Thank you for your response. so, if variable "YOA" refers to the three groups that are called AM, OA and YA and variable "cond" refer to the condition (e.g. shoe type) and include conditions 1, 2 and 3. when i include lsmeans YOA*cond/lines; in my codes, i see the following:  T Grouping for YOA*cond Least Squares Means (Alpha=0.05) LS-means with the same letter are not significantly different. YOA  cond Estimate                   OA 3 15.9907   A           A   OA 1 14.4178 B A         B A   OA 2 13.7219 B A         B A   AM 2 13.2346 B A         B A   AM 3 12.613 B A         B A   AM 1 11.5453 B A C       B   C YA 3 10.5709 B   C           C YA 1 7.4639     C           C YA 2 7.0647     C   does this mean that under condition 3 (i.e. the same shoe type),  OA and AM were not different?  also, AM conditions  1 and 2 were significantly different? In other words, which column do I have to look at?   thanks   ... View more

  proc mixed data=temp; class subj GROUP ShoeType ; model SPEED= GROUP ShoeType GROUP*ShoeType ; random subj (GROUP); lsmeans GROUP; run;     $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$   Kdisart YA                                              10:25 Monday, January 27, 2020  22   The Mixed Procedure                     Model Information   Data Set                     WORK.TEMP Dependent Variable           SPEED Covariance Structure         Variance Components Estimation Method            REML Residual Variance Method     Profile Fixed Effects SE Method      Model-Based Degrees of Freedom Method    Containment                  Class Level Information   Class    Levels    Values   subj        26    3 4 5 7 8 9 10 11 12 13 15 16                    17 19 20 21 22 23 24 25 26 27                    29 30 31 32 GROUP           3    AM OA YA ShoeType          3    1 2 3                 Dimensions   Covariance Parameters             2 Columns in X                     16 Columns in Z                     26 Subjects                          1 Max Obs per Subject             234               Number of Observations   Number of Observations Read             234 Number of Observations Used             234 Number of Observations Not Used           0                          Iteration History   Iteration    Evaluations    -2 Res Log Like       Criterion           0              1      1001.35302689         1              1       954.65672525      0.00000000                        Convergence criteria met.      Covariance Parameter        Estimates   Cov Parm       Estimate   subj(GROUP)       1.5245 Residual         3.0052                Fit Statistics   -2 Res Log Likelihood           954.7 AIC (Smaller is Better)         958.7 AICC (Smaller is Better)        958.7 BIC (Smaller is Better)         961.2             Type 3 Tests of Fixed Effects                 Num     Den Effect         DF      DF    F Value    Pr > F   GROUP             2      23       1.09    0.3540 ShoeType            2     202      13.11    <.0001 GROUP*ShoeType        4     202       2.78    0.0280                             Least Squares Means                                Standard Effect    GROUP    Estimate       Error      DF    t Value    Pr > |t|   GROUP       AM       5.5424      0.5565      23       9.96      <.0001 GROUP       OA       5.4719      0.4311      23      12.69      <.0001 GROUP       YA       4.6901      0.4311      23      10.88      <.0001   $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$ I also receive the following when I type lsmeans GROUP*ShoeType $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$                                      Least Squares Means                                                  Standard Effect      GROUP    ShoeType            Estimate       Error      DF    t Value    Pr > |t|   GROUP*ShoeType    AM                1      4.7795      0.6489     202       7.37      <.0001 GROUP*ShoeType    AM                2      5.9108      0.6489     202       9.11      <.0001 GROUP*ShoeType    AM                3      5.9370      0.6489     202       9.15      <.0001 GROUP*ShoeType    OA                1      5.3605      0.5026     202      10.67      <.0001 GROUP*ShoeType    OA                2      4.8596      0.5026     202       9.67      <.0001 GROUP*ShoeType    OA                3      6.1958      0.5026     202      12.33      <.0001 GROUP*ShoeType    YA                1      3.6963      0.5026     202       7.35      <.0001 GROUP*ShoeType    YA                2      4.3853      0.5026     202       8.72      <.0001 GROUP*ShoeType    YA                3      5.9887      0.5026     202      11.92      <.0001 ... View more

So when i use lsmeans, waht does the p value referes to?  So if i have gorups A, B and C and i include a statement lsmean(group_name), i get something like the following:    Effect                  Group_name      Estimate Error DF t Value Pr > |t| Group_name              A               .......   Is the p value refering to the difference in mean of A as compared to all other groups? thanks   ... View more

Thank you PaigeMiller Let's consider that i have 35 subjects in the three unequal groups. the subject_id variable goes from 1 to 35. All subjects run 5 minutes with each of the shoe types while i measure their speed. For instance, it can be running bare feet, sport shoe and dress shoe. the order of shoe type for each subject is randomly assigned. so, technically, each subject can only appear in one group but is tested three times for the three conditions of shoe type.   PS:can you also tell me how the code would have been different if a- each test was repeated three times. i.e. each subject belongs to one of the three age groups but runs 9 times (three times with each shoe).   b- can you also tell my how the code would have been different if the subject appeared in more than one group. For instance if my test groups instead of being different in age, they were the same subject by were were tested before and after having breakfast, or were tested morning, afternoon and night) ... View more