2012年10月30日星期二

Steps for ANCOVA spss

Steps for ANCOVA spss

Steps for ANCOVA spss
Consider the following:

All variables reliable?

Are groups sufficiently homogeneous, i.e., low within group variance?

Low to No correlation between IV & covariates?

Notable correlation between DV & covariates?

Can the design support causal inference (e.g., random assignment to manipulated IV, control confounds)?

Are means significantly different, i.e., high between group variance?

Do groups differ after controlling for covariate?

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 

2012年10月27日星期六

use spss CONDUCTING THE ONE-WAY ANCOVA

use spss CONDUCTING THE ONE-WAY ANCOVA

1. Click Analyze, click General Linear Model, and then click Univariate
2. Click Reset
If you have not exited SPSS – the prior commands will still be shown. As a
precaution for avoiding possible errors – click the reset key and begin the
procedure from the initial starting point
3. Click the dependent variable, then click to move it to the Dependent
Variable box
4. Click the independent variable, then click to move it to the Fixed
Factor(s) box
5. Click the covariate, then click to move it to the Covariate(s) box
6. Click on Options
7. In the Factor(s) and Factor Interactions box, click the independent variable
This will provide the adjusted (Estimated Marginal) means that will be
used later (if needed) in post hoc procedures
8. Click to move it to the Display Means for box
9. Select Descriptive statistics in the Display box
10. Select Homogeneity tests in the Display box
11. Click Continue
12. This will bring you back to the Univariate screen…
13. Click Plots
14. Click the independent variable, then click to move it to the Horizontal
Axis: box
15. Click Add
16. Click Continue
17. This will bring you back to the Univariate screen – click OK

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 

2012年10月22日星期一

Similarities with ANCOVA in spss

Similarities with ANCOVA in spss

Similarities with ANCOVA in spss
When you think of ANCOVA, you should think of sequential regression, as it can be conducted as such

Covariate(s) enter in step 1, categorical predictor after

Want to assess how much variance is accounted for in the DV after controlling for (partialing out) the effects of one or more continuous IV-covariates

ANCOVA always has at least 1 or more categorical, grouping IVs, and 1 or more continuous covariates).

Covariate:

Want high correlation with DV; low with other covariates

Want few covariates

Recall that you are partialling out variance in the DV, having the group variable explain very little leftover variance is unappealing

If covariate correlates with IV
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 

Entering data in SPSS

Entering data in SPSS

Entering 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).When we are creating a new data set, it is typical to start by definining the names and other properties of the variables first and then entering the specific values into each variable for each independent source of data. Recall that there is one row for each independent source of data and one column for each characteristic (i.e., variable) that we have measured from each data source. There are times, however, when we decide to add additional variables after we have entered some of the data. Adding variables after the fact does not present any special challenges; we simply go to the variable view, click in an empty row, and start defining our new variables as we do below. The first step to defining variable names and properties is to select the variable view tab in the data window. Then we can create (or edit) each of the properties below.
Name
The name of each SPSS variable in a given file must be unique; it must start with a letter; it may have up to 8 characters (including letters, numbers, and the underscore _ (note that certain key words are reversed and may not be used as variable names, e.g., "compute", "sum", and so forth). To change an existing name, click in the cell containing the name, highlight the part you want to change, and type in the replacement. To create a new variable name, click in the first empty row under the name column and type a new (unique) variable name.
 
Notice that we can use "cat_dog" but not "cat-dog" and not "cat dog". The hyphen gets interpreted as subtraction (cat minus dog) by SPSS, and the space confuses SPSS as to how many variables are being named.
Type
The two basic types of variables that you will use are numeric and string. Numeric variables may only have numbers assigned. String variables may contain letters or numbers, but even if a string variable happens to contain only numbers, numeric operations on that variable will not be allowed (e.g., finding the mean, variance, standard deviation, etc...). To change a variable type, click in that cell on the grey box with ...
 
Clicking on this box will bring up the variable type menu:
 
If you select a numeric variable, you can then click in the width box or the decimal box to change the default values of 8 characters reserved to displaying numbers with 2 decimal places. For whole numbers, you can drop the decimals down to 0.
If you select a string variable, you can tell SPSS how much "room" to leave in memory for each value, indicating the number of characters to be allowed for data entry in this string variable.
Width
The width of a variable is the number of characters SPSS will allow to be entered for the variable. If it is a numerical value with decimals, this total width has to include a spot for each decimal, as well as one for the decimal point. You can change a width by clicking in the width cell for the desired variable and typing a new number or you can use the arrow keys at the edge of the cell
 
Decimals
The decimals of a variable is the number of decimal places that SPSS will display. If more decimals have been entered (or computed by SPSS), the additional information will be retained internally but not displayed on screen. For whole numbers, you would reduce the number of decimals to zero. You can change the number of decimal places by clicking int he decimals cell for the desired variable and typing a new number or you can use the arrow keys at the edge of the cell
 
Label
The label of a variable is a string of text to indentify in more detail what a variable represents. Unlike the name, the label is limited to 255 characters and may contain spaces and punctuation. For instance, if there is a variable for each question on a questionnaire, you would type the question as the variable label. To change or edit a variable label, simply click anywhere within the cell.
 
Values
Although the variable label goes a long way to explaining what the variable represents, for categorical data (discrete data of both nominal and ordinal levels of measurement), we often need to know which numbers represent which categories. To indicate how these numbers are assigned, one can add labels to specific values by clicking on the ... box in the values cell
 
Clicking here opens up the Value Labels dialogue box.
 
Click in the Value field to type a specific numeric value
Click in the Label field to type the corresponding label
Click on the Add button to add this pair of value and label to the list
You can remove a pairing created above by clicking on that pair and then clicking on the delete button. Similarly, you can change pairing by clicking on the pair, then typing in a new value, a new label, or both; then, you click on the Change button. When you are satisfied with the definitions of each value, click on the OK button
The real beauty of value labels can be seen in the Data View by clicking on the "toe tag" icon in the tool bar , which switches between the numeric values and their labels
 
Missing
We sometimes want to signal to SPSS that data should be treated as missing, even though there is some other numerical code recorded instead of the data actually being missing (in which case SPSS displays a single period -- this is also called SYSTEM MISSING data). In this example, after clicking on the ... button in the Missing cell, I declared "9", "99", and "999" all to be treated by SPSS as missing (i.e., these values will be ignored)
 
Columns
The columns property tells SPSS how wide the column should be for each variable. Don't confuse this one with width, which indicates how many digits of the number will be displayed. The column size indicates how much space is allocated rather than the degree to which it is filled.
Align
The alignment property indicates whether the information in the Data View should be left-justified, right-justified, or centered
 
Measure
The Measure property indicates the level of measurement. Since SPSS does not differentiate between interval and ratio levels of measurement, both of these quantitative variable types are lumped together as "scale". Nominal and ordinal levels of measurement, however, are differentiated
 
Entering the Data
The first step for entering the actual data is to click on the Data View tab.
To enter new data, click in an empty cell in the first empty row. The "Tab" key will enter the value and jump to the next cell to the right. You may also use the Up, Down, Left, and Right arrow keys to enter values and move to another cell for data input.
To edit existing data points (i.e., the change a specific data value), click in the cell, type in the new value, and press the Tab, Enter, Up, Down, Right, or Left arrow keys.
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 

2012年10月19日星期五

how to perform and interpret a paired samples t-test in SPSS

how to perform and interpret a paired samples t-test in SPSS

What it does: The Paired Samples T Test compares the means of two variables. It computes the difference between the two variables for each case, and tests to see if the average difference is significantly different from zero.

Where to find it: Under the Analyze menu, choose Compare Means, then choose Paired Samples T Test. Click on both variables you wish to compare, then move the pair of selected variables into the Paired Variables box.

Assumption:
-Both variables should be normally distributed. You can check for normal distribution with a Q-Q plot.

Hypothesis:
Null: There is no significant difference between the means of the two variables.
Alternate: There is a significant difference between the means of the two variables.

SPSS Output

Following is sample output of a paired samples T test. We compared the mean test scores before (pre-test) and after (post-test) the subjects completed a test preparation course. We want to see if our test preparation course improved people's score on the test.

First, we see the descriptive statistics for both variables.

The post-test mean scores are higher.

Next, we see the correlation between the two variables.

There is a strong positive correlation. People who did well on the pre-test also did well on the post-test.

Finally, we see the results of the Paired Samples T Test. Remember, this test is based on the difference between the two variables. Under "Paired Differences" we see the descriptive statistics for the difference between the two variables.

To the right of the Paired Differences, we see the T, degrees of freedom, and significance.

The T value = -2.171

We have 11 degrees of freedom

Our significance is .053

If the significance value is less than .05, there is a significant difference.
If the significance value is greater than. 05, there is no significant difference.

Here, we see that the significance value is approaching significance, but it is not a significant difference. There is no difference between pre- and post-test scores. Our test preparation course did not help!

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 

how to perform a paired sample t-test using SPSS/PASW

how to perform a paired sample t-test using SPSS/PASW

When do we use Paired-Sample T-Test?
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.

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