**Statistics / Data Analysis in SPSS: Descriptive Statistics**

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Genre: Business / Data & Analytics | Language: English | +Project Files

Introduction to Descriptive Statistics / Data Analysis in SPSS and Beyond

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 LabelsInsert, Move, Modify, Sort, and Delete VariablesCreate Charts and GraphsMeasure 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.

**What are the requirements?**

Access to IBM SPSS Software (recommended)

**What am I going to get from this course?**

Over 47 lectures and 3.5 hours of content!Learn the basics of the SPSS software program, including how to enter and code values, run analyses, and interpret outputIn this course, you will gain proficiency in how to produce and interpret a number of different descriptive statistics in SPSS

**What is the target audience?**

Students seeking help with SPSSProfessionals desiring to augment their statistical skillsAnyone seeking to increase their data analytic skills

**Curriculum**

Lecture 1 Course Introduction 03:52

In this lecture, an overview of the course is provided, including how to access the data files and the output files.

Section 1: Introduction to SPSS

Lecture 2 Introduction to SPSS 09:00

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.

Lecture 3 Creating Value Labels for Groups 04:07

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".

Lecture 4 How to Copy and Apply Value Labels Across Several Variables at Once 07:32

In this lecture, how to copy value labels to multiple variables at once is illustrated. Likert scales are also explained, including how to code them in SPSS.

Lecture 5 Inserting, Moving, and Deleting Variables in SPSS 06:20

In this video, how to insert, move, and delete variables is illustrated. Shortcut keys are also described (including the benefits of using them).

There is no output file for this lecture, as no SPSS output is produced.

Lecture 6 How to Insert One or More Cases into SPSS 02:20

In this video, how to insert one or more cases is illustrated. “Cases” in SPSS are the rows in the data file when the Data View window is selected.

There is no output file for this lecture, as no SPSS output is produced.

Lecture 7 How to Sort One or More Variables in SPSS 04:35

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.

Lecture 8 Modifying Default Options in SPSS 04:42

This video examines how to modify a number of different default options in SPSS, including font type, style, and size, decimal places, value labels, and gridlines in the Data View window.

Lecture 9 How to Change the Columns Displayed in the Variable View Window 01:20

In this video, we take a look at how to modify the columns that are displayed in the variable view window when SPSS is opened.

Lecture 10 How to Edit SPSS Tables 03:02

Lecture 11 How to Copy a Dataset 02:22

Lecture 12 How to Save an SPSS Output File as a PDF (for Printing) 02:07

In this video, we take a look at how to save an SPSS output file as a pdf file, which can help for printing two-sided documents.

Section 2: Creating Charts and Graphs in SPSS

Lecture 13 Creating a Bar Chart 03:25

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).

Lecture 14 Creating a Histogram 04:21

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.

Lecture 15 Boxplot in SPSS 05:48

In this video, we take a look at how to construct and interpret a boxplot in SPSS. Each of the 5 key values in the boxplot are interpreted (minimum, Q1 median, Q3, and maximum), including the effect of outliers.

Lecture 16 Creating a Stem and Leaf Plot 03:12

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!

Lecture 17 Creating a Scatterplot 02:38

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".

Lecture 18 How to Create a Frequency Distribution Table in SPSS 03:10

How to create a frequency distribution table is illustrated in this lecture.

Lecture 19 Pie Charts 07:42

How to create a pie chart and modify chart options in SPSS is illustrated in this video.

Section 3: Central Tendency, Variability, z-Scores, and Correlation in SPSS

Lecture 20 Measures of Central Tendency: Mean, Median, and Mode 02:14

In this lecture, how to calculate the mean, median, and mode is illustrated using the frequencies procedure in SPSS.

Lecture 21 Measures of Variability: Standard Deviation and Variance 02:44

In this lecture, how to calculate the standard deviation and variance is illustrated. The measures are obtained first using the descriptives procedure and then the frequencies procedure in SPSS.

Lecture 22 The Best of both Worlds: The Mean and Standard Deviation 02:14

In this lecture, how to obtain the mean and standard deviation is illustrated using the frequencies procedure in SPSS.

Lecture 23 Obtaining Separate Means for Different Groups 05:52

In this lecture, the mean and standard deviation is obtained for separate groups of a categorical variable using the means procedure in SPSS.

Lecture 24 Finding z-Scores 04:11

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.

Lecture 25 Correlation 11:59

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).

Section 4: Statistics Videos I - Graphs and Central Tendency

Lecture 26 Mean, Median, and Mode (Measures of Central Tendency) 04:48

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.

Quiz 1 Quiz - Mean, Median, and Mode 5 questions

Lecture 27 Video Review of Quiz - Mean, Median, and Mode 03:30

In this video, the answers to the mean, median, and mode quiz are reviewed with explanations provided. The answers are also available in the attached PDF file.

Note: On problem #5, I state, "1, 3, 3, 5", but should have stated "1, 3, 3, 3, 5."

Lecture 28 Central Tendency and Skewed Distributions 03:34

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.

Quiz 2 Quiz - Central Tendency and Skewed Distributions 3 questions

Lecture 29 Video Review of Quiz - Central Tendency and Skewed Distributions 02:17

This video reviews the answers to the quiz on central tendency and skewed distributions. The answers are also available in the attached PDF file.

Lecture 30 The Weighted Mean 03:55

In this video, we take a look at the weighted mean, which can be used for finding an overall mean for two groups.

Quiz 3 The Weighted Mean 3 questions

Lecture 31 Video Review of Quiz - The Weighted Mean 03:12

In this video, the quiz answers are reviewed on the weighted mean.

Lecture 32 How to Create a Cumulative Frequency Distribution Table 02:18

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.

Lecture 33 How to Construct a Stem and Leaf Plot 02:42

In this video we examine how to construct a stem and leaf plot on a set of numbers ranging from the tens to fifties.

Lecture 34 Boxplots in SPSS 05:48

In this video, how to create a boxplot in SPSS is illsutrated.

Lecture 35 Calculating Percentiles in SPSS 05:34

In this video, we take a look at how to calculate percentiles in SPSS. Along with percentiles, how to interpret quartiles is also discussed.

Section 5: Statistics Videos II - Variability, Normal Distribution, and z-Scores

Lecture 36 Calculating the Standard Deviation and Variance - Step by Step 05:24

In this video, we take a look at how to calculate the variance and standard deviation by hand. Each step and calculation is illustrated in arriving at the solutions.

Quiz 4 Standard Deviation and Variance 2 questions

Lecture 37 Video Review of Quiz - Standard Deviation and Variance 05:45

This video reviews the quiz on the standard deviation and variance, illustrating step by step how to find each value.

Lecture 38 Normal Curve and z-Scores (68, 95, 99.7 Rule) 05:33

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.

Lecture 39 Properties of the z Score Normal Distribution 02:52

In this video lecture, we take a look at the properties of the z score normal distribution, including (1) that it is symmetrical, (2) that the mean, median, and mode are all equal to zero, and (3) that the standard deviation is equal to 1.

Quiz 5 Properties of the z Score Normal Distribution 5 questions

Lecture 40 Video Review of Quiz - Properties of the z-Score Normal Distribution 02:23

This video reviews the answers to the quiz, Properties of the z-Score Normal Distribution.

Lecture 41 Solving for z-Scores 04:08

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.

Quiz 6 Solving for z-Scores 5 questions

Lecture 42 Video Review of Quiz - Solving for z-Scores 03:38

Lecture 43 Solving for X Given a z-Score 04:47

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).

Quiz 7 Solving for X Given a z-Score 5 questions

Lecture 44 Video Review of Quiz - Solving for X Scores Given a z-Score 04:13

In this video, the answers are reviewed to the quiz, Solving for X Scores Given a z-Score.

Section 6: Conclusion & Course Previews

Lecture 45 Preview of Inferential Statistics Course - One sample t Test (Part 1) 09:07

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)

And more!

Lecture 46 New Upcoming Course by QS - Entering Survey Data and Likert Scales 07:42

This video previews content from our upcoming course, Survey Data and Likert Scale Analysis. Course is now available!

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