ENTERING THE DATA in spss

ENTERING THE DATA in spss

This page shows the basics of entering data into the SPSS data editor. The SPSS data editor can be a good choice for entering your data. It has a friendly interface that resembles an Excel spreadsheet and by entering the data directly into SPSS, you don't need to worry about converting the data from some other format into SPSS. For example, you might enter your data in Excel, and then try to convert it to SPSS and find out that you used the latest version of Excel, but your version of SPSS has trouble reading the latest Excel files.

Below is a screen snapshot of what the SPSS data editor looks like when you start SPSS. As you see, it does look like an Excel spreadsheet. In this editor, the columns will represent your variables, and the rows will represent your observations (sometimes called records, subjects or cases).

 

Let's illustrate how to enter data into the SPSS data editor using the data file below.

make mpg weight price
AMC Concord 22 2930 4099
AMC Pacer 17 3350 4749
AMC Spirit 22 2640 3799
Buick Century 20 3250 4816
Buick Electra 15 4080 7827
First, you need to tell SPSS the names of your variables. You can double click on the column heading (shown with the arrow below for the first column). That permits you to enter information about the variable for that column.

 

Below you see the dialog box that appears that allows you to enter information about your variable. For the first variable, let's change the Variable Name to be make (see arrow) and click on Type so we can tell SPSS that this is a string variable.

You can tell SPSS that make is a string by clicking String at the left (see left arrow) and then we should indicate that the length is up to 13 characters (see arrow at right). You can then click Continue. When you return to the Define Variable dialog, click OK.

Click the column heading for column 2 and supply the name mpg and click ok. Click the column heading for column 3 and indicate the variable name is weight, and likewise for column 4 indicate that the variable name is price. Once you have done this, the Data Editor will look like below.

Once you have created the column headings (variable names) you are ready to enter the data. It is usually best to enter the data one observation at a time going from left to right. After you type in a entry for a variable, you can press the Tab key to move to the next variable on the right. Once you reach the last column (race) then use the arrow keys to move to the first column of the next observation. Once you have entered the sample data file, the SPSS Data Editor would look like this.

You can save your data file by clicking File then Save. It would be wise to save your data about every 10-15 minutes. Imagine spending three hours typing in data, and then the power goes out, your computer stops responding, and then you have to enter the data all over again. The data are not saved when you type them in; rather, the file is saved when you choose File then Save.

Regression in GLM is simply a matter of entering the independent variables as covariates and, if there are sets of dummy variables (ex., Region, which would be translated into dummy variables in OLS regression, for ex., South = 1 or 0), the set variable (ex., Region) is entered as a fixed factor with no need for the researcher to create dummy variables manually. The b coefficients will be identical whether the regression model is run under ordinary regression (in SPSS, under Analyze, Regression, Linear) or under GLM (in SPSS, under Analyze, General Linear Model, Univariate). Where b coefficients are default output for regression in SPSS, in GLM the researcher must ask for "Parameter estimates" under the Options button. The R-square from the Regression procedure will equal the partial Eta squared from the GLM regression model.

The advantages of doing regression via the GLM procedure are that dummy variables are coded automatically, it is easy to add interaction terms, and it computes eta-squared (identical to R-squared when relationships are linear, but greater if nonlinear relationships are present). However, the SPSS regression procedure would still be preferred if the reseacher wishes output of standardized regression (beta) coefficients, wishes to do multicollinearity diagnostics, or wishes to do stepwise regression or to enter independent variables hierarchically, in blocks. PROC GLM in SAS has a greater range of options and outputs (SAS also has PROC ANOVA, but it handles only balanced designs/equal group sizes).

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spss ANCOVA Micro-Assessment

spss ANCOVA Micro-Assessment

Test of Means, Group Comparisons

Follow-up planned comparisons (e.g., FDR)

d-family effect size

While one might use adjusted means, if using experimental design the difference should be pretty much the same as original means. And typically you'll probably want to go with the regular means

However, current thinking is that the standardizer should come from the original metric, so run just the regular ANOVA and use the sqrt of the mean square error from that analysis

Graphs of (adjusted) meansfor each group also provide a qualitative examination of specific differences between groups

Just as with ANOVA, in ANCOVA we are very interested in the ratio of between-groups variance over within-groups variance.

Regression in GLM is simply a matter of entering the independent variables as covariates and, if there are sets of dummy variables (ex., Region, which would be translated into dummy variables in OLS regression, for ex., South = 1 or 0), the set variable (ex., Region) is entered as a fixed factor with no need for the researcher to create dummy variables manually. The b coefficients will be identical whether the regression model is run under ordinary regression (in SPSS, under Analyze, Regression, Linear) or under GLM (in SPSS, under Analyze, General Linear Model, Univariate). Where b coefficients are default output for regression in SPSS, in GLM the researcher must ask for "Parameter estimates" under the Options button. The R-square from the Regression procedure will equal the partial Eta squared from the GLM regression model.

The advantages of doing regression via the GLM procedure are that dummy variables are coded automatically, it is easy to add interaction terms, and it computes eta-squared (identical to R-squared when relationships are linear, but greater if nonlinear relationships are present). However, the SPSS regression procedure would still be preferred if the reseacher wishes output of standardized regression (beta) coefficients, wishes to do multicollinearity diagnostics, or wishes to do stepwise regression or to enter independent variables hierarchically, in blocks. PROC GLM in SAS has a greater range of options and outputs (SAS also has PROC ANOVA, but it handles only balanced designs/equal group sizes).

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perform and interpret repeated measures Anova (oneway) in SPSS

perform and interpret repeated measures Anova (oneway) inSPSS

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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spss ANCOVA in Terms of the General Linear Model

spss ANCOVA in Terms of the General Linear Model 

First run the ANCOVA model with a Treatment X Covariate interaction term included1

If the interaction is significant, assumption violated

Depending on the level of treatment, the relationship b/t covariate and DV changes

If not, rerun without interaction term

Or simply run the Covariate-DV regression for each group and assess in that way.2

Regression in GLM is simply a matter of entering the independent variables as covariates and, if there are sets of dummy variables (ex., Region, which would be translated into dummy variables in OLS regression, for ex., South = 1 or 0), the set variable (ex., Region) is entered as a fixed factor with no need for the researcher to create dummy variables manually. The b coefficients will be identical whether the regression model is run under ordinary regression (in SPSS, under Analyze, Regression, Linear) or under GLM (in SPSS, under Analyze, General Linear Model, Univariate). Where b coefficients are default output for regression in SPSS, in GLM the researcher must ask for "Parameter estimates" under the Options button. The R-square from the Regression procedure will equal the partial Eta squared from the GLM regression model.

The advantages of doing regression via the GLM procedure are that dummy variables are coded automatically, it is easy to add interaction terms, and it computes eta-squared (identical to R-squared when relationships are linear, but greater if nonlinear relationships are present). However, the SPSS regression procedure would still be preferred if the reseacher wishes output of standardized regression (beta) coefficients, wishes to do multicollinearity diagnostics, or wishes to do stepwise regression or to enter independent variables hierarchically, in blocks. PROC GLM in SAS has a greater range of options and outputs (SAS also has PROC ANOVA, but it handles only balanced designs/equal group sizes).

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Central Themes for ANCOVA spss

Central Themes for ANCOVA spss

Variance: in DV

The linear combination that is examined in ANCOVA is the Y score that is adjusted for the effects of the covariates

Covariance: between DV & Covariate(s)

in ANCOVA we can examine the proportion of shared variance (i.e., η2) between the adjusted Y score and the IV(s).

Ratio: Between Groups/ Within Groups

Just as with ANOVA, in ANCOVA we are very interested in the ratio of between-groups variance over within-groups variance.

Regression in GLM is simply a matter of entering the independent variables as covariates and, if there are sets of dummy variables (ex., Region, which would be translated into dummy variables in OLS regression, for ex., South = 1 or 0), the set variable (ex., Region) is entered as a fixed factor with no need for the researcher to create dummy variables manually. The b coefficients will be identical whether the regression model is run under ordinary regression (in SPSS, under Analyze, Regression, Linear) or under GLM (in SPSS, under Analyze, General Linear Model, Univariate). Where b coefficients are default output for regression in SPSS, in GLM the researcher must ask for "Parameter estimates" under the Options button. The R-square from the Regression procedure will equal the partial Eta squared from the GLM regression model.

The advantages of doing regression via the GLM procedure are that dummy variables are coded automatically, it is easy to add interaction terms, and it computes eta-squared (identical to R-squared when relationships are linear, but greater if nonlinear relationships are present). However, the SPSS regression procedure would still be preferred if the reseacher wishes output of standardized regression (beta) coefficients, wishes to do multicollinearity diagnostics, or wishes to do stepwise regression or to enter independent variables hierarchically, in blocks. PROC GLM in SAS has a greater range of options and outputs (SAS also has PROC ANOVA, but it handles only balanced designs/equal group sizes).

buy cheap SPSS statistion 21 SPSS 21  pc mac

 It is not a OEM or tryout version.

 We offer worldwide shippment .

 You can pay by paypal.

Full version  cheap SPSS statistion 21 spss 21   at   $54