# Differences between Stata & SPSS in multiple regression outcomes

I'm a graduate student working on a thesis whose aim is to test complementarity between 2 practices (CI and INNO, according to theory of supermodularity). To do so, I calculated two new variables (each as a mean of 6 other variables) corresponding to the 2 practices, then derived from them 4 binary variables based on the combination of the two (that is, I used medians of the two variables as a threshold, then created four categories like 11,10,01,00: HH,LH,HL,LL).

I took these four binaries, (plus two continuous variables and three other dummies as controls) and ran a multiple regression on Y (operational performance), without a constant term.

I used both SPSS and Stata, but obtained two very different outcomes. I don't understand why!!! Moreover, correlation matrices gave the same results. Here are all the outcomes:

SPSS

Variabili escluse a,b
Modello Beta In t   Sig.    Correlazioni parziali   Statistiche di collinearità
Tolleranza  VIF Tolleranza minima
1   LL  .c  .   .   .   ,000    .   ,000
a. Variabile dipendente: PERF_medio
b. Regressione lineare che passa per l'origine
c. Predittori nel modello : industryT, industryM, industryE, HL, LH, HH,
log_age, log_size

Coefficienti non standardizzati
B     STD ERROR DEV
LH       ,188     ,110
HL       ,156     ,115
HH       ,467     ,102
log_size   ,008   ,040
log_age   -,003   ,039
industryE 3,416   ,244
industryM 3,487   ,255
industryT 3,551   ,246


As you can see, the LL variable is excluded; Stata doesn't exclude this. Moreover multicollinearity tests give positive results, whereas VIF performed with Stata after regression do not.

STATA:

regress PERF_medio LL LH HL HH log_size log_age industryE industryM industryT, noconstant
note: industryM omitted because of collinearity

Source  SS       df     MS               Number of obs    =     190
F(  8,   182)  =   1253.59
Model   2654.3682   8   331.796025           Prob > F           =    0.0000
Residual    48.1709704  182 .264675662   R-squared  =    0.9822
Total   2702.53917  190 14.2238904       Root MSE           =    .51447

PERF_medio |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
LL |   3.486898   .2548016    13.68   0.000     2.984153    3.989643
LH |   3.675309   .2746033    13.38   0.000     3.133493    4.217124
HL |   3.643261   .2646596    13.77   0.000     3.121066    4.165457
HH |   3.954367   .2753474    14.36   0.000     3.411083     4.49765
log_size |    .008443   .0400578     0.21   0.833    -.0705944    .0874803
log_age |  -.0027773   .0393044    -0.07   0.944    -.0803283    .0747737
industryE |  -.0706952   .0935333    -0.76   0.451    -.2552442    .1138538
industryM |          0  (omitted)
industryT |   .0639002   .0960992     0.66   0.507    -.1257117     .253512

estat vif, uncentered

Variable |       VIF       1/VIF
-------------+----------------------
log_size |     42.17    0.023711
HH |     16.90    0.059170
LL |     15.21    0.065753
log_age |     12.70    0.078726
LH |     10.26    0.097499
HL |      8.73    0.114505
industryT |      2.23    0.447811
industryE |      2.21    0.451551
-------------+----------------------
Mean VIF |     13.80


Thus, SPSS excludes LL, but Stata does not. What's wrong? Why are coefficients so different?

• Stata does appear to be excluding something, industryM, it's just making a different default choice than SPSS. – gung - Reinstate Monica Aug 9 '12 at 16:41
• i know stata is omitting industryM whereas SPSS is omitting LL; the point is, i need LL to be in the model, because i have to test that betaHH-betaHL>betaLH-betaLL, but the stata model gives evidence of multicollinearity, if i am interepreting the last table correctly...I'm sorry but I'm not an statistics expert, so any comment will be really appreciated – MatBi Aug 9 '12 at 16:48
• Hint Did you notice that Stata has omitted industryM? The two programs are using different codings for categorical variables. Searching our site yields specific advice, such as stats.stackexchange.com/questions/20166 and stats.stackexchange.com/questions/24389. – whuber Aug 9 '12 at 18:03
• @whuber gives good advice (as usual!) but I'd just add a note that this is a REALLY easy problem to have (and to overlook). Default choices of coding vary from program to program and even within a particular program for different statistics, so it pays to be careful. – Peter Flom Aug 9 '12 at 20:20
• I have to thank you guys for the comments! Bytheway, it is incredible how the coefficients diverge from one software to the other (e.g., HH in spss is .467, in stata is 3.95, which is the actual mean for the sub-sample) – MatBi Aug 10 '12 at 9:21