#s 8-1
> m1 <- apply(set.data[c(1,2,3)], 1, sum)
> m2 <- apply(set.data[c(4,5,6)], 1, sum)
> m3 <- apply(set.data[c(7,8,9)], 1, sum)
> score.data <- cbind(set.data,m1,m2,m3)
> head(score.data)
  q1 q2 q3 q4 q5 q6 q7 q8 q9 m1 m2 m3
1  3  4  3  3  4  4  5  5  5 10 11 15
2  4  4  3  4  4  3  4  4  4 11 11 12
3  4  5  3  5  5  5  5  5  5 12 15 15
4  3  4  1  4  3  5  2  5  2  8 12  9
5  4  4  3  4  4  4  4  5  5 11 12 14
6  3  4  1  3  3  3  3  5  3  8  9 11
> write.csv(score.data,"g:/test.csv") #PC̃hCuGɕۑ̂ŗv

#s 8-2
> alpha(set.data[c(1,2,3)])

Reliability analysis   
Call: alpha(x = set.data[c(1, 2, 3)])

  raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
      0.72      0.72    0.66      0.47 2.6 0.031  3.4 0.78     0.54

 lower alpha upper     95% confidence boundaries
0.66 0.72 0.78 

 Reliability if an item is dropped:
   raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
q1      0.46      0.47    0.31      0.31 0.9    0.065    NA  0.31
q2      0.70      0.71    0.55      0.55 2.4    0.036    NA  0.55
q3      0.70      0.70    0.54      0.54 2.4    0.037    NA  0.54

 Item statistics 
     n raw.r std.r r.cor r.drop mean   sd
q1 257  0.86  0.87  0.79   0.67  3.3 0.92
q2 257  0.74  0.77  0.58   0.47  4.0 0.87
q3 257  0.81  0.77  0.59   0.49  2.8 1.11

Non missing response frequency for each item
      1    2    3    4    5 miss
q1 0.04 0.09 0.48 0.28 0.11    0
q2 0.02 0.03 0.19 0.48 0.29    0
q3 0.14 0.22 0.40 0.16 0.08    0
> alpha(set.data[c(4,5,6)])$total
 raw_alpha std.alpha   G6(smc) average_r      S/N        ase     mean        sd  median_r
  0.887059 0.8873682 0.8543661 0.7242263 7.878483 0.01252134 4.111543 0.8050464 0.6838311
> alpha(set.data[7:9])$total
 raw_alpha std.alpha   G6(smc) average_r      S/N       ase     mean        sd  median_r
 0.5606296 0.5647205 0.4681177 0.3018993 1.297374 0.0452352 3.992218 0.5742824 0.2873191

#s 8-3
> head(five.data, n=4)
   Љ p w 
1   67   51   62   50   64
2   46   58   71   70   55
3   39   41   41   59   61
4   65   99   89   63   80
> head(scale(five.data), n=4)
               Љ       p        w      
1  0.5731311 -0.9166013  0.1132119 -0.03575694 0.6901377
2 -1.2192828 -0.4440550  0.5908814  0.82700235 0.2541740
3 -1.8167541 -1.5916675 -1.0013503  0.35248474 0.5448165
4  0.4024250  2.3237165  1.5462205  0.52503660 1.4651842
> s5.result <- fa(five.data, nfactors=2, rotate="varimax")
> head(s5.result$scores, n=4)
         MR1         MR2
1  0.5317416 -0.02410815
2 -1.2498599  0.94595990
3 -1.8574545  0.66527487
4  0.4366432  0.71961869
