2012年10月30日星期二

spss ANCOVA Micro-Assessment

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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2012年10月27日星期六

spss TESTING THE HOMOGENEITY-OF-REGRESSION (SLOPES) ASSUMPTION

spss TESTING THE HOMOGENEITY-OF-REGRESSION (SLOPES) ASSUMPTION

spss TESTING THE HOMOGENEITY-OF-REGRESSION (SLOPES) ASSUMPTION
Before we get started – we must first conduct a test of the homogeneity-ofregression (slopes) assumption. To conduct this test, follow these steps:
1. Click Analyze, click General Linear Model, and then click Univariate
2. Click the dependent variable, then click to move it to the Dependent
Variable box
3. Click the independent variable, then click to move it to the Fixed
Factor(s) box
4. Click the covariate, then click to move it to the Covariate(s) box
5 Click Model
6 Click Custom under Specify Model
7. Click the independent variable under Factors & Covariates and click to
make it appear in the Model box
8. Click the covariate under Factors & Covariates and click to make it
appear in the Model box
9. Holding down the Ctrl key, click the independent variable (IV) and the
covariate (Cov) in the Factors & Covariates box. Check to see that the
default option Interaction is specified in the drop-down menu in the Build
Term(s) box. If it is not, select it
10. Click and the IV*Cov should now appear in the Model box
11. Click Continue. This will bring you back to the Univariate screen
12. Click OK

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2012年10月22日星期一

Analysis of Covariance in spss

Analysis of Covariance in spss

Analysis of Covariance 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
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How to input data into the SPSS data editor

How to input data into the SPSS data editor

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.
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