities. An example of this might be observing a classroom in both a private and public school so that you can see the differences in teaching styles. With interviews, the researcher is able to talk with participants and ask them questions and gather their opinions. So for your research, you might ask the students why they like their school.

Case studies can be customized for each research study. The researcher develops a study that revolves around the participants. They can use a variety of techniques including from interviews, participant observations, etc. Quantitative research uses numbers. This means, that there is usually a substantial amount of mathematics used with a quantitative study. Let's look at some of the techniques:. The goal of a survey is to gather responses from the participants through questions. For your assignment about school systems, you might mail out a survey to the teachers at both public and private schools.

This will allow you to gather the different perspectives from each type of school. 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.

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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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Are you still watching? Your next lesson will play in 10 seconds. Add to Add to Add to. 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:

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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.

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﻿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.

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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 onlinepersonalloansforpeoplewithbadcredit.cftative 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 .