Selected SPSS Output for One-Way Repeated Measures ANOVA

 Selected SPSS Output for One-Way Repeated Measures ANOVA

The one-way analysis of variance (ANOVA) is used to determine whether there are any significant differences between the means of three or more independent (unrelated) groups. This guide will provide a brief introduction to the one-way ANOVA, including the assumptions of the test and when you should use it. We will then show you how to run a one-way ANOVA in SPSS using an appropriate example, which options to choose and how to interpret the output. Should you wish to learn more about the one-way ANOVA before running the procedure in SPSS, please click here.
What does this test do?
The one-way ANOVA compares the means between the groups you are interested in and determines whether any of those means are statistically significantly different from each other. Specifically, it tests the null hypothesis:

where µ = group population mean and k = number of groups. The alternative hypothesis (HA) is that there are at least two group means that are significantly different from each other. Briefly stated, if the result of a one-way ANOVA is statistically significant, we accept the alternative hypothesis; otherwise, we reject the alternative hypothesis.
At this point, it is important to realise that the one-way ANOVA is an omnibus test statistic and it cannot tell you which specific groups were significantly different from each other (just that at least two groups were different). To determine which specific groups differed from each other you need to use a post-hoc test. Post-hoc tests are described later in this guide (here).
What is required
Your independent variable should be dichotomous.
Your dependent variable has either an interval or ratio (continuous) scale (see our guide on Types of Variable).
Assumptions
Your dependent variable is approximately normally distributed for each category of the independent variable (technically the residuals need to be normally distributed).
There is equality of variances between the independent groups (homogeneity of variances).
You have independence of cases.
You will need to run statistical tests in SPSS to check all of these assumptions before carrying out a one-way ANOVA. If you do not run these tests of assumptions, the results you get when running a one-way ANOVA might not be valid. If you are unsure how to do this correctly, we show you how, step-by-step in our enhanced one-way ANOVA in SPSS guide. To learn more about our enhanced guides, Take the Tour or go straight to Plans & Pricing (complete access to all our guides starts from just $3.99/£2.99/€3.99).
Example
A manager wants to raise the productivity at his company by increasing the speed at which his employees can use a particular spreadsheet program. As he does not have the skills in-house, he employs an external agency which provides training in this spreadsheet program. They offer 3 packages: a beginner, intermediate and advanced course. He is unsure which course is needed for the type of work they do at his company, so he sends 10 employees on the beginner course, 10 on the intermediate course and 10 on the advanced course. When they all return from the training he gives them a problem to solve using the spreadsheet program and times how long it takes them to complete the problem. He wishes to then compare the three courses (beginner, intermediate, advanced) to see if there are any differences in the average time it took to complete the problem.

 

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Discriminant Analysis with SPSS

Discriminant Analysis with SPSS

Rather than working with pre-existing classifications of subjects, as the other tests in 
Chapter 9 do, a discriminant analysis attempts to create classifications. To conduct a 
discriminant analysis in SPSS, therefore, you cannot use the "General Linear Model" 
function. The following process allows you to use continuous values to predict subjects' 
group placements.
1. Choose the "Classify" option in SPSS Analyze pull-down menu. 
2. Identify your desired type of classification as "Discriminant." Choose "Discriminant" 
from the prompts given. A window entitled a window entitled Discriminant Analysis
should appear. 
FIGURE 9.9 –SPSS DISCRIMINANT ANALYSIS WINDOW
The user identifies the variables involved in a one-way discriminant analysis by selecting their names from 
those listed on the left side of the Discriminant Analysis window. SPSS performs the test using variables with 
names placed into the "Independents" and variables with names placed into the "Grouping Variables" box.The user identifies the variables involved in a one-way discriminant analysis by selecting their names from 
those listed on the left side of the Discriminant Analysis window. SPSS performs the test using variables with 
names placed into the "Independents" and variables with names placed into the "Grouping Variables" box.
3. In this window, you can define the variables involved in the analysis as follows
a. Move the name of the categorical dependent variable from the box on the left to the 
"Grouping Variable" box. You must also click on the "Define Range" button below 
this box and type the values for the lowest and highest dummy-variable values used 
to identify groups. 
b. Identify the continuous measure(s) used to predict subjects' categories by moving 
the names of the predictor(s) to the "Independents" box. 
4. Click OK.
The Discriminant Analysis' "Independents Variable" box allows you to identify more than 
one predictor of subjects' categories. Inputting more than one independent variable leads 
to a multiple discriminant analysis. The analysis presented in Chapter 9's examples, though, 
use a single independent variable.

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Excel's date string parsing to fix dates & time for SPSS

The conversion functions convert time intervals from one unit of time to another. Time intervals are stored as the number of seconds in the interval; the conversion functions provide a means for calculating more appropriate units, for example, converting seconds to days.

Each conversion function consists of the CTIME function followed by a period (.), the target time unit, and an argument. The argument can consist of expressions, variable names, or constants. The argument must already be a time interval. 请参阅 主题 Aggregation functions 详细信息。 Time conversions produce noninteger results with a default format of F8.2.

Since time and dates are stored internally as seconds, a function that converts to seconds is not necessary.

CTIME.DAYS. CTIME.DAYS(timevalue)。数值。返回 timevalue 中的天数（包括小数天数），timevalue 可以是秒数、时间表达式或时间格式的变量。
CTIME.HOURS. CTIME.HOURS(timevalue)。数值。返回 timevalue 中的小时数（包括小数小时数），timevalue 可以是秒数、时间表达式或时间格式的变量。
CTIME.MINUTES. CTIME.MINUTES(timevalue)。数值。返回 timevalue 中的分钟数（包括分数分钟数），timevalue 可以是秒数、时间表达式或时间格式的变量。
CTIME.SECONDS. CTIME.SECONDS(timevalue)。数值。返回 timevalue 中的秒数（包括小数秒数），timevalue 可以是数字、时间表达式或时间格式的变量。
Example

DATA LIST FREE (",") 
/StartDate (ADATE12) EndDate (ADATE12)
StartDateTime(DATETIME20) EndDateTime(DATETIME20)
StartTime (TIME10) EndTime (TIME10).
BEGIN DATA
3/01/2003, 4/10/2003
01-MAR-2003 12:00, 02-MAR-2003 12:00
09:30, 10:15
END DATA.
COMPUTE days = CTIME.DAYS(EndDate-StartDate).
COMPUTE hours = CTIME.HOURS(EndDateTime-StartDateTime).
COMPUTE minutes = CTIME.MINUTES(EndTime-StartTime).
• CTIME.DAYS calculates the difference between EndDate and StartDate in days—in this example, 40 days.

• CTIME.HOURS calculates the difference between EndDateTime and StartDateTime in hours—in this example, 24 hours.

• CTIME.MINUTES calculates the difference between EndTime and StartTime in minutes—in this example, 45 minutes.

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Selected SPSS Output for Oneway MANOVA

Selected SPSS Output for Oneway MANOVA

show sample MANOVA output based upon imaginary data for 
the scenario described in Example 9.4. SPSS output for the MANOVA contains other tables 
as well. However, these three tables provide the information needed to address the 
omnibus hypothesis and the role of the dependent variables in determining whether 
canonical variate means differ significantly.
Category means and standard deviations for the canonical variate appear in Table 9.12, entitled Descriptive 
Statistics. Values in the Multivariate Tests table (Table 9.13) indicate whether these means differ 
significantly. In this table, the row labeled "Wilks' Lambda" contains the values pertaining to the MANOVA 
procedure described in Chapter 9. To further understand the p value included in this table, the researcher 
might find values in Table 9.14, Test of Between-Subjects Effects, useful. This table provides p values for 
oneway ANOVAs comparing category means for each of the dependent variables that compose the canonical 
variate.
Values in lower portion of the Multivariate Tests table, labeled "genre," indicate whether 
canonical variate means differ significantly for those who experienced the story by reading 
it, watching it as a film, and watching it as a Broadway musical. In this table, SPSS presents 
the results from four possible techniques of obtaining F for the MANOVA. For an analysis 
using Section 9.3.2's method involving Λ, values in the Wilks' Lambda row of the table 
should be examined. The F of 6.743 and the p of .000) indicate a significant difference 
between the mean canonical variate values for each genre.
The presence of a significant difference in canonical variate means, however, does not 
imply significant differences in the means for each dependent variable. The results of 
ANOVAs that compare the mean setting, characters, and plot scores for each category 
appear in Table 9.14. According to the values in the "genre" row of this table and based 
upon the standard α of .05, subjects in the three independent-variable categories do not 
have significantly different recall of characters (F=1.715, p=.182). They do, however, have 
significantly different recall of the story's setting (F=14.932, p=.000) and plot (F=7.355, 
p=.001). The differences in these dependent variable scores, provide a mathematical 
explanation for the differences in canonical variate scores.

 

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