You will also be provided with a list of helpful resources that will assist you in your own evaluative efforts. 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.
The results are then expressed as exact numbers and allow you to begin to give meaning to the data. For some studies, descriptive statistics may be sufficient if you do not need to generalize the results to a larger population.
For example, if you are comparing the percentage of teenagers that smoke in private versus public high schools, descriptive statistics may be sufficient. However, if you want to utilize the data to make inferences or predictions about the population, you will need to go anther step farther and use inferential statistics. Inferential statistics examine the differences and relationships between two or more samples of the population.
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. Performing correlational research is done to establish what the affect of one on the other might be and how that affects the relationship. Correlational research is conducted in order to explain a noticed occurrence.
In correlational research the survey is conducted on a minimum of two groups. In most correlational research there is a level of manipulation involved with the specific variables being researched.
Once the information is compiled it is then analyzed mathematically to draw conclusions about the affect that one has on the other. Remember, correlation does not always mean causation. Typically, you should not make assumptions from correlational research alone. Causal-comparative research looks to uncover a cause and effect relationship.
This research is not conducted between the two groups on each other. Rather than look solely for a statistical relationship between two variables it tries to identify, specifically, how the different groups are affected by the same circumstance. As always the use of statistical analysis is engaged to synthesize the data in a clear method for presentation. Though questions may be posed in the other forms of research, experimental research is guided specifically by a hypothesis.
Sometimes experimental research can have several hypotheses. A hypothesis is a statement to be proven or disproved. Once that statement is made experiments are begun to find out whether the statement is true or not.
This type of research is the bedrock of most sciences, in particular the natural sciences. It will help you use the types of quantitative research to great effect. Quantitative research can be exciting and highly informative. It can be used to help explain all sorts of phenomena. The best quantitative research gathers precise empirical data and can be applied to gain a better understanding of several fields of study.
The world of information is waiting for you. What will you find?
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 .