SPSS统计分析实战：单变量、双变量与多变量方法

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《SPSS数据分析：单变量、双变量与多变量统计》是一本由Daniel J. Denis编著的实用指南，专为那些希望快速掌握实际数据分析技巧的学习者设计。该书于2018年9月25日首次出版，适合统计学初学者和研究生阅读，也对研究人员和专业人士在执行基本统计分析和数据管理任务时非常有用。本书强调实践中的数据分析，而非过于深入的数学概念，让读者能在理解和应用概念的同时，利用专业数据分析师常用的计算工具。它按照逻辑顺序逐步介绍统计原理，然后逐步引入SPSS软件，涵盖了广泛的内容，包括： 1. **探索性数据分析、基础统计和视觉展示**：这部分引导读者理解数据的基本特征和可视化方法。 2. **SPSS中的数据管理**：学习如何组织、清洗和准备数据以进行后续分析。 3. **推断性测试，如相关性、计数和均值的检验**：介绍了如何进行假设检验和确定统计显著性。 4. **功效分析和样本量估计**：教授如何计算研究所需的样本大小以达到所需的效果。 5. **方差分析（固定效应和随机效应）**：涵盖了两种类型的实验设计分析。 6. **重复测量ANOVA**：探讨时间序列或组间比较的研究设计。 7. **简单和多元线性回归**：学习预测变量与响应变量之间的关系。 8. **逻辑回归**：用于分类问题的统计模型。 9. **多元方差分析（MANOVA）和判别分析**：处理多个自变量对一个或多个因变量的影响。 10. **主成分分析**：用于数据降维和变量组合。 11. **探索性因子分析**：揭示潜在结构和变量间的关系。 12. **非参数测试**：针对不适合正态分布的数据的统计方法。《SPSS数据分析：单变量、双变量与多变量统计》以简明易懂的方式，帮助读者在实践中掌握统计分析技巧，并将理论应用于实际研究项目。无论是在社会科学、行为科学还是自然科学领域，这本书都是快速获取和应用统计知识的重要资源。此外，书中还提供了许多公式和实例，便于读者理解和应用。对于任何希望通过SPSS进行数据处理的专业人士来说，这是一本不可或缺的“实战手册”。

In this second chapter, we provide a brief introduction to SPSS version 22.0 software. IBM SPSS

provides a host of online manuals that contain the complete capabilities of the software, and beyond

brief introductions such as this one should be consulted for specifics about its programming options.

These can be downloaded directly from IBM SPSS’s website. Whether you are using version 22.0 or an

earlier or later version, most of the features discussed in this book will be consistent from version to

version, so there is no cause for alarm if the version you are using is not the one featured in this book.

This is a book on using SPSS in general, not a specific version. Most software upgrades of SPSS ver-

sions are not that different from previous versions, though you are encouraged to keep up to date with

SPSS bulletins regarding upgrades or corrections (i.e. bugs) to the software. We survey only select

possibilities that SPSS has to offer in this chapter and the next, enough to get you started performing

data analysis quickly on a host of models featured in this book. For further details on data manage-

ment in SPSS not covered in this chapter or the next, you are encouraged to consult Kulas (2008).

2.1 How toCommunicate withSPSS

There are basically two ways a user can communicate with SPSS – through syntax commands

entered directly in the SPSS syntax window and through point‐and‐click commands via the graphi-

cal user interface (GUI). Conducting analyses via the GUI is sufficient for most essential tasks fea-

tured in this book. However, as you become more proficient with SPSS and may require advanced

computing commands for your specific analyses, manually entering syntax code may become neces-

sary or even preferable once you become more experienced at programming. In this introduction, we

feature analyses performed through both syntax commands and GUI. In reality, the GUI is simply a

reflection of the syntax operations that are taking place “behind the scenes” that SPSS has automated

through easy‐to‐access applications, similar to how selecting an app on your cell phone is a type of

fast shortcut to get you to where you want to go. The user should understand from the outset how-

ever that there are things one can do using syntax that cannot automatically be performed through

the GUI (just like on your phone, there is not an app for everything!), so it behooves one to learn at

least elementary programming skills at some point if one is going to work extensively in the field of

data analysis. In this book, we show as much as possible the window commands to obtaining output

and, in many places, feature the representative syntax should you ever need to adjust it to customize

your analysis for the given problem you are confronting. One word of advice – do not be

Introduction toSPSS

2 Introduction toSPSS

intimidated when you see syntax, since as mentioned, for the majority of analyses presented in this

book, you will not need to use it specifically. However, by seeing the corresponding syntax to the

window commands you are running, it will help “demystify” what SPSS is actually doing, and then

through trial and error (and SPSS’s documentation and manuals), the day may come where you are

adjusting syntax on your own for the purpose of customizing your analyses, such as one regularly

does in software packages such as R or SAS, where typing in commands and running code is the

habitual way of proceeding.

2.2 Data View vs. Variable View

When you open SPSS, you will find two choices for SPSS’s primary window–Data View vs. Variable

View (both contrasted in Figure2.1). The Data View is where you will manually enter data into SPSS,

whereas the Variable View is where you will do such things as enter the names of variables, adjust the

numerical width of variables, and provide labels for variables.

The case numbers in SPSS are listed along the left‐hand column. For

instance, in Figure2.1, in the Data View (left), approximately 28 cases are

shown. In the Variable View, 30 cases are shown. Entering data into SPSS is

very easy. As an example, consider the following small hypothetical data set

(left) on verbal, quantitative, and analytical scores for a group of students

ona standardized “IQ test” (scores range from 0 to 100, where 0indicates

virtually no ability and 100 indicates very much ability). The “group” variable

denotes whether students have studied “none” (0), “some” (1), or “much” (2).

Entering data into SPSS is no more complicated than what we have done

above, and barring a few adjustments, we could easily go ahead and start

conducting analyses on our data immediately. Before we do so, let us have

a quick look at a few of the features in the Variable View for these data and

how to adjust them.

Figure 2.1 SPSS Data View (left) vs. Variable View (right).

2.3 Missing Data inSPSS: Think Twice Before Replacing Data! 13

Any attempt to replace a missing data point, regard-

less of the approach used, is nonetheless an educated

“guess” at what that data point may have been had the

participant answered or it had not gone missing.

Presumably, the purpose of your scientific investigation

was to do science, which means making measurements on

objects in nature. In conducting such a scientific investiga-

tion, the data is your only true link to what you are study-

ing. Replacing a missing value means you are prepared to

“guesstimate” what the observation is, which means it

is no longer a direct reflection of your measurement

process. In some cases, such as in repeated measures or

longitudinal designs, avoiding missing data is difficult

because participants may drop out of longitudinal studies

or simply stop showing up. However, that does not necessarily mean you should automatically replace

their values. Get curious about your missing data. For our IQ data, though we may be able to attribute

the missing observations for cases 8 and 13 as possibly “missing at random,” it may be harder to draw

this conclusion regarding case 18, since for that case, two points are missing. Why are they missing? Did

the participant misunderstand the task? Was the participant or object given the opportunity to respond?

These are the types of questions you should ask before contemplating and carrying out a missing data

routine in SPSS. Hence, before we survey methods for replacing missing data then, you should heed the

following principle:

Let us survey a couple approaches to replacing

missing data. We will demonstrate these proce-

dures for our quant variable. To access the feature:

TRANSFORM → REPLACE MISSING VALUES

We can see that for cases 8, 13, and 18, we have missing

data. SPSS offers many capabilities for replacing missing

data, but if they are to be used at all, they should be used

with extreme caution.

Never, ever, replace missing data as

anordinary and usual process of data

analysis. Ask yourself first WHY the data

point might be missing and whether it is missing

“at random” or was due to some systematic error or

omission in your experiment. If it was due to some

systematic pattern or the participant misunder-

stood the instructions or was not given full oppor-

tunity to respond, that is a quite different scenario

than if the observation is missing at random due to

chance factors. If missing at random, replacing

missing data is, generally speaking, more appro-

priate than if there is a systematic pattern to the

missing data. Get curious about your missing data

instead of simply seeking to replace it.

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