第四单元学习小实验（第6周）

要完成本教程，您将需要名为“Lab practice 3.sav”的 SPSS 文件，，今天小编就来聊一聊关于第四单元学习小实验?接下来我们就一起去研究一下吧!

第四单元学习小实验

要完成本教程，您将需要名为“Lab practice 3.sav”的 SPSS 文件，

数据编辑器（SPSS 主窗口）

单击桌面图标（或程序菜单中的 SPSS；您可以在 IBM SPSS 下找到它）打开 SPSS。 SPSS 会询问您是否要打开现有数据集，或者您可以选择“输入数据”。现在，单击取消并转到 FileOpenData...并在您从 Blackboard 下载的文件夹中找到名为“Lab practice 3.sav”的文件。

1. 从窗口运行 T 检验分析

要在 SPSS 中运行单样本 t 检验，请单击分析 > 比较均值 > 单样本 T 检验。单样本 T 检验窗口打开，您将在其中指定要在分析中使用的变量。数据集中的所有变量都出现在左侧的列表中。通过在列表中选择变量并单击箭头按钮，将变量移动到测试变量区域。

A 测试变量：将其均值与假设总体均值（即测试值）进行比较的变量。您可以通过选择多个测试变量来同时运行多个单样本 t 检验。每个变量将与相同的测试值进行比较（输入 66.5）

B 检验值：将与您的检验变量进行比较的假设总体平均值。

C 选项：单击选项将打开一个窗口，您可以在其中指定置信区间百分比以及分析如何解决缺失值（即，通过分析排除案例分析或按列表排除案例）。完成规格说明后，单击“继续”。

单击确定运行单样本 t 检验。

输出中出现两个部分（框）：单样本统计和单样本测试。第一部分，单样本统计，提供有关所选变量高度的基本信息，包括有效（非缺失）样本大小 (n)、均值、标准偏差和标准误差。在此示例中，样本的平均高度为 68.03 英寸，这是基于 408 个非缺失观测值。

第二部分，单样本检验，显示与单样本 t 检验最相关的结果。

A 测试值：我们在单样本 T 测试窗口中作为测试值输入的数字。

B t 统计量：单样本 t 检验的检验统计量，记为 t。在本例中，t = 5.810。请注意，t 的计算方法是将平均差 (E) 除以标准误差均值（来自单样本统计框）。

C df：测试的自由度。对于单样本 t 检验，df = n - 1；所以在这里，df = 408 - 1 = 407。

D 信号。 (2-tailed)：对应于检验统计量的双尾 p 值。

E 均值差：“观察到的”样本均值（来自“一个样本统计”框）与“预期”均值（指定的测试值 (A)）之间的差值。平均差的符号对应于 t 值 (B) 的符号。此示例中的正 t 值表示样本的平均高度大于假设值 (66.5)。

F 差异的置信区间：指定检验值与样本均值之间差异的置信区间。

2. 从窗口运行 Anova (f-test) 分析

要在 SPSS 中运行单向方差分析，请单击分析 > 比较均值 > 单向方差分析。

One-Way ANOVA 窗口打开，您将在其中指定要在分析中使用的变量。数据集中的所有变量都出现在左侧的列表中。通过在列表中选择变量并单击蓝色箭头按钮将变量向右移动。您可以将变量移动到以下两个区域之一：从属列表或因子。

A因变量列表：因变量。这是将在样本（组）之间比较其均值的变量。您可以通过选择多个因变量同时运行多个均值比较。

B 因子：自变量。自变量的类别（或组）将定义将比较哪些样本。自变量必须至少有两个类别（组），但在单向方差分析中使用时通常有三个或更多组。

C 对比：（可选）指定在整体 ANOVA 测试之后进行的对比或计划比较。

当初始 F 检验表明组均值之间存在显着差异时，当您有要检验的特定假设时，对比可用于确定哪些特定均值显着不同。在分析数据之前决定对比（即先验）。对比将差异分解为组成部分。它们可能涉及使用权重、非正交比较、标准对比和多项式对比（趋势分析）。

许多在线和印刷资源详细说明了这些选项之间的区别，并将帮助用户选择适当的对比。有关对比的更多信息，您可以通过单击单向方差分析对话框窗口底部的“帮助”按钮从 SPSS 中打开 IBM SPSS 帮助手册。

D Post Hoc：（可选）请求 post hoc（也称为多重比较）测试。可以通过选中相关框来选择特定的事后测试。

1 假设方差相等：假设方差同质的多重比较选项（每组方差相等）。有关特定比较方法的详细信息，请单击此窗口中的帮助按钮。

2 检验：默认情况下，选择 2 边假设检验。或者，如果您选择使用 Dunnett 事后检验，则可以指定方向性、单边假设检验。单击 Dunnett 旁边的框，然后在数字上指定控制类别是分组变量的最后一组还是第一组。在测试区域中，单击 < 控制或 > 控制。单尾选项要求您指定是否预测指定控制组的均值将小于 (> Control) 或大于 (< Control) 另一个组。

3 不假设方差相等：不假设方差相等的多重比较选项。有关特定比较方法的详细信息，请单击此窗口中的帮助按钮。

4 显着性水平：统计显着性所需的临界值。默认情况下，重要性设置为 0.05。

当初始 F 检验表明组均值之间存在显着差异时，事后检验可用于在您没有要检验的特定假设时确定哪些特定均值显着不同。事后检验比较每对均值（如 t 检验），但与 t 检验不同，它们校正显着性估计以解释多重比较。

E 选项：单击选项将产生一个窗口，您可以在其中指定要包含在输出中的统计量（描述性、固定和随机效应、方差齐性检验、Brown-Forsythe、Welch）、是否包含均值图以及如何分析将解决缺失值（即，通过分析排除案例分析或按列表排除案例）。完成规格说明后，单击“继续”。

单击确定运行单向方差分析。

现在，通过“实验室练习 3.sav”，让我们尝试对 T 检验和 ANOVA 进行一些测试，如下所示：

1. 单样本 t 检验

2. 配对 t 检验

3. 单向方差分析

4. 双向方差分析

To complete this tutorial, you will need SPSS files named “Lab practice 3.sav,”

Data Editor (main SPSS window)

Open SPSS by clicking on the desktop icon (or on SPSS in the program menu; you may find it under IBM SPSS). SPSS will ask you if you would like to open an existing dataset or you could select “Type in data.” For now, click Cancel and go to FileàOpenàData…and find the file called “Lab practice 3.sav,” in the folder where you downloaded it to from Blackboard.

1. Running T-test Analyses from a Window

To run a One Sample t Test in SPSS, click Analyze > Compare Means > One-Sample T Test.The One-Sample T Test window opens where you will specify the variables to be used in the analysis. All of the variables in your dataset appear in the list on the left side. Move variables to the Test Variable(s) area by selecting them in the list and clicking the arrow button.

A Test Variable(s): The variable whose mean will be compared to the hypothesized population mean (i.e., Test Value). You may run multiple One Sample t Tests simultaneously by selecting more than one test variable. Each variable will be compared to the same Test Value (enter 66.5)

B Test Value: The hypothesized population mean against which your test variable(s) will be compared.

C Options: Clicking Options will open a window where you can specify the Confidence Interval Percentage and how the analysis will address Missing Values (i.e., Exclude cases analysis by analysis or Exclude cases listwise). Click Continue when you are finished making specifications.

Click OK to run the One Sample t Test.

Two sections (boxes) appear in the output: One-Sample Statistics and One-Sample Test. The first section, One-Sample Statistics, provides basic information about the selected variable, Height, including the valid (nonmissing) sample size (n), mean, standard deviation, and standard error. In this example, the mean height of the sample is 68.03 inches, which is based on 408 nonmissing observations.

The second section, One-Sample Test, displays the results most relevant to the One Sample t Test.

A Test Value: The number we entered as the test value in the One-Sample T Test window.

B t Statistic: The test statistic of the one-sample t test, denoted t. In this example, t = 5.810. Note that t is calculated by dividing the mean difference (E) by the standard error mean (from the One-Sample Statistics box).

C df: The degrees of freedom for the test. For a one-sample t test, df = n - 1; so here, df = 408 - 1 = 407.

D Sig. (2-tailed): The two-tailed p-value corresponding to the test statistic.

E Mean Difference: The difference between the "observed" sample mean (from the One Sample Statistics box) and the "expected" mean (the specified test value (A)). The sign of the mean difference corresponds to the sign of the t value (B). The positive t value in this example indicates that the mean height of the sample is greater than the hypothesized value (66.5).

F Confidence Interval for the Difference: The confidence interval for the difference between the specified test value and the sample mean.

2. Running Anova (f-test) Analyses from a Window

To run a One-Way ANOVA in SPSS, click Analyze > Compare Means > One-Way ANOVA.

The One-Way ANOVA window opens, where you will specify the variables to be used in the analysis. All of the variables in your dataset appear in the list on the left side. Move variables to the right by selecting them in the list and clicking the blue arrow buttons. You can move a variable(s) to either of two areas: Dependent List or Factor.

A Dependent List: The dependent variable(s). This is the variable whose means will be compared between the samples (groups). You may run multiple means comparisons simultaneously by selecting more than one dependent variable.

B Factor: The independent variable. The categories (or groups) of the independent variable will define which samples will be compared. The independent variable must have at least two categories (groups), but usually has three or more groups when used in a One-Way ANOVA.

C Contrasts: (Optional) Specify contrasts, or planned comparisons, to be conducted after the overall ANOVA test.

When the initial F test indicates that significant differences exist between group means, contrasts are useful for determining which specific means are significantly different when you have specific hypotheses that you wish to test. Contrasts are decided before analyzing the data (i.e., a priori). Contrasts break down the variance into component parts. They may involve using weights, non-orthogonal comparisons, standard contrasts, and polynomial contrasts (trend analysis).

Many online and print resources detail the distinctions among these options and will help users select appropriate contrasts. For more information about contrasts, you can open the IBM SPSS help manual from within SPSS by clicking the "Help" button at the bottom of the One-Way ANOVA dialog window.

D Post Hoc: (Optional) Request post hoc (also known as multiple comparisons) tests. Specific post hoc tests can be selected by checking the associated boxes.

1 Equal Variances Assumed: Multiple comparisons options that assume homogeneity of variance (each group has equal variance). For detailed information about the specific comparison methods, click the Help button in this window.

2 Test: By default, a 2-sided hypothesis test is selected. Alternatively, a directional, one-sided hypothesis test can be specified if you choose to use a Dunnett post hoc test. Click the box next to Dunnett and then specify whether the Control Category is the Last or First group, numerically, of your grouping variable. In the Test area, click either < Control or > Control. The one-tailed options require that you specify whether you predict that the mean for the specified control group will be less than (> Control) or greater than (< Control) another group.

3 Equal Variances Not Assumed: Multiple comparisons options that do not assume equal variances. For detailed information about the specific comparison methods, click the Help button in this window.

4 Significance level: The desired cutoff for statistical significance. By default, significance is set to 0.05.

When the initial F test indicates that significant differences exist between group means, post hoc tests are useful for determining which specific means are significantly different when you do not have specific hypotheses that you wish to test. Post hoc tests compare each pair of means (like t-tests), but unlike t-tests, they correct the significance estimate to account for the multiple comparisons.

E Options: Clicking Options will produce a window where you can specify which Statistics to include in the output (Descriptive, Fixed and random effects, Homogeneity of variance test, Brown-Forsythe, Welch), whether to include a Means plot, and how the analysis will address Missing Values (i.e., Exclude cases analysis by analysis or Exclude cases listwise). Click Continue when you are finished making specifications.

Click OK to run the One-Way ANOVA.

Now, by using “Lab practice 3.sav”, let’s try to do a few tests for T-test and ANOVA as follow:

1. One-sample t-test

2. Paired t-test

3. One-way ANOVA

4. Two-way ANOVA