Deskriptiva

Column

Godina 2017.

Godina 2022.

Deskriptiva - slike

Column

Godina 2017.

Column

Godina 2022.

Korelacije

Column

Godina 2017.

Godina 2022.

Korelacije - slike

Column1

korelacije 2017.

p-vrijednosti 2017.

korelacije 2022.

p-vrijednosti 2022.

Column1

korelacije 2022.

p-vrijednosti 2022.

korelacije 2017.

p-vrijednosti 2017.

Unutarnja pouzdanost

Column

Cronbach alpha


Reliability analysis   
Call: psych::alpha(x = podaci_EFA)

  raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd median_r
      0.94      0.94    0.97       0.3  16 0.0055  3.6 0.65     0.28

    95% confidence boundaries 
         lower alpha upper
Feldt     0.93  0.94  0.95
Duhachek  0.93  0.94  0.95

 Reliability if an item is dropped:
                        raw_alpha std.alpha G6(smc) average_r S/N alpha se
TIMELINE                     0.94      0.94    0.97      0.30  16   0.0056
RISK                         0.94      0.94    0.97      0.30  16   0.0056
AVERAGE                      0.94      0.94    0.97      0.30  16   0.0056
SCHEDULE                     0.94      0.94    0.97      0.30  16   0.0056
CALENDAR                     0.94      0.94    0.97      0.30  16   0.0056
COURSES                      0.94      0.94    0.97      0.30  16   0.0056
NOTIFICATIONS_COURSE         0.94      0.94    0.97      0.30  16   0.0056
NOTIFICATIONS_DEADLINES      0.94      0.94    0.97      0.30  16   0.0056
MESSAGES_TEACHER             0.94      0.94    0.97      0.30  16   0.0056
MESSAGES_ADMIN               0.94      0.94    0.97      0.30  16   0.0055
COMPARISON_EXAMS             0.94      0.94    0.97      0.30  16   0.0057
COMPARISON_ECTS              0.94      0.94    0.97      0.30  16   0.0057
COMPARISON_GRADES            0.94      0.94    0.97      0.30  16   0.0057
COMPARISON_OBLIGATIONS       0.94      0.94    0.97      0.30  16   0.0057
STATUS_STUDENTS              0.94      0.94    0.97      0.30  16   0.0056
PREDICTION_COURSE            0.94      0.94    0.97      0.30  16   0.0057
PREDICTION_YEAR              0.94      0.94    0.97      0.30  16   0.0057
PREDICTION_ECTS              0.94      0.94    0.97      0.30  16   0.0058
PREDICTION_PROGRAM           0.94      0.94    0.97      0.30  16   0.0057
ECTSAVG_STUDY                0.94      0.94    0.97      0.30  16   0.0057
ECTSAVG_COURSE               0.94      0.94    0.97      0.30  16   0.0057
AVGGRADE_COURSE              0.94      0.94    0.97      0.30  16   0.0057
PASSRATE_COURSE              0.94      0.94    0.97      0.30  16   0.0056
PPLAN_COURSE                 0.94      0.94    0.97      0.30  16   0.0057
PPLAN_STUDY                  0.94      0.94    0.97      0.30  16   0.0057
PPLAN_MONITORING             0.94      0.94    0.97      0.30  16   0.0057
ECTS_MONITORING              0.94      0.94    0.97      0.30  16   0.0057
PPLAN_EXTRACURRICULAR        0.94      0.94    0.97      0.30  16   0.0056
TEACHER_EVALUATION           0.94      0.94    0.97      0.30  16   0.0055
TEACHER_PROFILE              0.94      0.94    0.97      0.30  16   0.0056
TEACHERS_CONTEVAL            0.94      0.94    0.97      0.31  16   0.0055
TEACHERS_FEEDBACK            0.94      0.94    0.97      0.31  16   0.0055
BADGES_COMPARISON            0.94      0.94    0.97      0.30  16   0.0057
BADGES_COLLECTING            0.94      0.94    0.97      0.30  16   0.0057
AWARDS                       0.94      0.94    0.97      0.30  16   0.0057
COMPETITIONS                 0.94      0.94    0.97      0.30  16   0.0057
ASSESSMENT_CONT              0.94      0.94    0.97      0.30  16   0.0056
COMPARISON_OTHERS            0.94      0.94    0.97      0.30  16   0.0057
                        var.r med.r
TIMELINE                0.021  0.28
RISK                    0.021  0.28
AVERAGE                 0.021  0.28
SCHEDULE                0.021  0.28
CALENDAR                0.021  0.28
COURSES                 0.021  0.28
NOTIFICATIONS_COURSE    0.021  0.28
NOTIFICATIONS_DEADLINES 0.021  0.28
MESSAGES_TEACHER        0.021  0.28
MESSAGES_ADMIN          0.021  0.28
COMPARISON_EXAMS        0.020  0.28
COMPARISON_ECTS         0.020  0.28
COMPARISON_GRADES       0.020  0.28
COMPARISON_OBLIGATIONS  0.021  0.28
STATUS_STUDENTS         0.020  0.28
PREDICTION_COURSE       0.021  0.27
PREDICTION_YEAR         0.021  0.28
PREDICTION_ECTS         0.021  0.27
PREDICTION_PROGRAM      0.021  0.28
ECTSAVG_STUDY           0.020  0.28
ECTSAVG_COURSE          0.020  0.28
AVGGRADE_COURSE         0.021  0.28
PASSRATE_COURSE         0.021  0.28
PPLAN_COURSE            0.021  0.28
PPLAN_STUDY             0.020  0.28
PPLAN_MONITORING        0.021  0.28
ECTS_MONITORING         0.021  0.27
PPLAN_EXTRACURRICULAR   0.021  0.28
TEACHER_EVALUATION      0.021  0.28
TEACHER_PROFILE         0.021  0.29
TEACHERS_CONTEVAL       0.021  0.29
TEACHERS_FEEDBACK       0.021  0.29
BADGES_COMPARISON       0.021  0.28
BADGES_COLLECTING       0.021  0.28
AWARDS                  0.021  0.28
COMPETITIONS            0.021  0.28
ASSESSMENT_CONT         0.022  0.28
COMPARISON_OTHERS       0.021  0.28

 Item statistics 
                          n raw.r std.r r.cor r.drop mean   sd
TIMELINE                222  0.48  0.49  0.47   0.45  3.9 1.06
RISK                    222  0.54  0.55  0.54   0.51  4.0 1.07
AVERAGE                 222  0.48  0.47  0.46   0.43  3.4 1.25
SCHEDULE                222  0.43  0.46  0.44   0.40  4.3 0.92
CALENDAR                222  0.41  0.45  0.43   0.39  4.5 0.75
COURSES                 222  0.53  0.55  0.54   0.50  4.0 1.04
NOTIFICATIONS_COURSE    222  0.49  0.51  0.50   0.46  4.4 0.86
NOTIFICATIONS_DEADLINES 222  0.52  0.54  0.53   0.49  4.2 0.91
MESSAGES_TEACHER        222  0.46  0.49  0.47   0.43  4.1 0.86
MESSAGES_ADMIN          222  0.42  0.44  0.42   0.38  3.5 1.07
COMPARISON_EXAMS        222  0.64  0.62  0.62   0.61  3.1 1.24
COMPARISON_ECTS         222  0.66  0.63  0.63   0.62  2.9 1.31
COMPARISON_GRADES       222  0.63  0.60  0.60   0.59  3.0 1.28
COMPARISON_OBLIGATIONS  222  0.67  0.65  0.64   0.64  3.0 1.25
STATUS_STUDENTS         222  0.55  0.53  0.52   0.51  3.0 1.30
PREDICTION_COURSE       222  0.67  0.68  0.68   0.65  3.9 1.17
PREDICTION_YEAR         222  0.63  0.63  0.63   0.60  3.9 1.18
PREDICTION_ECTS         222  0.69  0.70  0.70   0.67  3.9 1.25
PREDICTION_PROGRAM      222  0.61  0.62  0.61   0.58  4.0 1.17
ECTSAVG_STUDY           222  0.63  0.61  0.61   0.60  3.1 1.21
ECTSAVG_COURSE          222  0.64  0.63  0.62   0.61  3.2 1.14
AVGGRADE_COURSE         222  0.63  0.62  0.61   0.60  3.5 1.14
PASSRATE_COURSE         222  0.56  0.56  0.55   0.53  3.8 1.13
PPLAN_COURSE            222  0.62  0.64  0.64   0.59  4.0 1.12
PPLAN_STUDY             222  0.60  0.62  0.63   0.57  4.1 1.08
PPLAN_MONITORING        222  0.66  0.67  0.67   0.63  4.1 1.04
ECTS_MONITORING         222  0.68  0.69  0.68   0.66  3.7 1.17
PPLAN_EXTRACURRICULAR   222  0.53  0.53  0.51   0.49  3.4 1.21
TEACHER_EVALUATION      222  0.45  0.44  0.42   0.41  3.5 1.22
TEACHER_PROFILE         222  0.46  0.46  0.44   0.42  3.3 1.17
TEACHERS_CONTEVAL       222  0.40  0.40  0.38   0.35  3.5 1.23
TEACHERS_FEEDBACK       222  0.41  0.41  0.40   0.37  3.8 1.16
BADGES_COMPARISON       222  0.62  0.60  0.60   0.58  2.7 1.28
BADGES_COLLECTING       222  0.64  0.62  0.62   0.61  3.0 1.35
AWARDS                  222  0.61  0.59  0.59   0.58  2.8 1.28
COMPETITIONS            222  0.60  0.58  0.58   0.56  2.8 1.24
ASSESSMENT_CONT         222  0.51  0.54  0.52   0.49  4.5 0.83
COMPARISON_OTHERS       222  0.67  0.64  0.64   0.64  3.0 1.28

Non missing response frequency for each item
                           1    2    3    4    5 miss
TIMELINE                0.05 0.07 0.13 0.47 0.28    0
RISK                    0.03 0.09 0.13 0.38 0.37    0
AVERAGE                 0.09 0.21 0.15 0.36 0.19    0
SCHEDULE                0.01 0.05 0.09 0.30 0.55    0
CALENDAR                0.01 0.02 0.05 0.28 0.64    0
COURSES                 0.04 0.06 0.14 0.41 0.35    0
NOTIFICATIONS_COURSE    0.02 0.02 0.06 0.36 0.54    0
NOTIFICATIONS_DEADLINES 0.03 0.02 0.09 0.39 0.46    0
MESSAGES_TEACHER        0.01 0.02 0.17 0.43 0.36    0
MESSAGES_ADMIN          0.04 0.15 0.29 0.34 0.17    0
COMPARISON_EXAMS        0.12 0.21 0.22 0.32 0.14    0
COMPARISON_ECTS         0.17 0.25 0.18 0.28 0.13    0
COMPARISON_GRADES       0.14 0.24 0.23 0.24 0.15    0
COMPARISON_OBLIGATIONS  0.14 0.24 0.20 0.30 0.11    0
STATUS_STUDENTS         0.15 0.24 0.23 0.23 0.15    0
PREDICTION_COURSE       0.07 0.08 0.13 0.39 0.34    0
PREDICTION_YEAR         0.07 0.07 0.11 0.37 0.38    0
PREDICTION_ECTS         0.07 0.10 0.10 0.32 0.41    0
PREDICTION_PROGRAM      0.07 0.05 0.11 0.35 0.42    0
ECTSAVG_STUDY           0.11 0.22 0.28 0.26 0.14    0
ECTSAVG_COURSE          0.08 0.23 0.27 0.30 0.12    0
AVGGRADE_COURSE         0.06 0.18 0.19 0.40 0.17    0
PASSRATE_COURSE         0.05 0.10 0.15 0.41 0.28    0
PPLAN_COURSE            0.05 0.07 0.15 0.33 0.41    0
PPLAN_STUDY             0.05 0.05 0.12 0.34 0.44    0
PPLAN_MONITORING        0.05 0.04 0.10 0.40 0.41    0
ECTS_MONITORING         0.06 0.09 0.24 0.30 0.32    0
PPLAN_EXTRACURRICULAR   0.07 0.17 0.27 0.26 0.23    0
TEACHER_EVALUATION      0.09 0.13 0.18 0.38 0.21    0
TEACHER_PROFILE         0.08 0.15 0.28 0.31 0.18    0
TEACHERS_CONTEVAL       0.07 0.16 0.23 0.27 0.27    0
TEACHERS_FEEDBACK       0.05 0.10 0.20 0.33 0.32    0
BADGES_COMPARISON       0.23 0.23 0.24 0.21 0.09    0
BADGES_COLLECTING       0.20 0.18 0.18 0.31 0.13    0
AWARDS                  0.20 0.21 0.23 0.27 0.09    0
COMPETITIONS            0.18 0.24 0.23 0.27 0.09    0
ASSESSMENT_CONT         0.02 0.02 0.05 0.24 0.67    0
COMPARISON_OTHERS       0.14 0.23 0.23 0.25 0.15    0

Column

split-half

Split half reliabilities  
Call: splitHalf(r = podaci_EFA)

Maximum split half reliability (lambda 4) =  0.98
Guttman lambda 6                          =  0.97
Average split half reliability            =  0.94
Guttman lambda 3 (alpha)                  =  0.94
Guttman lambda 2                          =  0.95
Minimum split half reliability  (beta)    =  0.82
Average interitem r =  0.3  with median =  0.28

Kaiser-Meyer-Olkin

Kaiser-Meyer-Olkin factor adequacy
Call: KMO(r = podaci_EFA)
Overall MSA =  0.9
MSA for each item = 
               TIMELINE                    RISK                 AVERAGE 
                   0.91                    0.93                    0.82 
               SCHEDULE                CALENDAR                 COURSES 
                   0.88                    0.85                    0.92 
   NOTIFICATIONS_COURSE NOTIFICATIONS_DEADLINES        MESSAGES_TEACHER 
                   0.89                    0.90                    0.87 
         MESSAGES_ADMIN        COMPARISON_EXAMS         COMPARISON_ECTS 
                   0.87                    0.88                    0.88 
      COMPARISON_GRADES  COMPARISON_OBLIGATIONS         STATUS_STUDENTS 
                   0.94                    0.94                    0.92 
      PREDICTION_COURSE         PREDICTION_YEAR         PREDICTION_ECTS 
                   0.91                    0.88                    0.93 
     PREDICTION_PROGRAM           ECTSAVG_STUDY          ECTSAVG_COURSE 
                   0.93                    0.91                    0.90 
        AVGGRADE_COURSE         PASSRATE_COURSE            PPLAN_COURSE 
                   0.93                    0.88                    0.87 
            PPLAN_STUDY        PPLAN_MONITORING         ECTS_MONITORING 
                   0.88                    0.93                    0.96 
  PPLAN_EXTRACURRICULAR      TEACHER_EVALUATION         TEACHER_PROFILE 
                   0.85                    0.85                    0.85 
      TEACHERS_CONTEVAL       TEACHERS_FEEDBACK       BADGES_COMPARISON 
                   0.78                    0.81                    0.89 
      BADGES_COLLECTING                  AWARDS            COMPETITIONS 
                   0.88                    0.90                    0.89 
        ASSESSMENT_CONT       COMPARISON_OTHERS 
                   0.94                    0.94

EFA - broj faktora

Column

Svojstvene vrijednosti matrice korelacija

 [1] 12.42373903  4.06091892  2.21268595  2.13544055  1.74727571  1.48503112
 [7]  1.15525942  1.06474573  0.93647312  0.88758821  0.82955348  0.74827467
[13]  0.65657864  0.61871765  0.56225809  0.50760458  0.50172114  0.47908901
[19]  0.43492714  0.42567976  0.40149080  0.39209593  0.35541637  0.33846030
[25]  0.29807297  0.29539232  0.26437269  0.24493040  0.20883164  0.20824388
[31]  0.19379031  0.17044193  0.15967540  0.14611364  0.13796640  0.12201814
[37]  0.10074791  0.08837706

Column

SCREE plot

EFA model

Column

EFA - 5 faktora

Factor Analysis using method =  minres
Call: fa(r = podaci_EFA, nfactors = 5)
Standardized loadings (pattern matrix) based upon correlation matrix
                          MR2   MR1   MR5   MR3   MR4   h2   u2 com
TIMELINE                 0.02  0.25  0.35  0.08 -0.08 0.29 0.71 2.1
RISK                     0.03  0.28  0.47  0.02 -0.09 0.42 0.58 1.8
AVERAGE                  0.33  0.11  0.23 -0.11  0.04 0.24 0.76 2.4
SCHEDULE                 0.03  0.44  0.17 -0.20  0.16 0.33 0.67 2.1
CALENDAR                 0.06  0.47  0.19 -0.27  0.12 0.36 0.64 2.2
COURSES                  0.16  0.44  0.18 -0.12  0.05 0.36 0.64 1.8
NOTIFICATIONS_COURSE     0.06  0.58 -0.01 -0.02  0.10 0.38 0.62 1.1
NOTIFICATIONS_DEADLINES  0.02  0.64  0.09 -0.08  0.06 0.47 0.53 1.1
MESSAGES_TEACHER         0.02  0.50  0.04 -0.04  0.15 0.33 0.67 1.2
MESSAGES_ADMIN          -0.07  0.53 -0.02  0.09  0.06 0.31 0.69 1.1
COMPARISON_EXAMS         0.81  0.00  0.01  0.03 -0.05 0.67 0.33 1.0
COMPARISON_ECTS          0.78  0.08  0.01  0.04 -0.09 0.65 0.35 1.1
COMPARISON_GRADES        0.89  0.02 -0.12  0.01  0.00 0.73 0.27 1.0
COMPARISON_OBLIGATIONS   0.63  0.08  0.09  0.09 -0.05 0.55 0.45 1.1
STATUS_STUDENTS          0.69 -0.12 -0.07  0.15  0.07 0.55 0.45 1.2
PREDICTION_COURSE        0.03  0.03  0.78  0.09  0.00 0.71 0.29 1.0
PREDICTION_YEAR         -0.01 -0.09  0.96  0.00  0.01 0.85 0.15 1.0
PREDICTION_ECTS          0.02  0.15  0.71  0.10 -0.03 0.71 0.29 1.1
PREDICTION_PROGRAM      -0.01  0.07  0.70  0.07  0.02 0.58 0.42 1.0
ECTSAVG_STUDY            0.79  0.07  0.00  0.02 -0.09 0.64 0.36 1.0
ECTSAVG_COURSE           0.77 -0.01  0.03 -0.02  0.07 0.63 0.37 1.0
AVGGRADE_COURSE          0.69 -0.02  0.10 -0.07  0.17 0.57 0.43 1.2
PASSRATE_COURSE          0.58  0.03  0.08 -0.07  0.15 0.42 0.58 1.2
PPLAN_COURSE             0.09  0.81 -0.04  0.07 -0.05 0.69 0.31 1.1
PPLAN_STUDY              0.00  0.84 -0.01  0.04  0.00 0.72 0.28 1.0
PPLAN_MONITORING        -0.04  0.66  0.13  0.18  0.04 0.64 0.36 1.2
ECTS_MONITORING          0.15  0.41  0.34  0.02  0.02 0.54 0.46 2.2
PPLAN_EXTRACURRICULAR    0.01  0.44 -0.06  0.39 -0.03 0.40 0.60 2.0
TEACHER_EVALUATION       0.22 -0.15  0.15 -0.06  0.59 0.46 0.54 1.6
TEACHER_PROFILE          0.12 -0.02  0.05  0.07  0.58 0.43 0.57 1.1
TEACHERS_CONTEVAL       -0.02  0.04 -0.04  0.04  0.75 0.58 0.42 1.0
TEACHERS_FEEDBACK       -0.11  0.16 -0.10  0.14  0.72 0.59 0.41 1.3
BADGES_COMPARISON        0.08  0.07 -0.03  0.77  0.07 0.70 0.30 1.1
BADGES_COLLECTING        0.03  0.01  0.14  0.79  0.02 0.75 0.25 1.1
AWARDS                   0.06 -0.01  0.05  0.84  0.00 0.77 0.23 1.0
COMPETITIONS             0.04  0.03  0.07  0.67  0.11 0.58 0.42 1.1
ASSESSMENT_CONT          0.04  0.29  0.31 -0.10  0.20 0.35 0.65 3.0
COMPARISON_OTHERS        0.50 -0.10  0.18  0.24  0.10 0.56 0.44 1.9

                       MR2  MR1  MR5  MR3  MR4
SS loadings           5.94 5.04 4.09 3.18 2.26
Proportion Var        0.16 0.13 0.11 0.08 0.06
Cumulative Var        0.16 0.29 0.40 0.48 0.54
Proportion Explained  0.29 0.25 0.20 0.16 0.11
Cumulative Proportion 0.29 0.54 0.73 0.89 1.00

 With factor correlations of 
     MR2  MR1  MR5  MR3  MR4
MR2 1.00 0.27 0.40 0.45 0.27
MR1 0.27 1.00 0.47 0.29 0.26
MR5 0.40 0.47 1.00 0.34 0.22
MR3 0.45 0.29 0.34 1.00 0.23
MR4 0.27 0.26 0.22 0.23 1.00

Mean item complexity =  1.4
Test of the hypothesis that 5 factors are sufficient.

The degrees of freedom for the null model are  703  and the objective function was  27.54 with Chi Square of  5713.94
The degrees of freedom for the model are 523  and the objective function was  6.47 

The root mean square of the residuals (RMSR) is  0.05 
The df corrected root mean square of the residuals is  0.05 

The harmonic number of observations is  222 with the empirical chi square  680.3  with prob <  4e-06 
The total number of observations was  222  with Likelihood Chi Square =  1321.02  with prob <  1.4e-70 

Tucker Lewis Index of factoring reliability =  0.782
RMSEA index =  0.083  and the 90 % confidence intervals are  0.078 0.089
BIC =  -1504.58
Fit based upon off diagonal values = 0.98
Measures of factor score adequacy             
                                                   MR2  MR1  MR5  MR3  MR4
Correlation of (regression) scores with factors   0.97 0.95 0.96 0.95 0.90
Multiple R square of scores with factors          0.94 0.91 0.93 0.91 0.82
Minimum correlation of possible factor scores     0.88 0.82 0.86 0.82 0.63

Column

Loadings (cutoff = 0.6)


Loadings:
                        MR2    MR1    MR5    MR3    MR4   
COMPARISON_EXAMS         0.808                            
COMPARISON_ECTS          0.777                            
COMPARISON_GRADES        0.886                            
COMPARISON_OBLIGATIONS   0.635                            
STATUS_STUDENTS          0.689                            
ECTSAVG_STUDY            0.791                            
ECTSAVG_COURSE           0.772                            
AVGGRADE_COURSE          0.687                            
PASSRATE_COURSE                                           
COMPARISON_OTHERS                                         
NOTIFICATIONS_COURSE                                      
NOTIFICATIONS_DEADLINES         0.638                     
MESSAGES_ADMIN                                            
PPLAN_COURSE                    0.810                     
PPLAN_STUDY                     0.840                     
PPLAN_MONITORING                0.655                     
PREDICTION_COURSE                      0.778              
PREDICTION_YEAR                        0.960              
PREDICTION_ECTS                        0.713              
PREDICTION_PROGRAM                     0.702              
BADGES_COMPARISON                             0.770       
BADGES_COLLECTING                             0.787       
AWARDS                                        0.836       
COMPETITIONS                                  0.671       
TEACHER_EVALUATION                                        
TEACHER_PROFILE                                           
TEACHERS_CONTEVAL                                    0.750
TEACHERS_FEEDBACK                                    0.719
TIMELINE                                                  
RISK                                                      
AVERAGE                                                   
SCHEDULE                                                  
CALENDAR                                                  
COURSES                                                   
MESSAGES_TEACHER                                          
ECTS_MONITORING                                           
PPLAN_EXTRACURRICULAR                                     
ASSESSMENT_CONT                                           

                 MR2   MR1   MR5   MR3   MR4
SS loadings    5.465 4.415 3.402 2.877 2.012
Proportion Var 0.144 0.116 0.090 0.076 0.053
Cumulative Var 0.144 0.260 0.350 0.425 0.478

Loadings

Column

Interaktivna tablica

graf

Scores

Column

Funkcija gustoće

Column

tablica

                MR2          MR1         MR5          MR3           MR4
  [1,]  1.276942115 -0.652156420  0.15947762  1.293587488  1.1714944756
  [2,]  0.541675806  0.292622433 -1.03300778 -0.059374377  0.2924238482
  [3,]  0.818316695  0.462787217  0.37923482  0.169163264  0.2563434922
  [4,] -0.035639914 -0.151339899  0.38209528 -1.187463552 -0.5372153641
  [5,]  0.920457258  0.955558891  0.33897911  1.515903370  1.3726656487
  [6,] -1.084974188  0.621897014  0.23103600  0.901036981  0.1423741804
  [7,]  0.775903235  0.131267302  1.09342977 -0.045664666  0.5669743109
  [8,]  1.736977607  0.981885016  1.03630149  1.549089142  0.0001734065
  [9,] -0.734461423 -3.232468974 -1.71206107  0.788761289 -0.9617110028
 [10,] -2.286971365 -0.669076503 -3.00421271 -1.143497147 -0.1592081637
 [11,] -0.885066136  0.970924252 -0.02186641 -1.106095694  0.6871750336
 [12,]  0.324019589 -0.174085071  1.04249511 -0.160555809 -0.1265282411
 [13,]  1.801938837 -0.010908550  0.87661412  0.291988375  0.8769503703
 [14,]  0.241546828 -1.242406470 -0.66681114 -0.808155897  1.7292142065
 [15,] -0.264759068  1.217569911  0.81758469  1.162210051  0.8310247432
 [16,] -0.405443507 -0.827938047 -0.52999747 -0.008609076 -0.9193804030
 [17,]  0.174332244  0.685088733  1.08272854  1.184975510 -0.0871423130
 [18,]  1.036759242 -0.363436803 -0.87828161  0.038301095 -0.0855650059
 [19,]  0.854101432  0.736155172  0.81083367  1.246190073  0.3927529906
 [20,]  1.131002711  0.349986285  0.97429595 -0.266727326  0.2859555217
 [21,] -1.488022161  0.027268130 -0.29217129 -0.222011037  0.6867430318
 [22,]  0.105573226 -0.194743280  0.02602096 -1.512421282 -0.1284113026
 [23,]  0.132499204  0.082080289  0.90378607  0.191486826 -0.3224647335
 [24,]  1.014681270  0.563300482  0.64055421 -0.636073231  0.2618281444
 [25,]  0.669424730  1.024860430  0.97748821  0.923861842  0.3003796867
 [26,] -1.240285146  0.876558782  0.38451177  0.007582975 -0.3962606794
 [27,]  0.063346548  0.989157301  0.81647112 -0.203124716 -0.9350240391
 [28,] -0.771678907 -0.341696603  0.68629334  0.726501243 -1.3759380593
 [29,] -0.403693562  1.154766031  0.42223782  0.917513618  1.3333694725
 [30,] -0.484220948  0.756550081  0.92668247  0.312296122  1.3611032878
 [31,]  0.164706195  0.937950832 -0.27271932  1.648003850 -0.5596817022
 [32,] -0.599837117  0.061796701  0.37865761 -1.354250344 -1.3024637276
 [33,] -0.506970443 -0.066387339  0.89170953 -1.573366785  0.3080860983
 [34,] -0.150211243 -0.062480865  0.11591030 -0.569452597  0.3991520224
 [35,]  1.681797788  1.088884141  1.05711635  1.720121993  1.4203707296
 [36,] -0.670447083  0.533749167 -1.54091455  0.412935739  0.2990893096
 [37,]  0.245563620  0.406445356  0.72628453  1.024932087 -0.5739253836
 [38,] -1.545085773  0.410151332  0.61015522  1.428131219 -0.3645223400
 [39,] -1.111323309  0.515142184 -0.03828415 -0.326042877 -0.5419293543
 [40,]  0.822902738  0.503482832  0.74334834 -1.459347421  1.3293621190
 [41,]  1.320828139  0.742917504 -0.28004909 -0.583384224  0.9077796433
 [42,] -0.330160883  0.172600851 -1.72023078 -1.212415264 -0.5607794159
 [43,]  1.068025105 -0.571624589  0.67595255  0.825872128 -0.6467452647
 [44,] -0.323132239 -0.023250266  1.07336325 -0.484132096  1.6434180659
 [45,] -0.761297407 -0.465084634 -0.21024681 -0.733911090  0.0202974505
 [46,] -0.388887615  0.874415924  0.10863535 -0.276629945  0.5013340355
 [47,]  0.091431066  0.186784589  0.83100703 -0.082563055 -0.2432547342
 [48,] -1.064837427  0.970808177  0.88238334 -0.517892072  0.0309000999
 [49,]  0.895564353  1.125501311 -0.95755536  0.961456951  0.9954093837
 [50,] -1.643787551 -0.053992677 -0.87489713 -1.128943340 -0.9262173867
 [51,] -0.800808212  0.927218076  0.85986934 -1.194381234  0.9712870006
 [52,]  0.027114139 -0.006168557  0.16964093  0.421867103  0.8951312313
 [53,]  0.798498166 -0.034990079 -0.43488701  0.863581749  0.1196515018
 [54,]  1.716557870  0.195076900  0.77595241  1.189170264  0.4157316390
 [55,] -0.092257806  0.654461794  0.19047776  1.114639880  1.4037557211
 [56,]  0.078584329  0.961246930  0.11684504 -0.266439664 -0.2190010002
 [57,]  0.286972613  0.749201383 -1.92774409  0.634656431 -0.0738314426
 [58,] -0.900667104 -0.198447181 -0.24441485  1.005690350 -0.1133818969
 [59,]  1.376245599 -0.053279363  0.52909849  1.095649945 -0.2777306213
 [60,]  0.059658200  0.655291474  1.02041815  0.740542728  0.1342132847
 [61,]  0.472477676 -0.573913103 -0.33418005  0.769395638 -0.1734837525
 [62,] -0.637505486  1.206195271 -0.34884201 -0.198521709 -2.3016218346
 [63,]  1.561304374  0.156325744  0.51559142  0.395579639  0.4951071286
 [64,]  1.604088871 -0.003499767  0.94267951 -0.518346527 -1.4500150291
 [65,] -0.495208393  0.103232844  0.03812812  0.520944103 -0.1645238998
 [66,]  0.121974568 -0.696924098  0.16705507 -0.247301835 -0.2437313394
 [67,]  1.653142028  0.019594873 -0.32405249 -1.485142407 -1.5963535330
 [68,]  1.038722868  1.155826398  0.43202066  0.888027920  0.7730027600
 [69,] -0.784721134  0.818016941 -0.59260502 -0.275747993  0.9859118077
 [70,] -0.838152624 -0.202916949 -0.24032278 -0.234525323 -0.1955172369
 [71,]  0.224210509  0.370955349  0.10511116  0.662596418 -0.0119003463
 [72,]  0.575685673  0.474419880  0.88682840  1.546599580 -0.5705606099
 [73,]  0.068579662  0.299884858  0.22430992  0.190179670 -0.0726178346
 [74,]  0.049888223  0.418683389  0.56031507 -0.312785000 -1.0957125512
 [75,] -0.510458896  0.238849131  0.19903876  0.006696932 -0.2673003579
 [76,]  0.183482623 -1.215082781 -0.18552716  1.225421340 -0.6325927708
 [77,]  0.876873802  0.442265823 -0.16659407 -0.867562474  0.6497650135
 [78,]  1.905000710  0.904998654  1.15184788  1.477017056 -0.0520549665
 [79,]  0.341441709  0.561450389  0.75245607  1.266730436  0.8014901594
 [80,]  0.660866676  0.648556654  0.37869287  1.237366225  0.5445470081
 [81,] -1.305327802  0.638732360  0.97147907  0.147710967 -0.2471279891
 [82,]  0.092455228 -0.345889282 -0.23345438 -1.023300084  0.6651390449
 [83,]  0.544616149 -0.945577701 -0.23650280  0.726225973 -0.5968314322
 [84,]  0.287419238  0.834267072  0.06931054 -1.153208939  0.9260762482
 [85,] -1.347971571 -0.199492535 -0.19463553 -1.593341376 -0.9467182284
 [86,] -1.236572801  0.749980498 -0.20754116  0.221569973 -0.9701169530
 [87,] -0.028243421 -0.789559292 -1.01693068 -1.275288376  1.1762404195
 [88,] -0.632485528 -0.210367014  0.16406644 -1.140244914 -0.0113681065
 [89,]  0.752486268  1.199870679  0.05770567 -0.429570707  0.7651234337
 [90,] -1.598912061 -0.005634223 -0.04065024 -0.362740750 -0.3364529576
 [91,] -1.634477201 -0.207455093 -0.16022136 -0.300164974  0.6376948614
 [92,]  0.089428479  0.321672597  0.20479681 -1.268996024 -1.2475722939
 [93,] -0.933305212  0.158250331  0.50760060 -0.596082074  1.0991015188
 [94,] -0.149168639 -0.443456818 -1.40503783  0.001789760 -0.5130170841
 [95,]  0.704491377 -1.309884990 -0.46650596 -1.334677287  0.9393152698
 [96,]  0.581374438  0.556364983  0.30322452  0.814440861 -0.2331310329
 [97,]  1.820100270  0.425996261  0.52756411  1.316636109  0.7461818509
 [98,]  0.522460619  0.280407111  0.86655490 -0.381106362 -0.5242089383
 [99,]  1.416388401  0.353903668  0.82077789  1.803551214  0.9067757471
[100,] -0.449467134  0.855346813  0.89523823 -1.164035497 -1.8931594142
[101,] -0.331069053 -0.797885383  0.73673036 -0.204152862 -0.3854731350
[102,]  1.674355966  0.682202741  0.44698243 -1.200793827  0.0173374238
[103,]  0.365368360  0.044718659  0.73452195  0.536249952 -0.4296284841
[104,] -1.927506489 -2.506169140 -2.58921765 -1.720319113 -2.1933104732
[105,] -1.997565515 -3.833656089 -2.99355874 -1.050191486 -2.7792118196
[106,] -2.119470683 -3.785575364 -2.99924498 -1.175746430 -2.7664254235
[107,]  1.129840761 -0.544331207  0.12010440  0.886634704 -0.4372594197
[108,]  0.553001455  0.614539884  0.84506116  0.282237619  1.0359631132
[109,]  1.266595680 -0.356188545 -0.05973479 -0.842541016 -0.2554255983
[110,]  1.733303577  0.668774607  0.13910323  0.885018001  1.2307738067
[111,]  0.473023407 -1.184589850 -0.03994689  0.475817258 -0.6143664157
[112,] -0.140845486 -1.391823045 -0.17380105  0.580318487 -0.5254689805
[113,] -2.025986777 -1.648704760 -2.51016497 -1.912216250 -2.1112576657
[114,] -1.592544596 -1.406821352 -2.31409351 -1.630712192 -0.3129058019
[115,]  0.003193568 -0.765696335 -0.37049692 -0.027970104 -0.5115187508
[116,] -0.229455868 -0.930040848  0.78061695  0.884254742  0.4653730271
[117,]  0.617418926 -0.568143197 -0.11031236  0.576956254  0.3354514602
[118,] -0.930851501 -0.490646730 -0.68393890  0.310470616 -0.7854064542
[119,] -0.542886875  0.760881430  0.83715802  1.015334983 -0.1938002504
[120,] -0.816987976 -0.246209486 -1.98020860 -1.784038039 -1.8766214494
[121,]  1.590275210  0.276644215  0.92053316  1.776860082  1.4953838529
[122,] -0.092785171 -1.396955847 -1.17455349  0.164763369 -0.7316540231
[123,]  1.219804354  0.563450615  0.97122773  0.831219344 -0.3338106137
[124,] -0.498161126  0.267816729 -0.71438523 -0.261073585 -0.4919806373
[125,] -0.673824243 -1.900976149 -2.35186130 -1.160298165  0.7883622830
[126,] -0.168562386 -2.903294853  0.12939537 -1.694116785  1.0414985310
[127,]  0.341599481 -0.124541825 -0.14761910  0.246038208 -0.1811090125
[128,] -1.065289109  0.175447260 -0.63655644 -0.944632610  0.8394007678
[129,] -0.341029856  0.991649954  1.14832847  0.325049335  0.0912061356
[130,]  0.715522620  0.164627703  0.12951452  0.509394243  0.5343520602
[131,]  1.212372129  0.772085989 -0.03830480 -0.212368012 -1.4292660822
[132,]  0.840303967  0.580875917  0.22010429  1.366869078  0.3403380413
[133,] -1.347514408 -1.202325063 -1.79688270 -1.371397041 -1.0703330312
[134,]  1.021130464  0.673065398  0.31006830  0.982701920  0.8390450250
[135,] -0.882167681 -0.254054808  0.23821842  0.676781519 -0.2956679534
[136,]  0.800132964  0.206306013  0.32236280  0.345927709 -0.6667241110
[137,]  1.087183929 -0.058776078 -0.69148039 -0.614295372  0.7013245996
[138,]  0.631654035 -0.800925751  0.70833876  0.666119570 -0.3216881686
[139,]  0.755944721  0.790374784 -0.93404779  1.690718934  0.5585641607
[140,]  0.150533369  1.171390988  1.03765777  0.260250521  0.5329160731
[141,] -0.971325053  0.870691558  0.98207384 -1.355883954 -1.9295302619
[142,] -1.228590358 -1.017533656 -0.65884849 -1.424584039  0.2013622362
[143,]  0.762667373 -0.019599167  0.74261999 -0.031925174 -1.0331891517
[144,]  1.231293535  0.469893528 -0.54627455  0.399191106  1.1336928106
[145,]  0.743646775  0.295633759  0.19764324  0.827225216  0.8290414734
[146,]  0.140541975  0.447547490  0.87740945  0.902556704  1.0934876205
[147,]  1.179382369 -1.017181264  0.55016099 -0.568778740  0.2683887390
[148,] -1.432827384  1.118799934  1.11869800 -0.488058788  0.2311496442
[149,] -0.792531949 -1.840133317 -1.31607093 -0.966287166 -0.3977146353
[150,]  0.335194823  0.534295598  0.30158187 -0.039316720  1.2784436399
[151,] -1.444360011  0.664393781  0.26925303 -1.460129513 -1.1999627188
[152,] -0.250809657  0.647430076  0.97961097 -0.101543280  0.1600715077
[153,]  0.433988126  1.012639746  1.01900214 -0.652286487  0.3857771622
[154,]  1.779929931  0.081823587  0.88816060  1.079005182  1.0731270775
[155,] -0.097476270 -0.598906706  0.03769724 -0.023857672  0.1756589459
[156,] -0.286770429  0.988817401  0.84990486  0.673190904 -1.2196796971
[157,] -0.607432371  1.027001394  0.35623871  0.367262219 -0.7678848383
[158,] -0.988022531 -0.156144316 -1.53881111 -1.314043050  1.0796951712
[159,]  0.928268165  1.025160978  1.05570557  1.620555863  0.4214784267
[160,]  0.570297845  1.127545476  0.27773104 -0.239229595  1.2525240082
[161,]  0.483315513 -0.035976340  0.81103077  0.649518771 -1.1000343522
[162,]  0.142646492  1.167693448  0.64918688 -0.039260784 -0.5970621616
[163,] -0.134174481 -0.401216474  0.07153130 -0.715579686  0.2497625446
[164,] -1.135316996 -2.095708784  0.48222592 -1.065524351  0.2763491351
[165,] -0.858189649  1.015122149 -0.01868526 -0.786235583  0.8034530379
[166,] -1.079892404 -0.733862086 -1.64605371 -0.466690712 -0.1087055294
[167,]  0.925951326  0.781993352  0.17899796 -0.042334615 -0.5137469367
[168,] -0.696322918 -1.098143842  0.72905059 -1.624939549 -0.7334007704
[169,]  0.134582259 -0.669981326 -1.48282571 -1.284676414 -1.0856116188
[170,] -0.787115850  1.085645300  1.00061716 -1.506165356  0.1912093789
[171,] -1.720922722 -0.503649281  0.28733170 -1.269012083  0.3030488449
[172,] -0.856599553  1.216904840  0.36693778  1.478594962  1.1339136227
[173,] -0.711357619 -0.545721123 -0.20601329  0.506525630 -0.4347541982
[174,] -0.242311340 -0.104710832  0.75513819  0.033464696 -0.0076459537
[175,]  0.039804576 -2.600053882 -2.40849665  1.532191583  1.5072742837
[176,] -0.517377801 -1.000665503 -0.69877433 -1.619280669 -1.4983240095
[177,]  1.081094520  0.669003126  0.59566121  0.943523552 -0.0202816593
[178,]  0.204219653  0.389763235 -0.06435586  0.953358640  0.3363948671
[179,]  0.214765364  0.757589343 -1.49567630 -1.156357474  0.2249201887
[180,]  0.284669132 -0.608005663 -0.34332452  0.392236567 -0.3577144897
[181,]  0.520984619 -0.201915888  0.93396051  0.352609956 -1.7749497505
[182,] -0.400162302 -1.077589926  0.67334722 -0.003960515  1.2397448829
[183,]  0.789261008  0.887149509  0.49159313  0.867508459 -0.2938527665
[184,]  1.839425302  0.823455753  0.99496315  1.527335606  1.3606584945
[185,]  0.659839089 -1.202018838 -0.75104652 -0.383889186 -1.7257254535
[186,] -0.746793277 -3.180518433  0.07131390 -1.507574144 -0.3859390006
[187,] -1.812773962 -0.798363604 -0.41802189 -0.910766662  0.3408099531
[188,]  1.843572775  1.119432513  1.05186297  1.719614172  1.3585562977
[189,]  0.198096114 -0.940938024 -0.35883381  0.592816783 -0.1246709533
[190,] -0.581936581 -0.418525871 -2.05832772 -1.435079844 -0.8908555136
[191,]  1.008011631 -0.390421364  1.05393285  0.282986131  0.8139929250
[192,] -0.291929742  0.715449463  0.95763382 -0.920218372 -0.0677139899
[193,] -1.264935701  0.771370438 -1.04167524  1.686623298 -0.3752991535
[194,]  1.126474429  0.653079810  0.26277521  0.769514045  0.8821341845
[195,]  1.597274700  0.438184957  0.84225696  1.234504807  1.1596359651
[196,] -1.221084372 -1.147563548 -1.27045980 -1.539073945 -1.1846527870
[197,]  0.526093460  0.396755161  0.08888298  0.111270900  1.1701031945
[198,]  0.300701213  0.813262324 -2.43077706 -0.876822340  1.4328226593
[199,] -0.108199312  0.119677854  0.07857256 -0.677262756 -1.3840404902
[200,]  1.064162443 -1.190930856 -0.72872840  0.355079882 -0.9789079249
[201,]  1.186511261  1.090457087  0.87470671  0.536268791  1.0685160268
[202,] -0.887981491 -1.497492136 -1.09446462  0.195949533 -1.5716260274
[203,] -2.157749239  0.771640265 -2.33385566 -1.255838971 -0.8448733129
[204,]  0.480279127  1.225742953  0.16971699  0.878082952  0.5386646404
[205,] -0.290643381 -0.157648476  0.47252869  0.481163243 -0.0526994083
[206,] -1.025234990  0.858653606  0.76680509 -0.122273809  0.8403411155
[207,] -1.351605194  0.245012591  0.09167721 -0.513721570 -0.4770502797
[208,]  0.866620951 -1.026565441 -1.50194025  0.476557348 -0.0747216196
[209,]  0.543623374 -0.723197859  0.81486873  0.208233574 -0.1350297056
[210,] -0.048407158 -0.628678863  1.22270655  0.405356865  1.9663647838
[211,] -1.507867664 -0.963553683 -0.74828844 -0.997498780  0.0057472685
[212,] -1.019397904  0.133980739  0.13089963 -0.899594414  0.7276846025
[213,] -0.951809190  0.557802701  0.26998232  0.510435456  0.3416382607
[214,]  0.127915904  0.579420712  0.95736257 -0.684213097  0.3855744158
[215,] -0.128399813  0.205654667 -0.08178721  0.488392117 -0.2442296319
[216,] -0.273147936  0.569201579  0.61785396  1.205512942  0.9870222498
[217,] -0.748927783  0.103725937  0.52125178 -0.764760167 -0.9906402909
[218,]  0.826860800 -0.058518191  0.05122342  1.080689148  0.3355392679
[219,]  0.314861765 -0.409053687  0.76372952  1.387888654 -2.0801592538
[220,] -2.036746266 -2.913531338 -2.72908474 -1.504300607  0.1826420533
[221,]  0.428251010 -0.648081302  0.02932462 -0.784613045 -0.5120523202
[222,] -1.473677765  0.639436030 -2.89546470 -0.399041183  0.6101550122

Summary

Column

Model (cutoff=0.6)

# Latent variables
MR2 =~ COMPARISON_EXAMS + COMPARISON_ECTS + COMPARISON_GRADES + COMPARISON_OBLIGATIONS + STATUS_STUDENTS + ECTSAVG_STUDY + ECTSAVG_COURSE + AVGGRADE_COURSE
MR1 =~ NOTIFICATIONS_DEADLINES + PPLAN_COURSE + PPLAN_STUDY + PPLAN_MONITORING
MR5 =~ PREDICTION_COURSE + PREDICTION_YEAR + PREDICTION_ECTS + PREDICTION_PROGRAM
MR3 =~ BADGES_COMPARISON + BADGES_COLLECTING + AWARDS + COMPETITIONS
MR4 =~ TEACHERS_CONTEVAL + TEACHERS_FEEDBACK

Summary (cutoff=0.6)

lavaan 0.6-11 ended normally after 57 iterations

  Estimator                                         ML
  Optimization method                           NLMINB
  Number of model parameters                        54
                                                      
  Number of observations                           196
                                                      
Model Test User Model:
                                                      
  Test statistic                               412.245
  Degrees of freedom                               199
  P-value (Chi-square)                           0.000

Model Test Baseline Model:

  Test statistic                              3433.352
  Degrees of freedom                               231
  P-value                                        0.000

User Model versus Baseline Model:

  Comparative Fit Index (CFI)                    0.933
  Tucker-Lewis Index (TLI)                       0.923

Loglikelihood and Information Criteria:

  Loglikelihood user model (H0)              -5308.295
  Loglikelihood unrestricted model (H1)      -5102.172
                                                      
  Akaike (AIC)                               10724.590
  Bayesian (BIC)                             10901.608
  Sample-size adjusted Bayesian (BIC)        10730.542

Root Mean Square Error of Approximation:

  RMSEA                                          0.074
  90 Percent confidence interval - lower         0.064
  90 Percent confidence interval - upper         0.084
  P-value RMSEA <= 0.05                          0.000

Standardized Root Mean Square Residual:

  SRMR                                           0.057

Parameter Estimates:

  Standard errors                             Standard
  Information                                 Expected
  Information saturated (h1) model          Structured

Latent Variables:
                   Estimate  Std.Err  z-value  P(>|z|)
  MR2 =~                                              
    COMPARISON_EXA    1.000                           
    COMPARISON_ECT    0.989    0.053   18.619    0.000
    COMPARISON_GRA    1.040    0.052   19.975    0.000
    COMPARISON_OBL    0.911    0.057   15.949    0.000
    STATUS_STUDENT    0.879    0.065   13.556    0.000
    ECTSAVG_STUDY     0.848    0.054   15.630    0.000
    ECTSAVG_COURSE    0.920    0.052   17.818    0.000
    AVGGRADE_COURS    0.613    0.056   10.915    0.000
  MR1 =~                                              
    NOTIFICATIONS_    1.000                           
    PPLAN_COURSE      2.443    0.437    5.592    0.000
    PPLAN_STUDY       2.283    0.413    5.522    0.000
    PPLAN_MONITORI    2.447    0.434    5.639    0.000
  MR5 =~                                              
    PREDICTION_COU    1.000                           
    PREDICTION_YEA    1.096    0.056   19.481    0.000
    PREDICTION_ECT    0.900    0.055   16.245    0.000
    PREDICTION_PRO    0.527    0.066    8.032    0.000
  MR3 =~                                              
    BADGES_COMPARI    1.000                           
    BADGES_COLLECT    1.085    0.074   14.712    0.000
    AWARDS            0.936    0.073   12.779    0.000
    COMPETITIONS      0.806    0.079   10.160    0.000
  MR4 =~                                              
    TEACHERS_CONTE    1.000                           
    TEACHERS_FEEDB    0.880    0.158    5.560    0.000

Covariances:
                   Estimate  Std.Err  z-value  P(>|z|)
  MR2 ~~                                              
    MR1               0.096    0.037    2.585    0.010
    MR5               0.543    0.106    5.118    0.000
    MR3               0.745    0.120    6.222    0.000
    MR4               0.304    0.097    3.123    0.002
  MR1 ~~                                              
    MR5               0.205    0.048    4.227    0.000
    MR3               0.158    0.043    3.697    0.000
    MR4               0.097    0.034    2.849    0.004
  MR5 ~~                                              
    MR3               0.461    0.098    4.701    0.000
    MR4               0.216    0.086    2.518    0.012
  MR3 ~~                                              
    MR4               0.360    0.094    3.812    0.000

Variances:
                   Estimate  Std.Err  z-value  P(>|z|)
   .COMPARISON_EXA    0.394    0.047    8.349    0.000
   .COMPARISON_ECT    0.352    0.043    8.202    0.000
   .COMPARISON_GRA    0.279    0.037    7.522    0.000
   .COMPARISON_OBL    0.532    0.059    8.951    0.000
   .STATUS_STUDENT    0.806    0.087    9.320    0.000
   .ECTSAVG_STUDY     0.492    0.055    9.012    0.000
   .ECTSAVG_COURSE    0.365    0.043    8.485    0.000
   .AVGGRADE_COURS    0.687    0.072    9.570    0.000
   .NOTIFICATIONS_    0.676    0.069    9.724    0.000
   .PPLAN_COURSE      0.249    0.037    6.674    0.000
   .PPLAN_STUDY       0.321    0.041    7.821    0.000
   .PPLAN_MONITORI    0.157    0.031    5.020    0.000
   .PREDICTION_COU    0.327    0.045    7.294    0.000
   .PREDICTION_YEA    0.153    0.038    4.016    0.000
   .PREDICTION_ECT    0.342    0.043    7.981    0.000
   .PREDICTION_PRO    0.785    0.081    9.647    0.000
   .BADGES_COMPARI    0.457    0.066    6.923    0.000
   .BADGES_COLLECT    0.407    0.068    6.016    0.000
   .AWARDS            0.618    0.077    8.019    0.000
   .COMPETITIONS      0.939    0.104    9.007    0.000
   .TEACHERS_CONTE    0.296    0.162    1.827    0.068
   .TEACHERS_FEEDB    0.420    0.130    3.225    0.001
    MR2               1.418    0.180    7.858    0.000
    MR1               0.126    0.046    2.774    0.006
    MR5               1.112    0.145    7.681    0.000
    MR3               1.162    0.164    7.095    0.000
    MR4               0.939    0.200    4.696    0.000

Sve mjere testa (cutoff=0.6)

               npar                fmin               chisq                  df 
             54.000               1.052             412.245             199.000 
             pvalue      baseline.chisq         baseline.df     baseline.pvalue 
              0.000            3433.352             231.000               0.000 
                cfi                 tli                nnfi                 rfi 
              0.933               0.923               0.923               0.861 
                nfi                pnfi                 ifi                 rni 
              0.880               0.758               0.934               0.933 
               logl   unrestricted.logl                 aic                 bic 
          -5308.295           -5102.172           10724.590           10901.608 
             ntotal                bic2               rmsea      rmsea.ci.lower 
            196.000           10730.542               0.074               0.064 
     rmsea.ci.upper        rmsea.pvalue                 rmr          rmr_nomean 
              0.084               0.000               0.079               0.079 
               srmr        srmr_bentler srmr_bentler_nomean                crmr 
              0.057               0.057               0.057               0.060 
        crmr_nomean          srmr_mplus   srmr_mplus_nomean               cn_05 
              0.060               0.057               0.057             111.737 
              cn_01                 gfi                agfi                pgfi 
            119.067               0.837               0.792               0.658 
                mfi                ecvi 
              0.580               2.654

Parametri

Parametri (cutoff=0.6)

Parametri (cutoff=0.4)
lhs	op	rhs	est	se	z	pvalue	ci.lower	ci.upper
MR2	=~	COMPARISON_EXAMS	1.0000000	0.0000000	NA	NA	1.0000000	1.0000000
MR2	=~	COMPARISON_ECTS	0.9892749	0.0531321	18.619140	0.0000000	0.8851378	1.0934120
MR2	=~	COMPARISON_GRADES	1.0402045	0.0520741	19.975478	0.0000000	0.9381412	1.1422678
MR2	=~	COMPARISON_OBLIGATIONS	0.9114090	0.0571437	15.949414	0.0000000	0.7994093	1.0234086
MR2	=~	STATUS_STUDENTS	0.8790583	0.0648468	13.555930	0.0000000	0.7519609	1.0061556
MR2	=~	ECTSAVG_STUDY	0.8483134	0.0542744	15.630082	0.0000000	0.7419375	0.9546892
MR2	=~	ECTSAVG_COURSE	0.9199076	0.0516271	17.818324	0.0000000	0.8187204	1.0210948
MR2	=~	AVGGRADE_COURSE	0.6125013	0.0561173	10.914654	0.0000000	0.5025134	0.7224893
MR1	=~	NOTIFICATIONS_DEADLINES	1.0000000	0.0000000	NA	NA	1.0000000	1.0000000
MR1	=~	PPLAN_COURSE	2.4428552	0.4368260	5.592284	0.0000000	1.5866920	3.2990184
MR1	=~	PPLAN_STUDY	2.2828155	0.4134059	5.521972	0.0000000	1.4725549	3.0930761
MR1	=~	PPLAN_MONITORING	2.4469093	0.4339316	5.638929	0.0000000	1.5964191	3.2973995
MR5	=~	PREDICTION_COURSE	1.0000000	0.0000000	NA	NA	1.0000000	1.0000000
MR5	=~	PREDICTION_YEAR	1.0960954	0.0562636	19.481444	0.0000000	0.9858208	1.2063699
MR5	=~	PREDICTION_ECTS	0.9004173	0.0554275	16.244954	0.0000000	0.7917814	1.0090532
MR5	=~	PREDICTION_PROGRAM	0.5268523	0.0655963	8.031733	0.0000000	0.3982858	0.6554188
MR3	=~	BADGES_COMPARISON	1.0000000	0.0000000	NA	NA	1.0000000	1.0000000
MR3	=~	BADGES_COLLECTING	1.0854225	0.0737758	14.712454	0.0000000	0.9408246	1.2300203
MR3	=~	AWARDS	0.9362455	0.0732657	12.778771	0.0000000	0.7926474	1.0798437
MR3	=~	COMPETITIONS	0.8057023	0.0793033	10.159752	0.0000000	0.6502706	0.9611340
MR4	=~	TEACHERS_CONTEVAL	1.0000000	0.0000000	NA	NA	1.0000000	1.0000000
MR4	=~	TEACHERS_FEEDBACK	0.8795207	0.1581887	5.559946	0.0000000	0.5694765	1.1895648
COMPARISON_EXAMS	~~	COMPARISON_EXAMS	0.3938320	0.0471709	8.349045	0.0000000	0.3013787	0.4862852
COMPARISON_ECTS	~~	COMPARISON_ECTS	0.3523383	0.0429601	8.201534	0.0000000	0.2681382	0.4365385
COMPARISON_GRADES	~~	COMPARISON_GRADES	0.2785856	0.0370373	7.521766	0.0000000	0.2059939	0.3511773
COMPARISON_OBLIGATIONS	~~	COMPARISON_OBLIGATIONS	0.5318568	0.0594175	8.951188	0.0000000	0.4154007	0.6483128
STATUS_STUDENTS	~~	STATUS_STUDENTS	0.8062633	0.0865112	9.319757	0.0000000	0.6367045	0.9758221
ECTSAVG_STUDY	~~	ECTSAVG_STUDY	0.4919518	0.0545894	9.011851	0.0000000	0.3849585	0.5989452
ECTSAVG_COURSE	~~	ECTSAVG_COURSE	0.3649304	0.0430080	8.485183	0.0000000	0.2806363	0.4492244
AVGGRADE_COURSE	~~	AVGGRADE_COURSE	0.6866915	0.0717555	9.569874	0.0000000	0.5460532	0.8273297
NOTIFICATIONS_DEADLINES	~~	NOTIFICATIONS_DEADLINES	0.6758187	0.0694994	9.724089	0.0000000	0.5396023	0.8120351
PPLAN_COURSE	~~	PPLAN_COURSE	0.2489639	0.0373063	6.673516	0.0000000	0.1758449	0.3220828
PPLAN_STUDY	~~	PPLAN_STUDY	0.3205517	0.0409873	7.820760	0.0000000	0.2402181	0.4008853
PPLAN_MONITORING	~~	PPLAN_MONITORING	0.1571198	0.0312980	5.020118	0.0000005	0.0957768	0.2184628
PREDICTION_COURSE	~~	PREDICTION_COURSE	0.3271403	0.0448525	7.293687	0.0000000	0.2392309	0.4150496
PREDICTION_YEAR	~~	PREDICTION_YEAR	0.1529889	0.0380940	4.016087	0.0000592	0.0783260	0.2276519
PREDICTION_ECTS	~~	PREDICTION_ECTS	0.3423137	0.0428917	7.980887	0.0000000	0.2582475	0.4263799
PREDICTION_PROGRAM	~~	PREDICTION_PROGRAM	0.7846173	0.0813292	9.647426	0.0000000	0.6252150	0.9440196
BADGES_COMPARISON	~~	BADGES_COMPARISON	0.4565987	0.0659499	6.923418	0.0000000	0.3273393	0.5858582
BADGES_COLLECTING	~~	BADGES_COLLECTING	0.4066095	0.0675935	6.015511	0.0000000	0.2741287	0.5390904
AWARDS	~~	AWARDS	0.6175570	0.0770095	8.019231	0.0000000	0.4666212	0.7684929
COMPETITIONS	~~	COMPETITIONS	0.9394415	0.1043053	9.006651	0.0000000	0.7350069	1.1438762
TEACHERS_CONTEVAL	~~	TEACHERS_CONTEVAL	0.2958784	0.1619745	1.826697	0.0677453	-0.0215858	0.6133427
TEACHERS_FEEDBACK	~~	TEACHERS_FEEDBACK	0.4200531	0.1302502	3.224971	0.0012599	0.1647674	0.6753388
MR2	~~	MR2	1.4177052	0.1804054	7.858441	0.0000000	1.0641171	1.7712933
MR1	~~	MR1	0.1264248	0.0455678	2.774433	0.0055298	0.0371136	0.2157361
MR5	~~	MR5	1.1123376	0.1448154	7.681074	0.0000000	0.8285047	1.3961706
MR3	~~	MR3	1.1621801	0.1638105	7.094662	0.0000000	0.8411174	1.4832428
MR4	~~	MR4	0.9393371	0.2000452	4.695624	0.0000027	0.5472557	1.3314185
MR2	~~	MR1	0.0959888	0.0371390	2.584586	0.0097496	0.0231978	0.1687799
MR2	~~	MR5	0.5425868	0.1060256	5.117508	0.0000003	0.3347804	0.7503931
MR2	~~	MR3	0.7452284	0.1197645	6.222448	0.0000000	0.5104943	0.9799625
MR2	~~	MR4	0.3044009	0.0974797	3.122711	0.0017919	0.1133442	0.4954575
MR1	~~	MR5	0.2049862	0.0484917	4.227238	0.0000237	0.1099441	0.3000283
MR1	~~	MR3	0.1581963	0.0427946	3.696641	0.0002185	0.0743204	0.2420721
MR1	~~	MR4	0.0973387	0.0341648	2.849090	0.0043844	0.0303769	0.1643006
MR5	~~	MR3	0.4613384	0.0981404	4.700799	0.0000026	0.2689867	0.6536901
MR5	~~	MR4	0.2155749	0.0856177	2.517878	0.0118064	0.0477673	0.3833826
MR3	~~	MR4	0.3600077	0.0944395	3.812046	0.0001378	0.1749096	0.5451057

Normalizirani parametri

Normalizirani parametri (cutoff=0.6)

Normalizirani parametri (cutoff=0.4)
lhs	op	rhs	est.std	se	z	pvalue	ci.lower	ci.upper
MR2	=~	COMPARISON_EXAMS	0.8846456	0.0174263	50.765056	0.0000000	0.8504908	0.9188005
MR2	=~	COMPARISON_ECTS	0.8930191	0.0164072	54.428501	0.0000000	0.8608615	0.9251766
MR2	=~	COMPARISON_GRADES	0.9199480	0.0131398	70.012112	0.0000000	0.8941944	0.9457016
MR2	=~	COMPARISON_OBLIGATIONS	0.8299886	0.0239546	34.648373	0.0000000	0.7830384	0.8769388
MR2	=~	STATUS_STUDENTS	0.7589791	0.0319236	23.774871	0.0000000	0.6964101	0.8215482
MR2	=~	ECTSAVG_STUDY	0.8213856	0.0249539	32.916064	0.0000000	0.7724768	0.8702945
MR2	=~	ECTSAVG_COURSE	0.8756502	0.0185178	47.286886	0.0000000	0.8393559	0.9119445
MR2	=~	AVGGRADE_COURSE	0.6606592	0.0418365	15.791456	0.0000000	0.5786612	0.7426572
MR1	=~	NOTIFICATIONS_DEADLINES	0.3969749	0.0635521	6.246446	0.0000000	0.2724151	0.5215348
MR1	=~	PPLAN_COURSE	0.8671113	0.0237664	36.484796	0.0000000	0.8205301	0.9136926
MR1	=~	PPLAN_STUDY	0.8201830	0.0281285	29.158442	0.0000000	0.7650522	0.8753139
MR1	=~	PPLAN_MONITORING	0.9100055	0.0203387	44.742451	0.0000000	0.8701422	0.9498687
MR5	=~	PREDICTION_COURSE	0.8790545	0.0201770	43.567242	0.0000000	0.8395084	0.9186006
MR5	=~	PREDICTION_YEAR	0.9472487	0.0143564	65.981063	0.0000000	0.9191107	0.9753867
MR5	=~	PREDICTION_ECTS	0.8513870	0.0230158	36.991392	0.0000000	0.8062768	0.8964972
MR5	=~	PREDICTION_PROGRAM	0.5314019	0.0536065	9.913009	0.0000000	0.4263350	0.6364687
MR3	=~	BADGES_COMPARISON	0.8473112	0.0264800	31.998099	0.0000000	0.7954113	0.8992112
MR3	=~	BADGES_COLLECTING	0.8780833	0.0237442	36.980889	0.0000000	0.8315455	0.9246212
MR3	=~	AWARDS	0.7890393	0.0321332	24.555294	0.0000000	0.7260595	0.8520192
MR3	=~	COMPETITIONS	0.6673755	0.0439995	15.167803	0.0000000	0.5811381	0.7536129
MR4	=~	TEACHERS_CONTEVAL	0.8720459	0.0758495	11.497063	0.0000000	0.7233837	1.0207081
MR4	=~	TEACHERS_FEEDBACK	0.7960402	0.0723511	11.002455	0.0000000	0.6542346	0.9378458
COMPARISON_EXAMS	~~	COMPARISON_EXAMS	0.2174021	0.0308322	7.051149	0.0000000	0.1569722	0.2778320
COMPARISON_ECTS	~~	COMPARISON_ECTS	0.2025170	0.0293039	6.910928	0.0000000	0.1450824	0.2599515
COMPARISON_GRADES	~~	COMPARISON_GRADES	0.1536957	0.0241759	6.357384	0.0000000	0.1063118	0.2010797
COMPARISON_OBLIGATIONS	~~	COMPARISON_OBLIGATIONS	0.3111189	0.0397641	7.824111	0.0000000	0.2331827	0.3890552
STATUS_STUDENTS	~~	STATUS_STUDENTS	0.4239507	0.0484587	8.748706	0.0000000	0.3289734	0.5189279
ECTSAVG_STUDY	~~	ECTSAVG_STUDY	0.3253256	0.0409936	7.936005	0.0000000	0.2449796	0.4056716
ECTSAVG_COURSE	~~	ECTSAVG_COURSE	0.2332367	0.0324303	7.191945	0.0000000	0.1696746	0.2967989
AVGGRADE_COURSE	~~	AVGGRADE_COURSE	0.5635294	0.0552793	10.194215	0.0000000	0.4551839	0.6718749
NOTIFICATIONS_DEADLINES	~~	NOTIFICATIONS_DEADLINES	0.8424109	0.0504572	16.695554	0.0000000	0.7435166	0.9413052
PPLAN_COURSE	~~	PPLAN_COURSE	0.2481180	0.0412162	6.019916	0.0000000	0.1673357	0.3289002
PPLAN_STUDY	~~	PPLAN_STUDY	0.3272998	0.0461410	7.093465	0.0000000	0.2368650	0.4177345
PPLAN_MONITORING	~~	PPLAN_MONITORING	0.1718901	0.0370167	4.643576	0.0000034	0.0993386	0.2444416
PREDICTION_COURSE	~~	PREDICTION_COURSE	0.2272631	0.0354733	6.406598	0.0000000	0.1577368	0.2967895
PREDICTION_YEAR	~~	PREDICTION_YEAR	0.1027199	0.0271981	3.776731	0.0001589	0.0494126	0.1560272
PREDICTION_ECTS	~~	PREDICTION_ECTS	0.2751402	0.0391907	7.020543	0.0000000	0.1983278	0.3519526
PREDICTION_PROGRAM	~~	PREDICTION_PROGRAM	0.7176120	0.0569732	12.595604	0.0000000	0.6059466	0.8292775
BADGES_COMPARISON	~~	BADGES_COMPARISON	0.2820637	0.0448737	6.285726	0.0000000	0.1941129	0.3700145
BADGES_COLLECTING	~~	BADGES_COLLECTING	0.2289697	0.0416989	5.491030	0.0000000	0.1472414	0.3106979
AWARDS	~~	AWARDS	0.3774169	0.0507087	7.442849	0.0000000	0.2780298	0.4768041
COMPETITIONS	~~	COMPETITIONS	0.5546099	0.0587284	9.443648	0.0000000	0.4395045	0.6697154
TEACHERS_CONTEVAL	~~	TEACHERS_CONTEVAL	0.2395359	0.1322884	1.810709	0.0701858	-0.0197447	0.4988164
TEACHERS_FEEDBACK	~~	TEACHERS_FEEDBACK	0.3663200	0.1151888	3.180169	0.0014719	0.1405541	0.5920860
MR2	~~	MR2	1.0000000	0.0000000	NA	NA	1.0000000	1.0000000
MR1	~~	MR1	1.0000000	0.0000000	NA	NA	1.0000000	1.0000000
MR5	~~	MR5	1.0000000	0.0000000	NA	NA	1.0000000	1.0000000
MR3	~~	MR3	1.0000000	0.0000000	NA	NA	1.0000000	1.0000000
MR4	~~	MR4	1.0000000	0.0000000	NA	NA	1.0000000	1.0000000
MR2	~~	MR1	0.2267314	0.0728570	3.112004	0.0018582	0.0839342	0.3695285
MR2	~~	MR5	0.4320738	0.0620679	6.961304	0.0000000	0.3104229	0.5537247
MR2	~~	MR3	0.5805768	0.0533162	10.889306	0.0000000	0.4760789	0.6850747
MR2	~~	MR4	0.2637802	0.0754818	3.494619	0.0004747	0.1158386	0.4117218
MR1	~~	MR5	0.5466257	0.0561677	9.732030	0.0000000	0.4365391	0.6567124
MR1	~~	MR3	0.4127084	0.0671940	6.142042	0.0000000	0.2810106	0.5444062
MR1	~~	MR4	0.2824612	0.0769172	3.672275	0.0002404	0.1317062	0.4332162
MR5	~~	MR3	0.4057557	0.0663716	6.113388	0.0000000	0.2756696	0.5358417
MR5	~~	MR4	0.2108964	0.0779799	2.704496	0.0068408	0.0580585	0.3637343
MR3	~~	MR4	0.3445595	0.0751454	4.585236	0.0000045	0.1972772	0.4918419

TIMELINE

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(TIMELINE)`
  <fct>            <dbl>
1 2017             1.12 
2 2022             0.773

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1   1.623 0.2034
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  TIMELINE by GODINA
t = -2.1694, df = 416, p-value = 0.03062
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.39600855 -0.01950901
sample estimates:
mean in group 2017 mean in group 2022 
          3.873874           4.081633

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  TIMELINE by GODINA
t = -2.1941, df = 414.6, p-value = 0.02878
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.39389339 -0.02162417
sample estimates:
mean in group 2017 mean in group 2022 
          3.873874           4.081633

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  TIMELINE by GODINA
W = 19710, p-value = 0.07373
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -2.348265e-05  2.841556e-05
sample estimates:
difference in location 
          -6.79442e-05

RISK

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(RISK)`
  <fct>        <dbl>
1 2017         1.14 
2 2022         0.845

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  0.8091 0.3689
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  RISK by GODINA
t = -2.8671, df = 416, p-value = 0.004353
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.47454778 -0.08851528
sample estimates:
mean in group 2017 mean in group 2022 
          3.968468           4.250000

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  RISK by GODINA
t = -2.8941, df = 415.71, p-value = 0.004002
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.47274763 -0.09031543
sample estimates:
mean in group 2017 mean in group 2022 
          3.968468           4.250000

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  RISK by GODINA
W = 18544, p-value = 0.005211
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -2.679233e-05 -1.140304e-06
sample estimates:
difference in location 
         -7.738274e-05

AVERAGE

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(AVERAGE)`
  <fct>           <dbl>
1 2017             1.56
2 2022             1.32

Levene's Test for Homogeneity of Variance (center = median)
       Df F value  Pr(>F)  
group   1  4.0114 0.04584 *
      416                  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  AVERAGE by GODINA
t = -3.2157, df = 416, p-value = 0.001403
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.6113743 -0.1474968
sample estimates:
mean in group 2017 mean in group 2022 
          3.360360           3.739796

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  AVERAGE by GODINA
t = -3.2325, df = 415.29, p-value = 0.001325
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.6101741 -0.1486970
sample estimates:
mean in group 2017 mean in group 2022 
          3.360360           3.739796

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  AVERAGE by GODINA
W = 18018, p-value = 0.001638
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -0.99991255 -0.00005849
sample estimates:
difference in location 
          -3.55019e-05

SCHEDULE

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(SCHEDULE)`
  <fct>            <dbl>
1 2017             0.855
2 2022             0.348

Levene's Test for Homogeneity of Variance (center = median)
       Df F value    Pr(>F)    
group   1  18.863 1.767e-05 ***
      416                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  SCHEDULE by GODINA
t = -4.3431, df = 416, p-value = 1.767e-05
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.4858008 -0.1830722
sample estimates:
mean in group 2017 mean in group 2022 
          4.328829           4.663265

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  SCHEDULE by GODINA
t = -4.459, df = 380.03, p-value = 1.086e-05
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.4819097 -0.1869633
sample estimates:
mean in group 2017 mean in group 2022 
          4.328829           4.663265

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  SCHEDULE by GODINA
W = 17716, p-value = 0.0001257
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -8.616909e-05 -6.128210e-05
sample estimates:
difference in location 
         -3.956132e-06

CALENDAR

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(CALENDAR)`
  <fct>            <dbl>
1 2017             0.567
2 2022             0.491

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  0.5842 0.4451
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  CALENDAR by GODINA
t = -0.76432, df = 416, p-value = 0.4451
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.19504139  0.08583013
sample estimates:
mean in group 2017 mean in group 2022 
          4.527027           4.581633

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  CALENDAR by GODINA
t = -0.76777, df = 414.85, p-value = 0.4431
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.19441155  0.08520029
sample estimates:
mean in group 2017 mean in group 2022 
          4.527027           4.581633

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  CALENDAR by GODINA
W = 20954, p-value = 0.4358
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -1.273726e-05  4.960957e-05
sample estimates:
difference in location 
         -1.265484e-05

COURSES

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(COURSES)`
  <fct>           <dbl>
1 2017            1.09 
2 2022            0.800

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  1.1352 0.2873
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  COURSES by GODINA
t = -1.3955, df = 416, p-value = 0.1636
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.32172163  0.05457693
sample estimates:
mean in group 2017 mean in group 2022 
          3.968468           4.102041

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  COURSES by GODINA
t = -1.4089, df = 415.64, p-value = 0.1596
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.31993177  0.05278708
sample estimates:
mean in group 2017 mean in group 2022 
          3.968468           4.102041

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  COURSES by GODINA
W = 20612, p-value = 0.3221
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -3.219377e-05  5.698662e-05
sample estimates:
difference in location 
         -4.635467e-05

NOTIFICATIONS_COURSE

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(NOTIFICATIONS_COURSE)`
  <fct>                        <dbl>
1 2017                         0.741
2 2022                         0.497

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  2.6173 0.1065
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  NOTIFICATIONS_COURSE by GODINA
t = -1.6178, df = 416, p-value = 0.1065
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.27804846  0.02699128
sample estimates:
mean in group 2017 mean in group 2022 
          4.369369           4.494898

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  NOTIFICATIONS_COURSE by GODINA
t = -1.6378, df = 413.76, p-value = 0.1022
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.27619307  0.02513589
sample estimates:
mean in group 2017 mean in group 2022 
          4.369369           4.494898

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  NOTIFICATIONS_COURSE by GODINA
W = 20325, p-value = 0.1887
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -5.206516e-05  6.122543e-05
sample estimates:
difference in location 
         -5.846141e-05

NOTIFICATIONS_DEADLINES

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(NOTIFICATIONS_DEADLINES)`
  <fct>                           <dbl>
1 2017                            0.836
2 2022                            0.806

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  0.0352 0.8512
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  NOTIFICATIONS_DEADLINES by GODINA
t = -0.65004, df = 416, p-value = 0.516
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.2324921  0.1169377
sample estimates:
mean in group 2017 mean in group 2022 
          4.243243           4.301020

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  NOTIFICATIONS_DEADLINES by GODINA
t = -0.65078, df = 411.32, p-value = 0.5155
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.2322981  0.1167438
sample estimates:
mean in group 2017 mean in group 2022 
          4.243243           4.301020

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  NOTIFICATIONS_DEADLINES by GODINA
W = 20798, p-value = 0.3947
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -1.221078e-05  6.490225e-06
sample estimates:
difference in location 
         -3.442804e-05

MESSAGES_TEACHER

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(MESSAGES_TEACHER)`
  <fct>                    <dbl>
1 2017                     0.739
2 2022                     0.725

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  0.1345  0.714
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  MESSAGES_TEACHER by GODINA
t = -0.5358, df = 416, p-value = 0.5924
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.2098709  0.1199647
sample estimates:
mean in group 2017 mean in group 2022 
          4.108108           4.153061

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  MESSAGES_TEACHER by GODINA
t = -0.53613, df = 410.55, p-value = 0.5922
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.2097775  0.1198713
sample estimates:
mean in group 2017 mean in group 2022 
          4.108108           4.153061

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  MESSAGES_TEACHER by GODINA
W = 21064, p-value = 0.547
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -6.627689e-06  1.989761e-05
sample estimates:
difference in location 
         -1.398957e-05

MESSAGES_ADMIN

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(MESSAGES_ADMIN)`
  <fct>                  <dbl>
1 2017                    1.14
2 2022                    1.05

Levene's Test for Homogeneity of Variance (center = median)
       Df F value  Pr(>F)  
group   1  3.0274 0.08261 .
      416                  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  MESSAGES_ADMIN by GODINA
t = -1.4742, df = 416, p-value = 0.1412
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.35372579  0.05054506
sample estimates:
mean in group 2017 mean in group 2022 
          3.450450           3.602041

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  MESSAGES_ADMIN by GODINA
t = -1.4781, df = 413.2, p-value = 0.1401
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.35319369  0.05001295
sample estimates:
mean in group 2017 mean in group 2022 
          3.450450           3.602041

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  MESSAGES_ADMIN by GODINA
W = 19902, p-value = 0.1161
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -2.693430e-05  2.806308e-06
sample estimates:
difference in location 
         -4.097029e-05

COMPARISON_EXAMS

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(COMPARISON_EXAMS)`
  <fct>                    <dbl>
1 2017                      1.54
2 2022                      1.82

Levene's Test for Homogeneity of Variance (center = median)
       Df F value  Pr(>F)  
group   1  3.5316 0.06091 .
      416                  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  COMPARISON_EXAMS by GODINA
t = 0.10014, df = 416, p-value = 0.9203
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.2363401  0.2617124
sample estimates:
mean in group 2017 mean in group 2022 
          3.135135           3.122449

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  COMPARISON_EXAMS by GODINA
t = 0.099614, df = 398.67, p-value = 0.9207
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.2376816  0.2630539
sample estimates:
mean in group 2017 mean in group 2022 
          3.135135           3.122449

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  COMPARISON_EXAMS by GODINA
W = 21814, p-value = 0.9618
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -5.478454e-05  5.561643e-05
sample estimates:
difference in location 
          4.688267e-05

COMPARISON_ECTS

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(COMPARISON_ECTS)`
  <fct>                   <dbl>
1 2017                     1.72
2 2022                     1.75

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  0.0189 0.8907
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  COMPARISON_ECTS by GODINA
t = -1.0072, df = 416, p-value = 0.3144
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.3836693  0.1236950
sample estimates:
mean in group 2017 mean in group 2022 
          2.941441           3.071429

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  COMPARISON_ECTS by GODINA
t = -1.0067, df = 408.76, p-value = 0.3147
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.3838109  0.1238367
sample estimates:
mean in group 2017 mean in group 2022 
          2.941441           3.071429

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  COMPARISON_ECTS by GODINA
W = 20577, p-value = 0.3276
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -6.757785e-05  3.413274e-05
sample estimates:
difference in location 
         -2.310569e-05

COMPARISON_GRADES

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(COMPARISON_GRADES)`
  <fct>                     <dbl>
1 2017                       1.63
2 2022                       1.82

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  1.6006 0.2065
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  COMPARISON_GRADES by GODINA
t = -0.26612, df = 416, p-value = 0.7903
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.2867983  0.2184034
sample estimates:
mean in group 2017 mean in group 2022 
          3.027027           3.061224

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  COMPARISON_GRADES by GODINA
t = -0.26519, df = 402.84, p-value = 0.791
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.2877080  0.2193131
sample estimates:
mean in group 2017 mean in group 2022 
          3.027027           3.061224

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  COMPARISON_GRADES by GODINA
W = 21429, p-value = 0.7864
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -2.920479e-05  4.033694e-05
sample estimates:
difference in location 
         -7.982535e-06

COMPARISON_OBLIGATIONS

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(COMPARISON_OBLIGATIONS)`
  <fct>                          <dbl>
1 2017                            1.56
2 2022                            1.72

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  1.5992 0.2067
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  COMPARISON_OBLIGATIONS by GODINA
t = -1.085, df = 416, p-value = 0.2786
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.3822892  0.1103642
sample estimates:
mean in group 2017 mean in group 2022 
          2.986486           3.122449

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  COMPARISON_OBLIGATIONS by GODINA
t = -1.0817, df = 403.95, p-value = 0.28
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.3830488  0.1111238
sample estimates:
mean in group 2017 mean in group 2022 
          2.986486           3.122449

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  COMPARISON_OBLIGATIONS by GODINA
W = 20392, p-value = 0.2552
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -3.424237e-05  3.419917e-05
sample estimates:
difference in location 
         -4.986745e-05

STATUS_STUDENTS

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(STATUS_STUDENTS)`
  <fct>                   <dbl>
1 2017                     1.68
2 2022                     1.91

Levene's Test for Homogeneity of Variance (center = median)
       Df F value  Pr(>F)  
group   1  3.3516 0.06785 .
      416                  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  STATUS_STUDENTS by GODINA
t = -0.34103, df = 416, p-value = 0.7333
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.3025040  0.2130574
sample estimates:
mean in group 2017 mean in group 2022 
          2.990991           3.035714

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  STATUS_STUDENTS by GODINA
t = -0.33969, df = 401.78, p-value = 0.7343
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.3035538  0.2141072
sample estimates:
mean in group 2017 mean in group 2022 
          2.990991           3.035714

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  STATUS_STUDENTS by GODINA
W = 21380, p-value = 0.7557
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -2.600967e-05  4.274425e-05
sample estimates:
difference in location 
         -5.555976e-06

PREDICTION_COURSE

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(PREDICTION_COURSE)`
  <fct>                     <dbl>
1 2017                       1.37
2 2022                       1.45

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  0.2122 0.6453
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  PREDICTION_COURSE by GODINA
t = -0.63087, df = 416, p-value = 0.5285
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.3017454  0.1551191
sample estimates:
mean in group 2017 mean in group 2022 
          3.860360           3.933673

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  PREDICTION_COURSE by GODINA
t = -0.62979, df = 406.57, p-value = 0.5292
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.3021521  0.1555259
sample estimates:
mean in group 2017 mean in group 2022 
          3.860360           3.933673

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  PREDICTION_COURSE by GODINA
W = 20646, p-value = 0.342
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -2.809962e-05  4.931042e-05
sample estimates:
difference in location 
         -4.099114e-06

PREDICTION_YEAR

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(PREDICTION_YEAR)`
  <fct>                   <dbl>
1 2017                     1.38
2 2022                     1.50

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  0.8563 0.3553
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  PREDICTION_YEAR by GODINA
t = -0.47813, df = 416, p-value = 0.6328
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.2870885  0.1747517
sample estimates:
mean in group 2017 mean in group 2022 
          3.923423           3.979592

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  PREDICTION_YEAR by GODINA
t = -0.47695, df = 405.07, p-value = 0.6337
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.2876761  0.1753393
sample estimates:
mean in group 2017 mean in group 2022 
          3.923423           3.979592

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  PREDICTION_YEAR by GODINA
W = 20698, p-value = 0.363
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -1.721420e-05  2.633612e-05
sample estimates:
difference in location 
         -7.957205e-05

PREDICTION_ECTS

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(PREDICTION_ECTS)`
  <fct>                   <dbl>
1 2017                     1.55
2 2022                     1.25

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  2.3598 0.1253
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  PREDICTION_ECTS by GODINA
t = -1.5479, df = 416, p-value = 0.1224
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.40889048  0.04862205
sample estimates:
mean in group 2017 mean in group 2022 
          3.896396           4.076531

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  PREDICTION_ECTS by GODINA
t = -1.5582, df = 415.87, p-value = 0.1199
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.40736915  0.04710072
sample estimates:
mean in group 2017 mean in group 2022 
          3.896396           4.076531

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  PREDICTION_ECTS by GODINA
W = 20288, p-value = 0.205
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -6.690491e-07  3.865104e-05
sample estimates:
difference in location 
         -5.663361e-06

PREDICTION_PROGRAM

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(PREDICTION_PROGRAM)`
  <fct>                      <dbl>
1 2017                        1.36
2 2022                        1.10

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  1.1818 0.2776
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  PREDICTION_PROGRAM by GODINA
t = -1.117, df = 416, p-value = 0.2646
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.33627822  0.09257533
sample estimates:
mean in group 2017 mean in group 2022 
          3.995495           4.117347

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  PREDICTION_PROGRAM by GODINA
t = -1.1245, df = 415.87, p-value = 0.2615
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.33485441  0.09115152
sample estimates:
mean in group 2017 mean in group 2022 
          3.995495           4.117347

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  PREDICTION_PROGRAM by GODINA
W = 20820, p-value = 0.4168
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -6.589806e-06  5.129451e-05
sample estimates:
difference in location 
          -3.18195e-05

ECTSAVG_STUDY

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(ECTSAVG_STUDY)`
  <fct>                 <dbl>
1 2017                   1.46
2 2022                   1.52

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  0.7446 0.3887
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  ECTSAVG_STUDY by GODINA
t = -1.9287, df = 416, p-value = 0.05444
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.465900568  0.004418678
sample estimates:
mean in group 2017 mean in group 2022 
          3.085586           3.316327

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  ECTSAVG_STUDY by GODINA
t = -1.9265, df = 407.56, p-value = 0.05474
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.466193675  0.004711785
sample estimates:
mean in group 2017 mean in group 2022 
          3.085586           3.316327

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  ECTSAVG_STUDY by GODINA
W = 19415, p-value = 0.0512
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -1.649149e-05  4.045726e-05
sample estimates:
difference in location 
         -6.715768e-06

ECTSAVG_COURSE

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(ECTSAVG_COURSE)`
  <fct>                  <dbl>
1 2017                    1.31
2 2022                    1.57

Levene's Test for Homogeneity of Variance (center = median)
       Df F value  Pr(>F)  
group   1  3.9584 0.04729 *
      416                  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  ECTSAVG_COURSE by GODINA
t = -0.61308, df = 416, p-value = 0.5402
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.3025708  0.1587025
sample estimates:
mean in group 2017 mean in group 2022 
          3.157658           3.229592

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  ECTSAVG_COURSE by GODINA
t = -0.60961, df = 397.55, p-value = 0.5425
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.3039170  0.1600486
sample estimates:
mean in group 2017 mean in group 2022 
          3.157658           3.229592

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  ECTSAVG_COURSE by GODINA
W = 20880, p-value = 0.4647
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -1.821468e-05  1.796003e-05
sample estimates:
difference in location 
         -6.425534e-05

AVGGRADE_COURSE

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(AVGGRADE_COURSE)`
  <fct>                   <dbl>
1 2017                     1.30
2 2022                     1.22

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  1.8589 0.1735
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  AVGGRADE_COURSE by GODINA
t = -2.3018, df = 416, p-value = 0.02184
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.47023059 -0.03703177
sample estimates:
mean in group 2017 mean in group 2022 
          3.450450           3.704082

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  AVGGRADE_COURSE by GODINA
t = -2.3059, df = 412.22, p-value = 0.02161
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.46984262 -0.03741974
sample estimates:
mean in group 2017 mean in group 2022 
          3.450450           3.704082

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  AVGGRADE_COURSE by GODINA
W = 18906, p-value = 0.01526
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -6.988467e-05 -5.155438e-05
sample estimates:
difference in location 
         -3.513701e-05

PASSRATE_COURSE

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(PASSRATE_COURSE)`
  <fct>                   <dbl>
1 2017                    1.29 
2 2022                    0.832

Levene's Test for Homogeneity of Variance (center = median)
       Df F value  Pr(>F)  
group   1  5.7539 0.01689 *
      416                  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  PASSRATE_COURSE by GODINA
t = -3.5062, df = 416, p-value = 0.000504
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.5557190 -0.1564524
sample estimates:
mean in group 2017 mean in group 2022 
          3.761261           4.117347

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  PASSRATE_COURSE by GODINA
t = -3.5534, df = 412.51, p-value = 0.0004243
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.5530727 -0.1590986
sample estimates:
mean in group 2017 mean in group 2022 
          3.761261           4.117347

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  PASSRATE_COURSE by GODINA
W = 18070, p-value = 0.001463
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -1.691586e-05 -3.816379e-05
sample estimates:
difference in location 
         -4.031717e-05

PPLAN_COURSE

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(PPLAN_COURSE)`
  <fct>                <dbl>
1 2017                  1.25
2 2022                  1.01

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  2.4616 0.1174
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  PPLAN_COURSE by GODINA
t = 1.9801, df = 416, p-value = 0.04835
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 0.001510562 0.412628066
sample estimates:
mean in group 2017 mean in group 2022 
          3.977477           3.770408

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  PPLAN_COURSE by GODINA
t = 1.9935, df = 415.89, p-value = 0.04686
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 0.002890476 0.411248153
sample estimates:
mean in group 2017 mean in group 2022 
          3.977477           3.770408

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  PPLAN_COURSE by GODINA
W = 24956, p-value = 0.006389
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 5.301290e-05 6.462976e-06
sample estimates:
difference in location 
          1.771137e-05

PPLAN_STUDY

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(PPLAN_STUDY)`
  <fct>               <dbl>
1 2017                1.16 
2 2022                0.984

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  2.1294 0.1453
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  PPLAN_STUDY by GODINA
t = 1.8419, df = 416, p-value = 0.0662
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.01260611  0.38785892
sample estimates:
mean in group 2017 mean in group 2022 
          4.085586           3.897959

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  PPLAN_STUDY by GODINA
t = 1.8516, df = 415.31, p-value = 0.0648
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.01156305  0.38681585
sample estimates:
mean in group 2017 mean in group 2022 
          4.085586           3.897959

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  PPLAN_STUDY by GODINA
W = 24780, p-value = 0.009218
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 3.287967e-05 5.191436e-05
sample estimates:
difference in location 
          6.987603e-06

PPLAN_MONITORING

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(PPLAN_MONITORING)`
  <fct>                    <dbl>
1 2017                     1.08 
2 2022                     0.919

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  1.0179 0.3136
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  PPLAN_MONITORING by GODINA
t = 1.9051, df = 416, p-value = 0.05746
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.005947415  0.380005146
sample estimates:
mean in group 2017 mean in group 2022 
          4.090090           3.903061

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  PPLAN_MONITORING by GODINA
t = 1.9146, df = 415.16, p-value = 0.05623
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.004992374  0.379050105
sample estimates:
mean in group 2017 mean in group 2022 
          4.090090           3.903061

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  PPLAN_MONITORING by GODINA
W = 24812, p-value = 0.008089
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 5.320344e-06 1.366807e-05
sample estimates:
difference in location 
          1.618971e-05

ECTS_MONITORING

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(ECTS_MONITORING)`
  <fct>                   <dbl>
1 2017                    1.37 
2 2022                    0.730

Levene's Test for Homogeneity of Variance (center = median)
       Df F value    Pr(>F)    
group   1  22.948 2.319e-06 ***
      416                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  ECTS_MONITORING by GODINA
t = -3.6608, df = 416, p-value = 0.0002838
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.5703878 -0.1718442
sample estimates:
mean in group 2017 mean in group 2022 
          3.720721           4.091837

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  ECTS_MONITORING by GODINA
t = -3.731, df = 402.24, p-value = 0.0002181
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.5666577 -0.1755743
sample estimates:
mean in group 2017 mean in group 2022 
          3.720721           4.091837

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  ECTS_MONITORING by GODINA
W = 18202, p-value = 0.002377
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -4.613658e-05 -4.993252e-05
sample estimates:
difference in location 
         -8.121092e-05

PPLAN_EXTRACURRICULAR

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(PPLAN_EXTRACURRICULAR)`
  <fct>                         <dbl>
1 2017                           1.47
2 2022                           1.19

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  2.1951 0.1392
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  PPLAN_EXTRACURRICULAR by GODINA
t = 0.060395, df = 416, p-value = 0.9519
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.2160574  0.2297548
sample estimates:
mean in group 2017 mean in group 2022 
          3.409910           3.403061

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  PPLAN_EXTRACURRICULAR by GODINA
t = 0.060804, df = 415.89, p-value = 0.9515
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.2145558  0.2282531
sample estimates:
mean in group 2017 mean in group 2022 
          3.409910           3.403061

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  PPLAN_EXTRACURRICULAR by GODINA
W = 22032, p-value = 0.8181
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -3.154344e-05  4.551866e-05
sample estimates:
difference in location 
          5.796829e-05

TEACHER_EVALUATION

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(TEACHER_EVALUATION)`
  <fct>                      <dbl>
1 2017                        1.50
2 2022                        1.30

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  0.5792 0.4471
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  TEACHER_EVALUATION by GODINA
t = -2.694, df = 416, p-value = 0.007346
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.5419667 -0.0847110
sample estimates:
mean in group 2017 mean in group 2022 
          3.477477           3.790816

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  TEACHER_EVALUATION by GODINA
t = -2.7057, df = 414.73, p-value = 0.007097
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.54098085 -0.08569685
sample estimates:
mean in group 2017 mean in group 2022 
          3.477477           3.790816

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  TEACHER_EVALUATION by GODINA
W = 18622, p-value = 0.008326
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -9.999860e-01 -1.349347e-05
sample estimates:
difference in location 
         -8.052474e-06

TEACHER_PROFILE

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(TEACHER_PROFILE)`
  <fct>                   <dbl>
1 2017                     1.38
2 2022                     1.38

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  0.0223 0.8814
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  TEACHER_PROFILE by GODINA
t = -0.61628, df = 416, p-value = 0.538
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.2971372  0.1552913
sample estimates:
mean in group 2017 mean in group 2022 
          3.342342           3.413265

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  TEACHER_PROFILE by GODINA
t = -0.61619, df = 409.36, p-value = 0.5381
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.2971822  0.1553363
sample estimates:
mean in group 2017 mean in group 2022 
          3.342342           3.413265

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  TEACHER_PROFILE by GODINA
W = 21030, p-value = 0.5435
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -2.66143e-05  2.89425e-05
sample estimates:
difference in location 
         -1.186989e-05

TEACHERS_CONTEVAL

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(TEACHERS_CONTEVAL)`
  <fct>                     <dbl>
1 2017                       1.52
2 2022                       1.24

Levene's Test for Homogeneity of Variance (center = median)
       Df F value  Pr(>F)  
group   1  5.6264 0.01815 *
      416                  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  TEACHERS_CONTEVAL by GODINA
t = -2.6715, df = 416, p-value = 0.007849
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.53551877 -0.08150642
sample estimates:
mean in group 2017 mean in group 2022 
          3.518018           3.826531

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  TEACHERS_CONTEVAL by GODINA
t = -2.6882, df = 415.75, p-value = 0.007473
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.5341067 -0.0829185
sample estimates:
mean in group 2017 mean in group 2022 
          3.518018           3.826531

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  TEACHERS_CONTEVAL by GODINA
W = 18705, p-value = 0.01041
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -9.999600e-01 -4.850579e-05
sample estimates:
difference in location 
         -4.338908e-05

TEACHERS_FEEDBACK

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(TEACHERS_FEEDBACK)`
  <fct>                     <dbl>
1 2017                       1.35
2 2022                       1.15

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  0.8079 0.3693
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  TEACHERS_FEEDBACK by GODINA
t = -0.54744, df = 416, p-value = 0.5844
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.2762109  0.1558763
sample estimates:
mean in group 2017 mean in group 2022 
          3.761261           3.821429

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  TEACHERS_FEEDBACK by GODINA
t = -0.55013, df = 415.13, p-value = 0.5825
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.2751522  0.1548176
sample estimates:
mean in group 2017 mean in group 2022 
          3.761261           3.821429

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  TEACHERS_FEEDBACK by GODINA
W = 21400, p-value = 0.764
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -6.108471e-05  3.492944e-05
sample estimates:
difference in location 
         -4.489519e-05

BADGES_COMPARISON

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(BADGES_COMPARISON)`
  <fct>                     <dbl>
1 2017                       1.65
2 2022                       1.63

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  1.1845 0.2771
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  BADGES_COMPARISON by GODINA
t = 2.8565, df = 416, p-value = 0.004497
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 0.1117972 0.6051545
sample estimates:
mean in group 2017 mean in group 2022 
          2.720721           2.362245

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  BADGES_COMPARISON by GODINA
t = 2.8578, df = 410.3, p-value = 0.004483
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 0.1118955 0.6050562
sample estimates:
mean in group 2017 mean in group 2022 
          2.720721           2.362245

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  BADGES_COMPARISON by GODINA
W = 25301, p-value = 0.003139
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 6.832901e-05 9.999536e-01
sample estimates:
difference in location 
          6.979644e-06

BADGES_COLLECTING

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(BADGES_COLLECTING)`
  <fct>                     <dbl>
1 2017                       1.83
2 2022                       1.78

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  0.0166 0.8976
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  BADGES_COLLECTING by GODINA
t = 2.7283, df = 416, p-value = 0.006635
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 0.1004993 0.6185667
sample estimates:
mean in group 2017 mean in group 2022 
          2.981982           2.622449

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  BADGES_COLLECTING by GODINA
t = 2.7303, df = 410.74, p-value = 0.006599
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 0.1006809 0.6183851
sample estimates:
mean in group 2017 mean in group 2022 
          2.981982           2.622449

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  BADGES_COLLECTING by GODINA
W = 25029, p-value = 0.006563
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 3.026748e-05 9.999745e-01
sample estimates:
difference in location 
          5.225705e-05

AWARDS

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(AWARDS)`
  <fct>          <dbl>
1 2017            1.63
2 2022            1.64

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  0.1674 0.6826
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  AWARDS by GODINA
t = 1.4776, df = 416, p-value = 0.1403
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.06122802  0.43197632
sample estimates:
mean in group 2017 mean in group 2022 
          2.833333           2.647959

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  AWARDS by GODINA
t = 1.4773, df = 409.23, p-value = 0.1404
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.0612959  0.4320442
sample estimates:
mean in group 2017 mean in group 2022 
          2.833333           2.647959

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  AWARDS by GODINA
W = 23570, p-value = 0.1316
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -4.442025e-05  5.211709e-06
sample estimates:
difference in location 
          4.385556e-05

COMPETITIONS

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(COMPETITIONS)`
  <fct>                <dbl>
1 2017                  1.55
2 2022                  1.70

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  1.2374 0.2666
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  COMPETITIONS by GODINA
t = -0.15472, df = 416, p-value = 0.8771
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.2645683  0.2259583
sample estimates:
mean in group 2017 mean in group 2022 
          2.837838           2.857143

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  COMPETITIONS by GODINA
t = -0.15426, df = 404.02, p-value = 0.8775
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.2653166  0.2267066
sample estimates:
mean in group 2017 mean in group 2022 
          2.837838           2.857143

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  COMPETITIONS by GODINA
W = 21627, p-value = 0.9149
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -6.823494e-05  9.041870e-06
sample estimates:
difference in location 
         -1.979868e-05

ASSESSMENT_CONT

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(ASSESSMENT_CONT)`
  <fct>                   <dbl>
1 2017                    0.685
2 2022                    0.371

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)  
group   1  5.0255 0.0255 *
      416                 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  ASSESSMENT_CONT by GODINA
t = -2.2418, df = 416, p-value = 0.0255
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.30245556 -0.01984634
sample estimates:
mean in group 2017 mean in group 2022 
          4.522523           4.683673

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  ASSESSMENT_CONT by GODINA
t = -2.2837, df = 403.34, p-value = 0.02291
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.29987472 -0.02242717
sample estimates:
mean in group 2017 mean in group 2022 
          4.522523           4.683673

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  ASSESSMENT_CONT by GODINA
W = 19875, p-value = 0.05656
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -9.717446e-06  6.594312e-05
sample estimates:
difference in location 
         -6.562629e-05

COMPARISON_OTHERS

Column

Varijance po grupama i Levene test

# A tibble: 2 × 2
  GODINA `var(COMPARISON_OTHERS)`
  <fct>                     <dbl>
1 2017                       1.64
2 2022                       1.85

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  2.6677 0.1032
      416

Stupčasti dijagram po godinama

Column

t-test (uz pretpostavku da su varijance po grupama iste)


    Two Sample t-test

data:  COMPARISON_OTHERS by GODINA
t = -1.3543, df = 416, p-value = 0.1764
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.42885773  0.07897723
sample estimates:
mean in group 2017 mean in group 2022 
           3.04955            3.22449

t-test (uz pretpostavku da varijance po grupama nisu iste)


    Welch Two Sample t-test

data:  COMPARISON_OTHERS by GODINA
t = -1.3493, df = 402.39, p-value = 0.178
alternative hypothesis: true difference in means between group 2017 and group 2022 is not equal to 0
95 percent confidence interval:
 -0.42982320  0.07994271
sample estimates:
mean in group 2017 mean in group 2022 
           3.04955            3.22449

Wilcox test


    Wilcoxon rank sum test with continuity correction

data:  COMPARISON_OTHERS by GODINA
W = 20052, p-value = 0.1575
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -2.743212e-07  8.353270e-06
sample estimates:
difference in location 
         -5.744507e-06

---
title: "Faktorska analiza"
output: 
  flexdashboard::flex_dashboard:
    social: menu
    orientation: columns
    vertical_layout: fill
    source_code: embed
---

```{css, echo=FALSE}
.sidebar { overflow: auto; }
.dataTables_scrollBody {
    height:95% !important;
    max-height:95% !important;
}
.chart-stage-flex {
    overflow:auto !important;
}
```

```{r setup, include=FALSE}
library(DT)
library(knitr)
library(kableExtra)
library(psych)
library(car)
library(readxl)
library(tidyverse)
library(corrplot)
library(svgPanZoom)
library(lavaan)
library(parameters)

cols <- c("osoba", "DYNAMICS", "AVERAGE_COMPARISON", "REQUESTS", "MESSAGES_SU",
          "EXTRACURRICULAR", "STATUS_COMPARISON", "PREDICTION_GROUP", "TEACHERS", "BADGES", 
          "PREDICTION_OBLIGATIONS", "ABSENCE", "STATUS_CURRENT", "ASSESSMENT_EXAMS")
podaci_EFA <- read_excel('potrebe_studenata_2017.xlsx', sheet=1) %>% select(-one_of(cols))
new_cols <- c("osoba")
podaci_CFA <- read_excel('potrebe_studenata_2022.xlsx', sheet=1) %>% select(-one_of(new_cols))
EFA_model <- fa(podaci_EFA, nfactors = 5)
file.name <- './graf4.svg'
in.svg <- readChar(file.name, nchars = file.info(file.name)$size)

podaci_sve <- bind_rows("2017" = podaci_EFA, "2022" = podaci_CFA, .id = "GODINA") %>% 
  select_if(~ !any(is.na(.)))
podaci_sve$GODINA <- factor(podaci_sve$GODINA)

model1 <- efa_to_cfa(EFA_model, threshold = 0.6)
rez1 <- cfa(model1, podaci_CFA)
```

Deskriptiva {data-navmenu="Podaci"}
=======================================================================

Column {.tabset .tabset-fade}
-----------------------------------------------------------------------

### Godina 2017.

```{r}
datatable(round(describe(podaci_EFA),2), filter = 'top',
    extensions = c('FixedColumns','Select', 'Buttons'),
    options = list(
      dom = 'Blfrtip',
      select = list(style = 'os', items = 'row'),
      buttons = c('selectAll', 'selectNone', 'selectRows', 'selectColumns', 'selectCells'),
      pageLength = 15,
      scrollX = TRUE,
      scrollY= TRUE,
      fixedColumns = TRUE
    )
)
```

### Godina 2022.

```{r}
datatable(round(describe(podaci_CFA),2), filter = 'top',
    extensions = c('FixedColumns','Select', 'Buttons'),
    options = list(
      dom = 'Blfrtip',
      select = list(style = 'os', items = 'row'),
      buttons = c('selectAll', 'selectNone', 'selectRows', 'selectColumns', 'selectCells'),
      pageLength = 15,
      scrollX = TRUE,
      scrollY= TRUE,
      fixedColumns = TRUE
    )
)
```

Deskriptiva - slike {data-navmenu="Podaci"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Godina 2017.

```{r,fig.width = 12,fig.height=11}
error.dots(podaci_EFA, tail=30, head=30, sort=FALSE)
```

Column {data-width=400}
-----------------------------------------------------------------------

### Godina 2022.

```{r,fig.width = 12,fig.height=11}
error.dots(podaci_CFA, tail=30, head=30, sort=FALSE)
```

Korelacije {data-navmenu="Podaci"}
=======================================================================

Column {.tabset .tabset-fade}
-----------------------------------------------------------------------

### Godina 2017.

```{r}
datatable(round(corr.test(podaci_EFA, use = "pairwise.complete.obs", minlength=50)$ci, 2), filter = 'top',
    extensions = c('FixedColumns','Select', 'Buttons'),
    options = list(
      dom = 'Blfrtip',
      select = list(style = 'os', items = 'row'),
      buttons = c('selectAll', 'selectNone', 'selectRows', 'selectColumns', 'selectCells'),
      pageLength = 15,
      scrollX = TRUE,
      fixedColumns = TRUE
    )
)
```

### Godina 2022.

```{r}
datatable(round(corr.test(podaci_CFA, use = "pairwise.complete.obs", minlength=50)$ci, 2), filter = 'top',
    extensions = c('FixedColumns','Select', 'Buttons'),
    options = list(
      dom = 'Blfrtip',
      select = list(style = 'os', items = 'row'),
      buttons = c('selectAll', 'selectNone', 'selectRows', 'selectColumns', 'selectCells'),
      pageLength = 15,
      scrollX = TRUE,
      fixedColumns = TRUE
    )
)
```


Korelacije - slike {data-navmenu="Podaci"}
=======================================================================

## Column1 {.tabset .tabset-fade}

### korelacije 2017.

```{r}
corrplot(round(cor(podaci_EFA, use = "pairwise.complete.obs"), 2), type = "upper", tl.col = "black", tl.srt = 90,
         diag=FALSE, mar=c(0,0,0.1,0), tl.cex=0.4)
```

### p-vrijednosti 2017.

```{r}
corrplot(round(corr.test(podaci_EFA, use = "pairwise.complete.obs")$p, 2), type = "upper", tl.col = "black", tl.srt = 90,
         diag=FALSE, mar=c(0,0,0.1,0), tl.cex=0.4)
```

### korelacije 2022.

```{r}
corrplot(round(cor(podaci_CFA, use = "pairwise.complete.obs"), 2), type = "upper", tl.col = "black", tl.srt = 90,
         diag=FALSE, mar=c(0,0,0.1,0), tl.cex=0.4)
```

### p-vrijednosti 2022.

```{r}
corrplot(round(corr.test(podaci_CFA, use = "pairwise.complete.obs")$p, 2), type = "upper", tl.col = "black", tl.srt = 90,
         diag=FALSE, mar=c(0,0,0.1,0), tl.cex=0.4)
```

## Column1 {.tabset .tabset-fade}

### korelacije 2022.

```{r}
corrplot(round(cor(podaci_CFA, use = "pairwise.complete.obs"), 2), type = "upper", tl.col = "black", tl.srt = 90,
         diag=FALSE, mar=c(0,0,0.1,0), tl.cex=0.4)
```

### p-vrijednosti 2022.

```{r}
corrplot(round(corr.test(podaci_CFA, use = "pairwise.complete.obs")$p, 2), type = "upper", tl.col = "black", tl.srt = 90,
         diag=FALSE, mar=c(0,0,0.1,0), tl.cex=0.4)
```

### korelacije 2017.

```{r}
corrplot(round(cor(podaci_EFA, use = "pairwise.complete.obs"), 2), type = "upper", tl.col = "black", tl.srt = 90,
         diag=FALSE, mar=c(0,0,0.1,0), tl.cex=0.4)
```

### p-vrijednosti 2017.

```{r}
corrplot(round(corr.test(podaci_EFA, use = "pairwise.complete.obs")$p, 2), type = "upper", tl.col = "black", tl.srt = 90,
         diag=FALSE, mar=c(0,0,0.1,0), tl.cex=0.4)
```

Unutarnja pouzdanost {data-navmenu="EFA 2017"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Cronbach alpha

```{r, attr.output='style="max-height: 100%;"'}
psych::alpha(podaci_EFA)
```

Column {data-width=400}
-----------------------------------------------------------------------

### split-half


```{r, attr.output='style="max-height: 100%;"'}
splitHalf(podaci_EFA)
```

### Kaiser-Meyer-Olkin

```{r, attr.output='style="max-height: 100%;"'}
KMO(podaci_EFA)
```

EFA - broj faktora {data-navmenu="EFA 2017"}
=======================================================================

Column {data-width=300}
-----------------------------------------------------------------------

### Svojstvene vrijednosti matrice korelacija

```{r warning=FALSE}
EFA_cor <- cor(podaci_EFA, use = "pairwise.complete.obs")
sv <- eigen(EFA_cor)
sv$values
```

Column {data-width=500}
-----------------------------------------------------------------------

### SCREE plot

```{r warning=FALSE,fig.width=8, fig.height=7}
scree(podaci_EFA)
```

EFA model {data-navmenu="EFA 2017"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### EFA - 5 faktora

```{r, attr.output='style="max-height: 100%;"'}
EFA_model
```

Column {data-width=400}
-----------------------------------------------------------------------

### Loadings (cutoff = 0.6)

```{r, attr.output='style="max-height: 100%;"'}
print(EFA_model$loadings, sort=TRUE, cutoff=0.6)
```

Loadings {data-navmenu="EFA 2017"}
=======================================================================

Column {.tabset .tabset-fade}
-----------------------------------------------------------------------

### Interaktivna tablica

```{r}
datatable(round(unclass(EFA_model$loadings),2), filter = 'top',
    extensions = c('FixedColumns','Select', 'Buttons'),
    options = list(
      dom = 'Blfrtip',
      select = list(style = 'os', items = 'row'),
      buttons = c('selectAll', 'selectNone', 'selectRows', 'selectColumns', 'selectCells'),
      pageLength = 15,
      scrollX = TRUE,
      fixedColumns = TRUE
    )
)
```

### graf

```{r}
svgPanZoom(in.svg, controlIconsEnabled = TRUE)
```

Scores {data-navmenu="EFA 2017"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Funkcija gustoće

```{r warning=FALSE}
plot(density(EFA_model$scores, na.rm = TRUE), 
    main = "Factor Scores")
```

Column {data-width=400}
-----------------------------------------------------------------------

### tablica

```{r,attr.output='style="max-height: 100%;"'}
EFA_model$scores
```

Summary {data-navmenu="CFA 2022"}
=======================================================================

Column {.tabset .tabset-fade}
-----------------------------------------------------------------------

### Model (cutoff=0.6)

```{r}
model1
```

### Summary (cutoff=0.6)

```{r}
summary(rez1, fit.measures=TRUE)
```

### Sve mjere testa (cutoff=0.6)

```{r}
fitMeasures(rez1)
```

Parametri {data-navmenu="CFA 2022"}
=======================================================================

### Parametri (cutoff=0.6)

```{r}
parameterEstimates(rez1) %>%
  kbl(caption = "Parametri (cutoff=0.4)") %>%
  kable_classic("hover",full_width = F, html_font = "Cambria")
```

Normalizirani parametri {data-navmenu="CFA 2022"}
=======================================================================

### Normalizirani parametri (cutoff=0.6)

```{r}
standardizedSolution(rez1) %>%
  kbl(caption = "Normalizirani parametri (cutoff=0.4)") %>%
  kable_classic("hover",full_width = F, html_font = "Cambria")
```

TIMELINE {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(TIMELINE))
leveneTest(TIMELINE ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(TIMELINE, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(TIMELINE ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(TIMELINE ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(TIMELINE ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

RISK {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(RISK))
leveneTest(RISK ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(RISK, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(RISK ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(RISK ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(RISK ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

AVERAGE {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(AVERAGE))
leveneTest(AVERAGE ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(AVERAGE, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(AVERAGE ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(AVERAGE ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(AVERAGE ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

SCHEDULE {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(SCHEDULE))
leveneTest(SCHEDULE ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(SCHEDULE, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(SCHEDULE ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(SCHEDULE ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(SCHEDULE ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

CALENDAR {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(CALENDAR))
leveneTest(CALENDAR ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(CALENDAR, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(CALENDAR ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(CALENDAR ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(CALENDAR ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

COURSES {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(COURSES))
leveneTest(COURSES ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(COURSES, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(COURSES ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(COURSES ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(COURSES ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

NOTIFICATIONS_COURSE {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(NOTIFICATIONS_COURSE))
leveneTest(NOTIFICATIONS_COURSE ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(NOTIFICATIONS_COURSE, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(NOTIFICATIONS_COURSE ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(NOTIFICATIONS_COURSE ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(NOTIFICATIONS_COURSE ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

NOTIFICATIONS_DEADLINES {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(NOTIFICATIONS_DEADLINES))
leveneTest(NOTIFICATIONS_DEADLINES ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(NOTIFICATIONS_DEADLINES, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(NOTIFICATIONS_DEADLINES ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(NOTIFICATIONS_DEADLINES ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(NOTIFICATIONS_DEADLINES ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

MESSAGES_TEACHER {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(MESSAGES_TEACHER))
leveneTest(MESSAGES_TEACHER ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(MESSAGES_TEACHER, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(MESSAGES_TEACHER ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(MESSAGES_TEACHER ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(MESSAGES_TEACHER ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

MESSAGES_ADMIN {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(MESSAGES_ADMIN))
leveneTest(MESSAGES_ADMIN ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(MESSAGES_ADMIN, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(MESSAGES_ADMIN ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(MESSAGES_ADMIN ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(MESSAGES_ADMIN ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

COMPARISON_EXAMS {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(COMPARISON_EXAMS))
leveneTest(COMPARISON_EXAMS ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(COMPARISON_EXAMS, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(COMPARISON_EXAMS ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(COMPARISON_EXAMS ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(COMPARISON_EXAMS ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

COMPARISON_ECTS {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(COMPARISON_ECTS))
leveneTest(COMPARISON_ECTS ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(COMPARISON_ECTS, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(COMPARISON_ECTS ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(COMPARISON_ECTS ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(COMPARISON_ECTS ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

COMPARISON_GRADES {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(COMPARISON_GRADES))
leveneTest(COMPARISON_GRADES ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(COMPARISON_GRADES, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(COMPARISON_GRADES ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(COMPARISON_GRADES ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(COMPARISON_GRADES ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

COMPARISON_OBLIGATIONS {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(COMPARISON_OBLIGATIONS))
leveneTest(COMPARISON_OBLIGATIONS ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(COMPARISON_OBLIGATIONS, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(COMPARISON_OBLIGATIONS ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(COMPARISON_OBLIGATIONS ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(COMPARISON_OBLIGATIONS ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

STATUS_STUDENTS {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(STATUS_STUDENTS))
leveneTest(STATUS_STUDENTS ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(STATUS_STUDENTS, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(STATUS_STUDENTS ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(STATUS_STUDENTS ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(STATUS_STUDENTS ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

PREDICTION_COURSE {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(PREDICTION_COURSE))
leveneTest(PREDICTION_COURSE ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(PREDICTION_COURSE, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(PREDICTION_COURSE ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(PREDICTION_COURSE ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(PREDICTION_COURSE ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

PREDICTION_YEAR {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(PREDICTION_YEAR))
leveneTest(PREDICTION_YEAR ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(PREDICTION_YEAR, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(PREDICTION_YEAR ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(PREDICTION_YEAR ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(PREDICTION_YEAR ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

PREDICTION_ECTS {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(PREDICTION_ECTS))
leveneTest(PREDICTION_ECTS ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(PREDICTION_ECTS, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(PREDICTION_ECTS ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(PREDICTION_ECTS ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(PREDICTION_ECTS ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

PREDICTION_PROGRAM {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(PREDICTION_PROGRAM))
leveneTest(PREDICTION_PROGRAM ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(PREDICTION_PROGRAM, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(PREDICTION_PROGRAM ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(PREDICTION_PROGRAM ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(PREDICTION_PROGRAM ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

ECTSAVG_STUDY {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(ECTSAVG_STUDY))
leveneTest(ECTSAVG_STUDY ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(ECTSAVG_STUDY, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(ECTSAVG_STUDY ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(ECTSAVG_STUDY ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(ECTSAVG_STUDY ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

ECTSAVG_COURSE {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(ECTSAVG_COURSE))
leveneTest(ECTSAVG_COURSE ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(ECTSAVG_COURSE, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(ECTSAVG_COURSE ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(ECTSAVG_COURSE ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(ECTSAVG_COURSE ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

AVGGRADE_COURSE {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(AVGGRADE_COURSE))
leveneTest(AVGGRADE_COURSE ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(AVGGRADE_COURSE, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(AVGGRADE_COURSE ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(AVGGRADE_COURSE ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(AVGGRADE_COURSE ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

PASSRATE_COURSE {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(PASSRATE_COURSE))
leveneTest(PASSRATE_COURSE ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(PASSRATE_COURSE, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(PASSRATE_COURSE ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(PASSRATE_COURSE ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(PASSRATE_COURSE ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

PPLAN_COURSE {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(PPLAN_COURSE))
leveneTest(PPLAN_COURSE ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(PPLAN_COURSE, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(PPLAN_COURSE ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(PPLAN_COURSE ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(PPLAN_COURSE ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

PPLAN_STUDY {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(PPLAN_STUDY))
leveneTest(PPLAN_STUDY ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(PPLAN_STUDY, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(PPLAN_STUDY ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(PPLAN_STUDY ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(PPLAN_STUDY ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

PPLAN_MONITORING {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(PPLAN_MONITORING))
leveneTest(PPLAN_MONITORING ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(PPLAN_MONITORING, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(PPLAN_MONITORING ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(PPLAN_MONITORING ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(PPLAN_MONITORING ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

ECTS_MONITORING {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(ECTS_MONITORING))
leveneTest(ECTS_MONITORING ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(ECTS_MONITORING, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(ECTS_MONITORING ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(ECTS_MONITORING ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(ECTS_MONITORING ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

PPLAN_EXTRACURRICULAR {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(PPLAN_EXTRACURRICULAR))
leveneTest(PPLAN_EXTRACURRICULAR ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(PPLAN_EXTRACURRICULAR, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(PPLAN_EXTRACURRICULAR ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(PPLAN_EXTRACURRICULAR ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(PPLAN_EXTRACURRICULAR ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

TEACHER_EVALUATION {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(TEACHER_EVALUATION))
leveneTest(TEACHER_EVALUATION ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(TEACHER_EVALUATION, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(TEACHER_EVALUATION ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(TEACHER_EVALUATION ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(TEACHER_EVALUATION ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

TEACHER_PROFILE {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(TEACHER_PROFILE))
leveneTest(TEACHER_PROFILE ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(TEACHER_PROFILE, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(TEACHER_PROFILE ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(TEACHER_PROFILE ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(TEACHER_PROFILE ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

TEACHERS_CONTEVAL {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(TEACHERS_CONTEVAL))
leveneTest(TEACHERS_CONTEVAL ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(TEACHERS_CONTEVAL, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(TEACHERS_CONTEVAL ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(TEACHERS_CONTEVAL ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(TEACHERS_CONTEVAL ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

TEACHERS_FEEDBACK {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(TEACHERS_FEEDBACK))
leveneTest(TEACHERS_FEEDBACK ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(TEACHERS_FEEDBACK, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(TEACHERS_FEEDBACK ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(TEACHERS_FEEDBACK ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(TEACHERS_FEEDBACK ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

BADGES_COMPARISON {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(BADGES_COMPARISON))
leveneTest(BADGES_COMPARISON ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(BADGES_COMPARISON, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(BADGES_COMPARISON ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(BADGES_COMPARISON ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(BADGES_COMPARISON ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

BADGES_COLLECTING {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(BADGES_COLLECTING))
leveneTest(BADGES_COLLECTING ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(BADGES_COLLECTING, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(BADGES_COLLECTING ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(BADGES_COLLECTING ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(BADGES_COLLECTING ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

AWARDS {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(AWARDS))
leveneTest(AWARDS ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(AWARDS, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(AWARDS ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(AWARDS ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(AWARDS ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

COMPETITIONS {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(COMPETITIONS))
leveneTest(COMPETITIONS ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(COMPETITIONS, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(COMPETITIONS ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(COMPETITIONS ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(COMPETITIONS ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

ASSESSMENT_CONT {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(ASSESSMENT_CONT))
leveneTest(ASSESSMENT_CONT ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(ASSESSMENT_CONT, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(ASSESSMENT_CONT ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(ASSESSMENT_CONT ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(ASSESSMENT_CONT ~ GODINA, data = podaci_sve, conf.int = TRUE)
```

COMPARISON_OTHERS {data-navmenu="TESTOVI"}
=======================================================================

Column {data-width=400}
-----------------------------------------------------------------------

### Varijance po grupama i Levene test

```{r}
podaci_sve %>% group_by(GODINA) %>% summarize(var(COMPARISON_OTHERS))
leveneTest(COMPARISON_OTHERS ~ GODINA, podaci_sve)
```

### Stupčasti dijagram po godinama

```{r}
ggplot(podaci_sve, aes(COMPARISON_OTHERS, fill=GODINA)) + geom_bar(position = position_dodge2(preserve = "single"))
```

Column {data-width=400}
-----------------------------------------------------------------------

### t-test (uz pretpostavku da su varijance po grupama iste)

```{r}
t.test(COMPARISON_OTHERS ~ GODINA, data = podaci_sve, var.equal = TRUE)
```

### t-test (uz pretpostavku da varijance po grupama nisu iste)

```{r}
t.test(COMPARISON_OTHERS ~ GODINA, data = podaci_sve)
```

### Wilcox test

```{r}
wilcox.test(COMPARISON_OTHERS ~ GODINA, data = podaci_sve, conf.int = TRUE)
```