use spss One-Way Analysis of Covariance

use spss One-Way Analysis of Covariance

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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Run a one-way repeated-measures ANOVA in SPSS

Run a one-way repeated-measures ANOVA in SPSS

Overview: The instructions on this sheet cover two procedures: 
1. A one-way within-subjects ANOVA, used when you have one independent variable and one group of subjects measured repeatedly under 3 or more conditions. For example, subjects are measured in a baseline condition, are given a treatment, and are followed up at 3 later points in time. 
2. A 2-way mixed ANOVA, used when you have two independent variables with one within-subjects factor, and one between-subjects factor. The within-subjects factor is the repeated measures factor. On the between-subjects factor, subjects are divided into discrete subgroups, and each subject falls into only one of those subgroups.
To run: From the Data Editor Window 
Click on "Analyze" 
Click on "General Linear Model" 
Click on "Repeated Measures"

The following dialog box will appear:

Begin with the repeated measures factor. The first thing you need to do is to instruct SPSS to treat your repeated measurements not as different variables, but as different levels of the same variable. To do this click the box "Within-Subject Factor Name" and type in a name for your repeated measures variable.

Type in the number of observations you have on each subject in the box labeled, "Number of Levels" 
Click on "Add". The name of your new variable will appear in the box with the number of levels of that variable in parentheses.

Click on "Define" 
The following GLM - Repeated Measures dialog box will appear:

You must now tell SPSS what the different levels of your repeated measures factor are, that is, which variables from the column on the left represent your different levels. Click on each of the variables, i.e. baseline, time2, time3, time4 and click the right arrow key to move those variables to the box labeled, "Within-subjects Variables"

If you are running a one-way repeated measures ANOVA, you are done. 
Click "OK"

If you are running a two-way mixed ANOVA you need to indicate which variable is your between subjects variable. Click on that variable from the column on the left and click the right arrow to move it to the box on the right labeled, "Between-Subjects Factor(s):"

Click on "OK"

Output:

One-way repeated measures ANOVA 
The first table you see lists "Descriptive Statistics" for each of your groups, i.e., mean, standard deviation, and sample size. Note the sample size will be identical for all groups because the same subjects appear in each group.

Look at the table, "Tests of within-subjects effects." 
The first line of this table gives you your F, its degrees of freedom, and the probability of your F.

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spss ANCOVA - analysis of covariance

spss ANCOVA - analysis of covariance

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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 We offer worldwide shippment .

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Full version  cheap SPSS statistion 21 spss 21   at   $54 

Testing Hypothesis using Paired Sample t-Test spss tip

Testing Hypothesis using Paired Sample t-Test spss tip

Testing Hypothesis using Paired Sample t-Test spss tip
Paired-Sample T-Test is also known as dependent T-Test, repeated-measures T-test or within-subjects T-test. A Paired-sample t-test is used to analyse paired scores, specifically, we want to see if there is difference between paired scores.

Example Scenario
A new fitness program is devised for obese people. Each participant's weight was measured before and after the program to see if the fitness program is effective in reducing their weights.

In this example, our null hypothesis is that the program is not effective, i.e., there is no difference between the weight measured before and after the program. The alternative hypothesis is that the program is effective and the weight measured after is less than the weight measured before the program. The dataset can be obtained here.

In the data, the first column is the weight measured before the program and the second column is the weight after.

Step 1
Select "Analyze -> Compare Means -> Paired-Samples T Test".

A new window pops out. Drag the variable "Before" and "After" from the list on the left to the pair 1 variable 1 and variable 2 respectively, as shown below. Then click "OK".

Step 2
The results now pop out in the "Output" window.

Step 4
We can now interpret the result.

From A, since the p-value is 0.472, we reject the alternative hypothesis and conclude that the fitness program is not effective at 5% significant level.

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spss Models with multiple mediators

spss Models with multiple mediators

Hey, just wondering if anyone can provide some assistance on this stats question. I am working on doing a moderation analysis and have been searching the internet and books for some instrcutions on how to do this (if you have any recommendation for resources, that would also be wonderful...).

Here is what I have come up with so far from my readings and I an wondering if I am doing it right:

Step 1 - Some sources say I would need to center the variable first, others say it is not necessary. Anyone have any advice on this?

Step 2 - Multiply the variables by the moderator.

Step 3 - Multiple regression with the predictor variable, the moderator, and their product entered. Is this all entered at once or is this a step-wise regression?

If the product of the variables is significant, then there is moderation? Does it matter if the initial variable and the moderator are individually significant or not?

buy cheap SPSS statistion 21 SPSS 21  pc mac

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Full version  cheap SPSS statistion 21 spss 21   at   $54