Solved – How to report SPSS OLS output

interpretationleast squaresregressionspss

Consider the model below:

enter image description here

In many research papers, significance of statistical results are indicated by *, **, and *** as a significant value at respectively the 1%, 5%, or 10% level.

Does a Sig. value of .047 (forninstance, LAW_LE) mean a significane at the 4,7% level? (So that I may report LAW_LE 0.313**)?

Second, I see many times the Std.Error is also reported in papers. How do I need to interpret the standard error in combination with the significance? For instance, COM_BIK has a standard error of 0.045 and a significance of 0.021; whereas HOF_MAS has a low standard error (0.002) but a significance of .416. What can be said about these two variables based on this information?

Your help is greatly appreciated.

Best Answer

It depends on the field, but I think this is the most common approach:

Report coefficients that have a p-value that is less than 0.05 but greater than 0.01 with one asterisk (*) -- equivalent to the 5% level of significance;
Report coefficients that have a p-value that is less than 0.01 but greater than 0.001 with two asterisks (**);
Report coefficients that have a p-value that is less than 0.001 with three asterisks (*)

So the LAW_LE would get one asterisk. Usually/often the standard error is also reported, as you suggest. Again, this varies according to the specific field of study, it is fairly standard to see values in a table as the coefficient, the significance level and the standard error in parentheses. So for your LAW_LE you would have

LAW_LE $0.075^{*}$ $(0.037)$

(unless you wanted to report the standardized coefficients in which case $0.075$ would be replaced with $0.313$ -- the decision as to whether you report standardized or unstandardized coefficients is another question and has probably been covered here).

Related Solutions

Solved – SPSS dumthe variables in OLS

If you have the advanced statistics package that allows you do estimate generalized linear models (see the menus Analyze -> Generalized Linear Models or the GENLIN command), you can have SPSS on the fly generate the dummy variables. Given your data it may be good to see if some of the newer mixed model commands can estimate auto-regressive components for panel data.

Alternatively, you can use the DO REPEAT syntax to efficiently generate your dummy variables for use in regression equations. For instance, for your weekDay variable it would be;

VECTOR weekDay_Dummy(7,F1.0).
DO REPEAT weekDay_Dummy = weekDay_Dummy1 to weekDay_Dummy7 /i = 1 to 7.
    DO IF weekDay = i. 
        COMPUTE weekDay_Dummy = 1.
    ELSE IF weekDay <> i.
        COMPUTE weekDay_Dummy = 0.
    END IF.
END REPEAT.

As long as your variables are in a sequential list of integer values, the do repeat command will work (if they aren't in a sequential list see the AUTORECODE command). Then in the linear regression command you can subsequently use the TO operator to specify a list of variables that are in sequential order in your dataset (extra note it has to do with the order of the variables in the dataset, nothing to do with the names directly).

Below I have an example.

data list free / company  sector  obsDay weekDay  stockPrice.
begin data
1        15      1      3        10.40
1        15      2      4         9.42
1        15      3      5         9.66
1        15      4      1        11.00
1        15      5      2        10.21
2        10      1      3        43.55
2        10      2      4        43.50
2        10      3      5        40.31
2        10      4      1        48.43
2        10      5      2        43.00
3        20      1      3        10.00
3        20      2      4        11.00
3        20      3      5        12.00
3        20      4      1        13.00
3        20      5      2        14.00
end data.
dataset name examp.

VECTOR weekDay_Dummy(7,F1.0).
DO REPEAT weekDay_Dummy = weekDay_Dummy1 to weekDay_Dummy7 /i = 1 to 7.
    DO IF weekDay = i. 
        COMPUTE weekDay_Dummy = 1.
    ELSE IF weekDay <> i.
        COMPUTE weekDay_Dummy = 0.
    END IF.
END REPEAT.

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN 
  /DEPENDENT stockPrice
  /METHOD=ENTER weekDay_Dummy2 to weekDay_Dummy5.

Another extension command with more flexibility for writing dummy variables, SPSSINC_CREATE_DUMMIES (written in Python) is on the developerworks site (but I have not used it). Also one of the members here, ttnphns, has some tools to accomplish similar tasks on his site. Given your example though a few do repeat commands should be sufficient.

Solved – How to control for market return in an (SPSS) OLS

It'd be helpful if you told us what procedure you used. My answers rely on some guesses.

Question 1: If you're running the OLS regression using Analyze > Regression, then they cannot be random effects because this module does not allows it. So, they can be seen as fixed effects. If you have used Mixed module then it would depend where you put the variables: whether they were fed into the fixed, or random slot.

We use fixed effect to discern mean difference, and we use random effect to adjust for variance introduced by the variables. If a variable returns a regression coefficient (or a set of coefficients in the case of categorical variables), it belongs to fixed effect; if a variable ends up in the variance/covariance output, it's been treated as a random effect.

Another way to think if you have correctly modeled the variables is to imagine if you're to repeat the measurement, will the attributes inside that variable change? In your case, all the dummies' attributes probably wouldn't change, so I would agree that they are fixed effects. My concern, as I've stated in the comment, goes to the company ID. It's a repeated measurement design and you may want to consider using Mixed model. In addition, if the companies were randomly chosen, you may want to consider allowing a random intercept/slope for your regression model. But again, this is just my crude guess. You better discuss with your statistical support.

Question 2: Just by the wording, "added the market return as a control" usually just means "market return" is treated as one of the independent variables.

Question 3: Well, I am actually not entirely sure. I feel that control variable can either be fixed or random because there are needs to control for mean difference and to control for variance. For instance, you can "control" for the effect of gender as a fixed effect, and you can also "control" for the clustering due to state/province by treating it as a random effect. I have seen both of these wordings used.

Best Answer

Related Solutions

Solved – SPSS dumthe variables in OLS

Solved – How to control for market return in an (SPSS) OLS

Related Question