我们前面介绍的一般线性模型、Logistic回归模型、对数线性模型、Poisson回归模型等，实际上均属于广义线性模型的范畴，广义 线性模型包含的范围非常广泛，原因在于其对于因变量、因变量的概率分布等条件的限制放宽，使其应用范围加大。
广义线性模型由以下几个部分组成
1.因变量广义线性模型的因变量还是要去独立性，但是分布不再局限于正态分布一种，而是可以是指数族概率分布的任意一种，其方差也可
以不稳定，但必须要能表达为依赖均值的函数
2.线性部分广义线性模型因变量与自变量必须为线性关系，即因变量与自变量之间是一次方函数关系，这点和传统线性模型也一样
3.连接函数用于描述因变量的期望值是如何和预测值相关联的
由上可知，和传统线性模型相比，广义线性模型主要从以下两个方面进行了扩展1.因变量的分布范围扩大2.连接函数的引入
通过选定不同的因变量概率分布、连接函数等，就可以拟合各种不同的广义线性模型，例如当因变量分布为正态分布、连接函数为
恒等函数时，就是拟合一般线性模型；当因变量分布为二项分布，连接函数为Logit函数时，就是拟合Logistic回归，当因变量分布
为Poisson分布，连接函数为对数时，就是拟合Poisson回归，下面我们通过一个例子来进行说明广义线性模型在SPSS中的使用情况
例，希望研究不同温度不同催化剂不同批次条件下，某化合物的转化率情况，数据如下根据本例的实验目的，可以采用方差分析，但是本例为嵌套实验设计，共有三个因素，温度、催化剂、批次，其中温度是嵌套在催
化剂因素下面的，因此SPSS无法直接使用方差分析的对话框来进行分析，需要在程序中进行修改，比较麻烦，但是如果使用广义线
性模型，就可以直接使用对话框进行分析了
分析&广义线性模型&广义线性模型
阅读(...) 评论()“一起学SPSS”的联盟订阅号“医学统计精粹”曾介绍过用比率分析进行。今天给大家介绍如何用一个非常牛X的方法来计算率及95%CI。我们还是用之前的一个例子（详见：）来介绍其操作方法。〖例〗某医院对门诊产前检查并住院分娩的孕36~41周无其他高危因素的孕妇为研究对象中，出现规则变化脐动脉血流频谱曲线的孕晚期胎儿为观察组，出现正常脐动脉血流频谱的孕晚期胎儿为对照组。两组胎儿的分娩方式情况情况如下表，试计算两组胎儿分的剖宫产率及其95%CI。【Step 01】 对数据进行加权，方法详见：。【Step 02】拆分数据文件，选择【数据】→【拆分文件】子菜单。【Step 03】选择【分析】→【广义线性模型】→【广义线性模型】子菜单。按红色方框设置相关选项，其他均为默认设置。由于剖宫产的编码为2，顺产为1，因此【参考类别】选择【第一个值（最低值）】【结果解释】观察组的剖宫产率为50.0%，总体剖宫产率的95%CI为（34.5%，65.5%），对照组剖宫产率为29.1%，总体剖宫产率的95%CI为（21.4%，36.8%）。注：该方法计算结果与比率分析的结果是一致的，当率接近100%时，同样会存在其95%CI上限超出100%的情况。【参考文章】?您的支持是我们写作的动力，各位小伙伴记得分享本文，以帮助更多的朋友哦。【“一起学SPSS”倾情整理统计学习资源包】内含配套免费电子书及数据文件；各种统计及数据管理免费软件；各种统计学习素材。（不定期更新）注：资源包提供部分免费工具和试用版软件下载，但不提供盗版软件。见谅！【资源包下载方法】回复关键词“SPSS”即可获取下载地址。回复“文章”或“art”可获取订阅号文章目录。投稿邮箱：凡与医学科研、统计相关的文章，都可以给小编发来！文章会默认给作者署名，如有需作者简介等要求，请在投稿邮件中注明。一起学SPSS(lizh_SPSS)　
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采用SPSS进行Two-way ANOVA统计分析
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Testing of Assumptions
To determine whether your dependent variable is normally
distributed for each combination of the levels of the two
independent variables see our
guide that runs through how to test for
normality using SPSS using a specific two-way ANOVA example. In
SPSS, homogeneity of variances is tested using Levene's Test for
Equality of Variances. This is included in the main procedure for
running the two-way ANOVA, so we get to evaluate whether there is
homogeneity of variances at the same time as we get the results
from the two-way ANOVA.
Test Procedure in SPSS
Click Analyze & General
Linear Model & Univariate... on the
top menu as shown below:
<img alt="Two-way ANOVA Menu" src="/spss-tutorials/img/two-way-anova-3.png" title="Two-way&ANOVA&using&SPSS（转载）" border="0" height="445" width="405">
Published with written permission from SPSS Inc, an IBM
You will be presented with the "Univariate" dialogue box:
<img alt="Two-way ANOVA Dialogue Box" src="/spss-tutorials/img/two-way-anova-4.png" title="Two-way&ANOVA&using&SPSS（转载）" border="0" height="392" width="456">
Published with written permission from SPSS Inc, an IBM
You need to transfer the dependent variable
"Int_Politics" into the "Dependent
Variable:" box and transfer both independent variables,
"Gender" and "Edu_Level", into
the "Fixed Factor(s):" box. You can do this by
drag-and-dropping the variables into the respective boxes or by
button. If you are using older versions of SPSS you
will need to use the former method. The result is shown below:
[For this analysis you will not need to worry about the
"Random Factor(s):",
"Covariate(s):" or "WLS Weight:"
<img alt="Two-way ANOVA Dialogue Box" src="/spss-tutorials/img/two-way-anova-5.png" title="Two-way&ANOVA&using&SPSS（转载）" border="0" height="392" width="456">
Published with written permission from SPSS Inc, an IBM
Click on the
button. You will be presented with the "Univariate:
Profile Plots" dialogue box:
<img alt="Two-way ANOVA Plots Dialogue Box" src="/spss-tutorials/img/two-way-anova-6.png" title="Two-way&ANOVA&using&SPSS（转载）" border="0" height="323" width="380">
Published with written permission from SPSS Inc, an IBM
Transfer the independent variable "Edu_Level"
from the "Factors:" box into the
"Horizontal Axis:" box and transfer the
"Gender" variable into the "Separate
Lines:" box. You will be presented with the following screen:
[Tip: Put the independent variable with the greater number of
levels in the "Horizontal Axis:" box.]
<img alt="Two-way ANOVA Plots Dialogue Box" src="/spss-tutorials/img/two-way-anova-7.png" title="Two-way&ANOVA&using&SPSS（转载）" border="0" height="323" width="380"><img alt="Two-way ANOVA Plots Dialogue Box" src="/spss-tutorials/img/two-way-anova-8.png" title="Two-way&ANOVA&using&SPSS（转载）" border="0" height="323" width="380">
Published with written permission from SPSS Inc, an IBM
You will see that "Edu_Level*Gender" has been
added to the "Plots:" box.
button. This will return you to the "Univariate"
dialogue box.Click the
button. You will be presented with the "Univariate:
Post Hoc Multiple Comparisons for Observed..." dialogue box as
shown below:
<img alt="Two-way ANOVA Post-hoc Dialogue Box" src="/spss-tutorials/img/two-way-anova-9.png" title="Two-way&ANOVA&using&SPSS（转载）" border="0" height="431" width="429">
Published with written permission from SPSS Inc, an IBM
Transfer "Edu_Level" from the
"Factor(s):" box to the "Post Hoc Tests
for:" box. This will make the "Equal Variances Assumed"
section become active (loose the "grey sheen") and present you with
some choices for which post-hoc test to use. For this example, we
are going to select "Tukey", which is a good, all-round
post-hoc test.
[You only need to transfer independent variables that have more
than two levels into the "Post Hoc Tests for:" box.
This is why we do not transfer "Gender".]
You will finish up with the following screen:
<img alt="Two-way ANOVA Post-hoc Dialogue Box" src="/spss-tutorials/img/two-way-anova-10.png" title="Two-way&ANOVA&using&SPSS（转载）" border="0" height="431" width="429">
Published with written permission from SPSS Inc, an IBM
button to return to the "Univariate" dialogue
box.Click the
button. This will present you with the "Univariate:
Options" dialogue box as shown below:
<img alt="Two-way ANOVA Options Dialogue Box" src="/spss-tutorials/img/two-way-anova-11.png" title="Two-way&ANOVA&using&SPSS（转载）" border="0" height="471" width="421">
Published with written permission from SPSS Inc, an IBM
Transfer "Gender",
"Edu_Level" and
"Gender*Edu_Level" from the "Factor(s)
and "Factor Interactions:" box into the "Display
Means for:" box. In the "Display" section, tick the
"Descriptive Statistics" and "Homogeneity tests"
options. You will presented with the following screen:
<img alt="Two-way ANOVA Options Dialogue Box" src="/spss-tutorials/img/two-way-anova-12.png" title="Two-way&ANOVA&using&SPSS（转载）" border="0" height="471" width="421">
Published with written permission from SPSS Inc, an IBM
button to return to the "Univariate" dialogue
button to generate the output.SPSS Output of Two-way ANOVA
SPSS produces many tables in its output from a two-way ANOVA and
we are going to start with the "Descriptives" table as shown
<img alt="Output of two-way ANOVA in SPSS" src="/spss-tutorials/img/two-way-anova-output-1.png" title="Two-way&ANOVA&using&SPSS（转载）" border="0" height="322" width="369">
Published with written permission from SPSS Inc, an IBM
This table is very useful as it provides the mean and standard
deviation for the groups that have been split by both independent
variables. In addition, the table also provides "Total" rows, which
allows means and standard deviations for groups only split by one
independent variable or none at all to be known.
Levene's Test of Equality of Error Variances
The next table to look at is Levene's Test of Equality of Error
Variances as shown below:
<img alt="Levene's Equality of Variances in two-way ANOVA in SPSS" src="/spss-tutorials/img/two-way-anova-output-2.png" title="Two-way&ANOVA&using&SPSS（转载）" border="0" height="172" width="274">
Published with written permission from SPSS Inc, an IBM
From this table we can see that we have homogeneity of variances
of the dependent variable across groups. We know this as the
Sig. value is greater than 0.05, which is the
level we set for alpha. If the Sig. value had been
less than 0.05 then we would have concluded that the variance
across groups was significantly different (unequal).
Tests of Between-Subjects Effects Table
This table shows the actual results of the two-way ANOVA as
shown below:
<img alt="Tests of Between-Subjects Effects Table in two-way ANOVA in SPSS" src="/spss-tutorials/img/two-way-anova-output-3.png" title="Two-way&ANOVA&using&SPSS（转载）" border="0" height="264" width="537">
Published with written permission from SPSS Inc, an IBM
We are interested in the Gender, Edu_Level and Gender*Edu_Level
rows of the table as highlighted above. These rows inform us of
whether we have significant mean differences between our groups for
our two independent variables, Gender and Edu_Level, and for their
interaction, Gender*Edu_Level. We must first look at the
Gender*Edu_Level interaction as this is the most important result
we are after. We can see from the Sig. column that
we have a statistically significant interaction at the P =
.014 level. You may wish to report the results of Gender and
Edu_Level as well. We can see from the above table that there was
no significant difference in interest in politics between Gender
(P = .207) but there were significant differences between
educational levels (P & .0005).
Multiple Comparisons Table
This table shows the Tukey post-hoc test results for the
different levels of education as shown below:
<img alt="Tests of Between-Subjects Effects Table in two-way ANOVA in SPSS" src="/spss-tutorials/img/two-way-anova-output-4.png" title="Two-way&ANOVA&using&SPSS（转载）" border="0" height="298" width="610">
Published with written permission from SPSS Inc, an IBM
We can see form the above table that there is some repetition of
the results but, regardless of which row we choose to read from, we
are interested in the differences between (1) School and College,
(2) School and University, and (3) College and University. From the
results we can see that there is a significant difference between
all three different combinations of educational level (P
Plot of the Results
The following plot is not of sufficient quality to present in
your reports but provides a good graphical illustration of your
results. In addition, we can get an idea of whether there is an
interaction effect by inspecting whether the lines are parallel or
<img alt="Plot of the Results in two-way ANOVA in SPSS" src="/spss-tutorials/img/two-way-anova-output-5.png" title="Two-way&ANOVA&using&SPSS（转载）" border="0" height="476" width="517">
Published with written permission from SPSS Inc, an IBM
From this plot we can see how our results from the previous
table might make sense. Remember that if the lines are not parallel
then there is the possibility of an interaction taking place.
Procedure for Simple Main Effects in SPSS
You can follow up the results of a significant interaction
effect by running tests for simple main effects - that is, the mean
difference in interest in politics between genders at each
education level. SPSS does not allow you to do this using the
graphical interface you will be familiar with, but requires you to
use syntax. We explain how to do this below:
Click File & New
& Syntax from the main menu as shown
<img alt="Syntax Editor for Two-way ANOVA" src="/spss-tutorials/img/two-way-anova-syntax-1.png" title="Two-way&ANOVA&using&SPSS（转载）" border="0" height="405" width="413">
Published with written permission from SPSS Inc, an IBM
You will be presented with the Syntax Editor as shown below:
（在SPSS 16中界面不是这样的）
<img alt="Syntax Editor for Two-way ANOVA" src="/spss-tutorials/img/two-way-anova-syntax-2.png" title="Two-way&ANOVA&using&SPSS（转载）" border="0" height="289" width="569">
Published with written permission from SPSS Inc, an IBM
Type text into the syntax editor so that you end up with the
following (the colours are automatically added):
[Depending on the version of SPSS you are using you might have
suggestion boxes appear when you type in SPSS-recognised commands,
such as, UNIANOVA. If you are familiar with using this type of
auto-prediction then please feel free to do so, but otherwise
simply ignore the pop-up suggestions and keep typing
normally.]（在SPSS
16中，你只管如下图右框显示输入命令，然后点击上面菜单中的Run－all，结果就输出了<img src="/uc/myshow/blog/misc/gif/E___7474ZH00SIGG.gif" type="face" alt="Two-way&ANOVA&using&SPSS（转载）" title="Two-way&ANOVA&using&SPSS（转载）">）
<img alt="Syntax Editor for Two-way ANOVA" src="/spss-tutorials/img/two-way-anova-syntax-7.png" title="Two-way&ANOVA&using&SPSS（转载）" border="0" height="202" width="575">
Published with written permission from SPSS Inc, an IBM
Basically, all text you see above that is in CAPITALS, is
required by SPSS and does not change when you enter your own data.
Non-capitalised text represents your variables and will change when
you use your own data. Breaking it all down, we have:
Tells SPSS to use the Univariate Anova command
Int_Politics BY Gender
Your dependent variable BY your
two independent variables (with a space between them)
Tells SPSS to calculate estimated marginal means
TABLES(Gender*Edu_Level)
Generate statistics for the interaction term. Put your two
independent variables here, separated by a * to denote an
interaction
COMPARE(Gender)
Tells SPSS to compare the interaction term between genders
Making sure that the cursor is at the end of row 2 in the
syntax editor click the
button, which will run the syntax you have typed.
Your results should appear in the Output Viewer below the results
you have already generated.
SPSS Output of Simple Main Effects
The table you are interested in is the Univariate
Tests table:
<img alt="Simple Main Effects for Two-way ANOVA" src="/spss-tutorials/img/two-way-anova-syntax-6.png" title="Two-way&ANOVA&using&SPSS（转载）" border="0" height="243" width="531">
Published with written permission from SPSS Inc, an IBM
This table shows us whether there are statistical differences in
mean political interest between gender for each educational level.
We can see that there are no statistically significant mean
differences between male and females' interest in politics when
individuals are educated to school (P = .465) or college
level (P = .793). However, when individuals are educated
to University level, there are significant differences between
males and females' interest in politics (P = .002).
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