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. Quantitative research most often uses deductive logic, in which researchers start with hypotheses and then collect data which can be used to determine whether empirical evidence to support that hypothesis exists.
Quantitative analysis requires numeric information in the form of variables. A variable is a way of measuring any characteristic that varies or has two or more possible values. Many characteristics are naturally numeric in nature such as years of education, age, income ; for these numeric variables, the numbers used to measure the characteristic are meaningful in that they measure the amount of that characteristic that is present.
Often researchers are interested in characteristics which are not numeric in nature such as gender, race, religiosity , but even these variables are assigned numeric values for use in quantitative analysis although these numbers do not measure the amount of the characteristic present.
Variables can thus be divided into numeric variables in which the numbers have meaning and categorical variables which are commonly words or ranges. Quantitative data can be collected in a variety of ways. In experimental settings, researchers can directly collect quantitative data such as reaction times, blood pressure or such data can be self-reported by research participants on a pretest or posttest.
It can be difficult to identify patterns or visualize what the data is showing if you are just looking at raw data. Following is a list of commonly used descriptive statistics:. It is now apparent why determining the scale of measurement is important before beginning to utilize descriptive statistics. For example, nominal scales where data is coded, as in the case of gender, would not have a mean score. Therefore, you must first use the scale of measurement to determine what type of descriptive statistic may be appropriate.
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.
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 Research Approach. Quantitative research most often uses deductive logic, in which researchers start with hypotheses and then collect data which can be used to determine whether empirical evidence to support that hypothesis exists.. Quantitative analysis requires numeric information in the form of variables. A variable is a way of measuring any characteristic that varies or has.
Analyze Quantitative Data Quantitative data analysis is helpful in evaluation because it provides quantifiable and easy to understand results. Quantitative data can be . 1/19 Quantitative data analysis. First of all let's define what we mean by quantitative data analysis. It is a systematic approach to investigations during which numerical data is collected and/or the researcher transforms what is collected or observed into numerical data.
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 ggettsy.cftative research focuses on gathering numerical data and generalizing it across groups of people or to explain a. 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.