Statistics/Data Analysis with SPSS: Descriptive Statistics
- 3.5 hours on-demand video
- 20 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
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- Learn the basics of the SPSS software program, including how to enter and code values, run analyses, and interpret output
- In this course, you will gain proficiency in how to produce and interpret a number of different descriptive statistics in SPSS
- Access to IBM SPSS Software (recommended)
Get marketable and highly sought after skills in this course that will increase your knowledge of data analytics, with a focus on descriptive statistics, an important tool for understanding trends in data and making important business decisions.
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Whether a student or professional in the field, learn the important basics of both descriptive statistics and IBM SPSS so that you can perform data analyses and start using descriptive statistics effectively.
By monitoring and analyzing data correctly, you can make the best decisions to excel in your work as well as increase profits and outperform your competition.
This beginner's course offers easy to understand step-by-step instructions on how to make the most of IBM SPSS for data analysis.
Make Better Business Decisions with SPSS Data Analysis
Create, Copy, and Apply Value Labels
Insert, Move, Modify, Sort, and Delete Variables
Create Charts and Graphs
Measure Central Tendency, Variability, z-Scores, Normal Distribution, and Correlation
Interpret and Use Data Easily and Effectively with IBM SPSS
IBM SPSS is a software program designed for analyzing data. You can use it to perform every aspect of the analytical process, including planning, data collection, analysis, reporting, and deployment.
This introductory course will show you how to use SPSS to run analyses, enter and code values, and interpret data correctly so you can make valid predictions about what strategies will make your organization successful.
Contents and Overview
This course begins with an introduction to IBM SPSS. It covers all of the basics so that even beginners will feel at ease and quickly progress. You'll tackle creating value labels, manipulating variables, modifying default options, and more.
Once ready, you'll move on to learn how to create charts and graphs, such as histograms, stem and leaf plots, and more. You'll be able to clearly organize and read data that you've collected.
Then you'll master central tendency, which includes finding the mean, median, and mode. You'll also learn how to measure the standard deviation and variance, as well as how to find the z-score.
The course ends with introductory statistics video lectures that dive deeper into graphs, central tendency, normal distribution, variability, and z-scores.
Upon completion of this course, you'll be ready to apply what you've learned to excel in your statistics classes and make smarter business decisions. You'll be able to use the many features in SPSS to gather and interpret data more effectively, as well as plan strategies that will yield the best results as well as the highest profit margins.
- Students seeking help with SPSS
- Professionals desiring to augment their statistical skills
- Anyone seeking to increase their data analytic skills
An introduction to SPSS is covered in this lecture.
The SPSS data files (for the entire course) are available under "downloadable materials" (see below) in this lecture. The file labeled "Data Files Descriptive Statistics in SPSS" contains the set of data files for the course.
Also, a pdf file of the results (the output file) is also available. The output file for this lecture is located below and is titled, "Introduction output"
All other output files are located within their respective lecture.
This lecture covers how to create value labels for different categories of a variable. In SPSS, numbers are required to be entered (in nearly all circumstances) to perform analyses. Value labels help us keep track of which group corresponds to a given number such as 1 = "male" and 2 = "female".
This video illustrates how to use the sort command in SPSS. The sort command is illustrated first on a single variable in SPSS; afterwards, the data set is sorted on two variables simultaneously. How to sort using both ascending (lowest values first) and descending (highest values first) order is shown.
How to create a bar chart in SPSS is covered in this lecture. Bar charts are typically created on categorical variables, such as gender, ethnicity, and so on. The bars of a bar chart are not touching (there are gaps in-between them) since the data are not continuous (they are categorical or discrete).
How to create a histogram in SPSS is covered in this lecture. Histograms are typically created on continuous variables, such as height, weight, high school GPA, and so on. Unlike the bar chart covered in the previous lecture, the bars of a histogram are touching (as long as there is a frequency of at least one for a given category) since the data are continuous.
How to create a stem and leaf plot is covered in this lecture. Stem and leaf plots are interesting alternatives to histograms, as they convey the same information as a histogram, while having the advantage of also presenting the actual values in the graph.
Interesting note: Unlike the bar chart and histogram, notice that the graphics for the stem and leaf plot are a bit antiquated and could use some updating!
How to create a scatterplot is covered in this lecture. Scatterplots contain one variable on the X-axis and another variable on the Y-axis. It's a good idea to create a scatterplot when conducting a correlation coefficient. Correlation is a topic covered in our next course, "Inferential Statistics in SPSS - Step by Step".
In this lecture, how to calculate the mean, median, and mode is illustrated using the frequencies procedure in SPSS.
In this lecture, how to calculate z scores on a variable is illustrated. After calculating z scores, the mean and standard deviation on the new z-score variable is found to show that the mean of the new variable is 0 and the standard deviation is 1 (within rounding error), which is a property of the z-score distribution.
In this video, we take a look at Pearson’s r correlation coefficient. We examine it first as a descriptive statistic (the topic of this class), then we take a look at it an inferential statistic (as a preview to our next course). The basic difference between these two approaches is the following: as a descriptive statistic, correlation describes the relationship between two variables, while as an inferential statistic, we test to see whether the correlation is significantly different from zero (in addition to describing the relationship).
This video lecture covers the mean, median, and mode. First the mode is covered, including examples of two modes (bimodal) and three or more modes (multimodal). Next, finding the median is covered for both an even and odd number of values. After the median, how to calculate the mean (arithmetic average) is covered.
In this video, we take a look at the relationship between the mean, median, and mode and asymmetrical (skewed) distributions. As the video illustrates, the order of the three measures of central tendency (where they fall on a number line in relation to each other) depends on whether a distribution is positively or negatively skewed.
In this video, we examine how to construct a cumulative frequency distribution table, which includes the columns X, f, and cf. X corresponds to the values (or scores) of a variable X, f is the frequency value for each X (how many of each X there are), and cf is the cumulative frequency.
In this video, the normal distribution and z scores are covered. First, properties of the normal distribution are described, including how the mean, median, mode are equal to zero and how the normal distribution is symmetrical. Next the areas under the curve are illustrated, closing with a demonstration of the 68, 95, 99.7 rule for values that are 1, 2, and 3 standard deviations away from the mean.
In this video lecture, z scores are covered, including how to solve for z scores for a number of different examples. Also illustrated is how the z score indicates the number of standard deviations a value is from the mean. For example, a z score of 1.5 indicates that a value is 1.5 standard deviations above the mean.
In this video, we take a look at how to solve for X given a z score, mean, and standard deviation. This not only is covered in many statistics texts, but is a very common procedure that is used in score reporting for standardized tests, such as IQ tests, the SAT, and so on. In creating these types of test scores, standard test companies have a z score for each test taker and then find their X value (for example, IQ score) using a certain mean and standard deviation (a popular one for IQ tests: mean = 100, standard deviation = 15).
In this video, the one sample t test is introduced from our Introductory Statistics in SPSS Course. In the course, several procedures are covered, including:
one sample t test (2 examples + confidence intervals and effect size)
independent samples t test (2 examples + confidence intervals and effect size)
dependent samples t test (2 examples + confidence intervals and effect size)
one-way between subjects ANOVA (2 examples + effect size)
Post hoc tests
One-way within subjects ANOVA (2 examples)
+ Post hoc tests
Correlation (2 examples)
Regression (2 examples)
Chi-square goodness of fit test (2 examples)
Chi-square test of independence (2 examples)