# Descriptive and inferential statistics in research pdf

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## Basic statistical tools in research and data analysis

Descriptive statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data. Descriptive statistics are typically distinguished from inferential statistics. With descriptive statistics you are simply describing what is or what the data shows.

With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone. For instance, we use inferential statistics to try to infer from the sample data what the population might think. Or, we use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study.

Descriptive Statistics are used to present quantitative descriptions in a manageable form. In a research study we may have lots of measures. Or we may measure a large number of people on any measure. Descriptive statistics help us to simplify large amounts of data in a sensible way.

Each descriptive statistic reduces lots of data into a simpler summary. For instance, consider a simple number used to summarize how well a batter is performing in baseball, the batting average.

This single number is simply the number of hits divided by the number of times at bat reported to three significant digits. A batter who is hitting. One batting. The single number describes a large number of discrete events.

This single number describes the general performance of a student across a potentially wide range of course experiences. Every time you try to describe a large set of observations with a single indicator you run the risk of distorting the original data or losing important detail.

Even given these limitations, descriptive statistics provide a powerful summary that may enable comparisons across people or other units. Univariate analysis involves the examination across cases of one variable at a time. There are three major characteristics of a single variable that we tend to look at:.

In most situations, we would describe all three of these characteristics for each of the variables in our study. The distribution is a summary of the frequency of individual values or ranges of values for a variable. The simplest distribution would list every value of a variable and the number of persons who had each value.

For instance, a typical way to describe the distribution of college students is by year in college, listing the number or percent of students at each of the four years. Or, we describe gender by listing the number or percent of males and females.

In these cases, the variable has few enough values that we can list each one and summarize how many sample cases had the value. But what do we do for a variable like income or GPA? With these variables there can be a large number of possible values, with relatively few people having each one. In this case, we group the raw scores into categories according to ranges of values. For instance, we might look at GPA according to the letter grade ranges.

Or, we might group income into four or five ranges of income values. One of the most common ways to describe a single variable is with a frequency distribution. Depending on the particular variable, all of the data values may be represented, or you may group the values into categories first e. Rather, the value are grouped into ranges and the frequencies determined.

Frequency distributions can be depicted in two ways, as a table or as a graph. The table above shows an age frequency distribution with five categories of age ranges defined.

The same frequency distribution can be depicted in a graph as shown in Figure 1. This type of graph is often referred to as a histogram or bar chart. Distributions may also be displayed using percentages. For example, you could use percentages to describe the:. There are three major types of estimates of central tendency:. The Mean or average is probably the most commonly used method of describing central tendency. To compute the mean all you do is add up all the values and divide by the number of values.

For example, the mean or average quiz score is determined by summing all the scores and dividing by the number of students taking the exam.

For example, consider the test score values:. The Median is the score found at the exact middle of the set of values. One way to compute the median is to list all scores in numerical order, and then locate the score in the center of the sample. For example, if there are scores in the list, score would be the median.

If we order the 8 scores shown above, we would get:. There are 8 scores and score 4 and 5 represent the halfway point. Since both of these scores are 20 , the median is If the two middle scores had different values, you would have to interpolate to determine the median.

The Mode is the most frequently occurring value in the set of scores. To determine the mode, you might again order the scores as shown above, and then count each one.

The most frequently occurring value is the mode. In our example, the value 15 occurs three times and is the model. In some distributions there is more than one modal value. For instance, in a bimodal distribution there are two values that occur most frequently.

Notice that for the same set of 8 scores we got three different values If the distribution is truly normal i. Dispersion refers to the spread of the values around the central tendency. There are two common measures of dispersion, the range and the standard deviation. The range is simply the highest value minus the lowest value. The Standard Deviation is a more accurate and detailed estimate of dispersion because an outlier can greatly exaggerate the range as was true in this example where the single outlier value of 36 stands apart from the rest of the values.

The Standard Deviation shows the relation that set of scores has to the mean of the sample. Again lets take the set of scores:. We know from above that the mean is So, the differences from the mean are:. Notice that values that are below the mean have negative discrepancies and values above it have positive ones.

Next, we square each discrepancy:. Here, the sum is Next, we divide this sum by the number of scores minus 1. Here, the result is This value is known as the variance. To get the standard deviation, we take the square root of the variance remember that we squared the deviations earlier.

This would be SQRT To see this, consider the formula for the standard deviation:. In the top part of the ratio, the numerator, we see that each score has the the mean subtracted from it, the difference is squared, and the squares are summed. In the bottom part, we take the number of scores minus 1. The ratio is the variance and the square root is the standard deviation. In English, we can describe the standard deviation as:. Although we can calculate these univariate statistics by hand, it gets quite tedious when you have more than a few values and variables.

Every statistics program is capable of calculating them easily for you. The standard deviation allows us to reach some conclusions about specific scores in our distribution. Assuming that the distribution of scores is normal or bell-shaped or close to it! For instance, since the mean in our example is This kind of information is a critical stepping stone to enabling us to compare the performance of an individual on one variable with their performance on another, even when the variables are measured on entirely different scales.

There are three major characteristics of a single variable that we tend to look at: the distribution the central tendency the dispersion In most situations, we would describe all three of these characteristics for each of the variables in our study. The Distribution The distribution is a summary of the frequency of individual values or ranges of values for a variable.

## Descriptive vs. Inferential Statistics: What's the Difference?

By Malcolm J. Brandenburg, Derald E. Wentzien, Riza C. Bautista, Agashi P. Nwogbaga, Rebecca G. Miller and Paul E.

## Descriptive Statistics

Published on September 4, by Pritha Bhandari. Revised on March 2, While descriptive statistics summarize the characteristics of a data set, inferential statistics help you come to conclusions and make predictions based on your data. When you have collected data from a sample , you can use inferential statistics to understand the larger population from which the sample is taken. Table of contents Descriptive versus inferential statistics Estimating population parameters from sample statistics Hypothesis testing Frequently asked questions about inferential statistics.

Sign in. Statistics plays a main role in the field of research. It helps us in the collection, analysis and presentation of data.

Statistics analysis provides you with the best information on methods to collect data. Data collection helps you make informed decisions in the workplace while rendering evidence that's beneficial in achieving your goals. The differences between descriptive and inferential statistics can assist you in delineating these concepts and how to calculate certain statistics.

#### COMMENT 2

• When analysing data, such as the marks achieved by students for a piece of coursework, it is possible to use both descriptive and inferential statistics in your analysis of their marks. Kuytusleper - 10.05.2021 at 02:33
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