ວິຊາ software

 ຂ຃໅ ທຌຖະທຄັ Regression Analysis ຾ຎັຌກາຌທ຾ິ ຃າະຏຌົ ກະ຋ຍົ ຋າຄກຄົ (Direct Effect) ຾຋ໄ າົ ຌຌັ໅ ຍໄ ຘາຓາຈທ຾ິ ຃າະ Indirect Effect ໂຈ໅ 9.2 ກາຌທ຾ິ ຃າະກາຌຊຈົ ຊບງ຿ຍຍຑະນ ຅ະຎະກບຍຈທ໅ ງຉທົ ຎໄ ຽຌ຾ກຌ 1 ຉທົ ຿ຖະ ຉທົ ຎໄ ຽຌ຋າຌາງ 2 ຉທົ ຂຌ໅ ໂຎ ຆໄ ຄຂຽຌ຾ຎັຌຘຓົ ຏຌົ ຃: y,  a  b1x1  b2 x2  .....  bk xk ແຌກຖະຌຓີ ຉີ ທົ ຎໄ ຽຌ຋າຌາງ 2 ຉທົ ຘາຓາຈຂຽຌ຾ຎັຌ y,  a  b1x1  b2 x2 ຆໄ ຄຓທີ ຋ິ ກີ າຌ຃າຌທຌ຃ໄ າຂບຄ a,b1,b2 ຈໄ ຄັ ຘູຈຉໄ ໂຎຌ:ີ໅ a  y  b1 x1  b2 x2     b1   x22  x1 y   x1x2  x2 y   x12  x 2   x1 x2 2 2     b2   x12  x2 y   x1x2  x1 y   x12  x22   x1 x2 2 101

 ຘາ຾ນຈ຋ໄ ຉີ ບ໅ ຄທ຾ິ ຃າະກາຌຊຈົ ຊບງຑະນ  ປຍັ ປູ຾໅ ຊຄີ ຃ທາຓຘາຑຌັ , ບຈິ ຋ຑິ ຌົ ຖະນທໄ າຄຉທົ ຎໄ ຽຌຉຌົ໅ ຿ຖະຉທົ ຎໄ ຽຌຉາຓ  ກາຌ຋າຌາງຉທົ ຎໄ ຽຌຉາຓຈທ໅ ງຉທົ ຎໄ ຽຌບໄ ຌໃ຋ໄ ປີ ູ຿໅ ຖທ໅  ຃ທາຓຘາຓາຈບະ຋ຍິ າງ຃ທາຓຘາຑຌັ ຈທ໅ ງຘຓົ ຏຌົ ກາຌຊຈົ ຊບງ ນຼ R 2  ຎະເນງຈ຅າກກາຌທ຾ິ ຃າະ  ປໂູ ໅ ຈຂ໅ ະໜາຈ,຋ຈີ ຋າຄ຋ໄ ຉີ ບ໅ ຄກາຌ  ນຼຈຏໄ ບຌ຃ທາຓຘູຌ຾ຘງັ  ແຆຆ໅ ຍັ ຑະງາກບຌດໄ າຄຓຎີ ະຘຈິ ຋ຑິ າຍ  ຌາແຆ຾໅ ຂາົ໅ ແຌກາຌທາຄ຿ຏຌນຼກຈິ ຅ະກາ  ຾ປຈັ ແນຍ໅ ຌັ ຖ຅ຈຎະຘຄົ ນ຾ຼ ຎົາ໅ ໝາງ  ຘາ໅ ຄ຃ທາຓໂຈຎ໅ ຽຍ 2. ກາຌທ຾ິ ຃າະຈທ໅ ງເຎ຿ກຓ SPSS ຉທົ ດໄ າຄ: ຘຓົ ຓຈແນ ໅ y ຾ຎັຌ຋ຈັ ຘະຌະ຃ະຉຉິ ໄ ເປຄປຽຌ x1 ຾ຎັຌຌຈິ ໂຘ຋າຄກາຌປຽຌ x2 ຾ຎັຌຍກ຃ະຖກິ ຉະຑາຍ ຘະ຿ຈຄບບກ x3 ຾ຎັຌ຃ທາຓປກັ ຑທກຑບ໅ ຄເຉ຾ບຄ ຆໄ ຄ຾ກຍັ ຂຓ໅ ຌໂຈຉ໅ ທົ ຅ຄີ ຈໄ ຄັ ຌ:ີ໅ ຖ/ຈ y X1 X2 X3 1 2 2 53 2 1 2 42 3 3 3 64 4 2 2 35 5 5 3 55 6 4 4 46 7 8 6 63 8 7 5 54 9 8 7 47 10 8 6 6 6 11 4 5 3 8 12 3 3 5 10 13 8 7 9 5 14 6 6 8 4 102

15 9 9 8 7 16 10 9 6 5 17 6 10 5 7 18 7 10 5 8 19 9 4 7 9 20 10 6 9 7 ທ຋ິ ຎີ ບ໅ ຌຂຓ໅ ຌ຾ຂາົ໅ ແຌເຎ຿ກຓ  ຉບ໅ ຄຘກຶ ຘາຂຓ໅ ຌກໄ ບຌທໄ າຓກີ າຌ຿຅ກ຿຅ຄ຾ຎັຌເ຃ຄ໅ ຎົກກະຉນິ ໄ ຼຍໄ ຿ຖະ ຉທົ ຎໄ ຽຌຉໄ າຄໃຓ຃ີ ທາຓຘາຑຌັ ກຌັ ນາຼ ງນໜຼ ບ໅ ງ 103

ຘຄັ ຾ກຈ຾ນຌັ ທໄ າຂຓ໅ ຌຂບຄຉທົ ຎໄ ຽຌ຋ຄັ ໝຈົ ຓກີ າຌ຿຅ກ຿຅ຄ຾ຎັຌເ຃ຄ໅ ຎົກກະຉ຿ິ ຖທ໅ ຾ຍໄ ຄີ ຃ທາຓຘາຑຌັ ຉໄ ຓ Descriptive Statistics Mean Std. Deviation N Y 6.00 2.828 20 X1 5.45 2.625 20 X2 5.65 1.785 20 X3 5.75 2.124 20 Correlations Y X1 X2 X3 Y Pearson 1 .716** .636** .289 Correlation Sig. (2-tailed) .000 .003 .216 N 20 20 20 20 X1 Pearson .716** 1 .350 .304 Correlation Sig. (2-tailed) .000 .131 .192 N 20 20 20 20 X2 Pearson .636** .350 1 .017 Correlation Sig. (2-tailed) .003 .131 .942 N 20 20 20 20 104

X3 Pearson .289 .304 .017 1 Correlation Sig. (2-tailed) .216 .192 .942 N 20 20 20 20 **. Correlation is significant at the 0.01 level (2-tailed). ຾ນຌັ ທໄ າ: ຉທົ ຎໄ ຽຌ Y ຿ຖະຉທົ ຎໄ ຽຌ X1 X2 ຓ຃ີ ທາຓຘາຑຌັ ກຌັ ແຌຖະຈຍັ ຎາຌກາຄຍໄ ຾ຊຄີ 1 ແຌກາຌທ຾ິ ຃າະ຾ປາົ ຅ະແຆ໅ Enter Regression Linear ກາຌທ຾ິ ຃າະ຅ະ຾ຂາົ໅ Analyze ກາຌຈາ຾ຌຌີ ກຍັ ຉທົ ຎໄ ຽຌ 105

຾ຖບກ(Method) ຘະຊຉິ ຋ິ ໄ ແີ ຆ຾໅ ຆໄ ຌັ : Stepwise, Backward, Forward ຾ຑໄ ບແນເ໅ ຎ຿ກຓຘະ຿ຈຄກຕາຍ ຉາຓ຃ທາຓຉບ໅ ຄກາຌ຾ຖບກ Plots ຈໄ ຄັ ປູຍຖໄ ູຓຌ:ີ໅ 106

ກຈົ ຎຓ Save ຾ຑໄ ບຍຌັ ຋ກຘະຊຉິ ຉິ ໄ າຄໃ຋ໄ ຉີ ບ໅ ຄກາຌ 107

ນຄຼັ ຅າກຌຌັ໅ ກຈົ OK ຅ະໂຈຏ໅ ຌົ ກາຌທ຾ິ ຃າະຖໄ ຓຌີ໅ Model Summaryb Mode R R Adjusted R Std. Error of l .834a Square Square the 1 .638 .695 Estimate 1.701 a. Predictors: (Constant), X3, X2, X1 b. Dependent Variable: Y Sum of ANOVAa Mean Square Model Squares df F Sig. 3 35.228 12.170 .000b 1 Regressio 105.685 16 n 19 2.895 Residual 46.315 Total 152.000 a. Dependent Variable: Y 108

b. Predictors: (Constant), X3, X2, X1 Coefficientsa Unstandardized Standardize Coefficients d Model B Std. Error Coefficients t Sig. 1 (Constant) Beta -1.230 .236 -2.039 1.658 3.350 .004 X1 .521 3.051 .008 X2 .561 .167 .452 .843 .411 X3 .123 .716 .235 .163 .194 a. Dependent Variable: Y Residuals Statisticsa Predicted Value Minimu Maximu Mean Std. N Residual m m 6.00 Deviation 20 .000 20 Std. Predicted 2.05 9.88 2.358 Value -2.291 2.316 .000 1.561 20 Std. Residual -1.676 1.644 .000 1.000 20 -1.347 1.361 .918 a. Dependent Variable: Y  ກາຌຌາຘະ຾ໜຏີ ຌົ ກາຌທ຾ິ ຃າະ ຘຓົ ຏຌົ ຂບຄກາຌຊຈົ ຊບງ Y  2.039  0.561X1  0.716X 2  0.163X 3 ຾ຘຌັ໅ ຘະ຿ຈຄ 109

110

ຘະ຾ໜ຿ີ ຍຍຉາຉະຖາຄ ຉທົ ຋າຌາງ B S.E Beta t sig Y 0.575 0.138 4.158 0.000 ຉທົ ຃ຄົ ຃ໄ າ -34.301 1.658 0.521 -1.550 0.236 X1 0.561 0.167 0.452 3.350 0.004 X2 0.716 0.235 0.123 3.051 0.008 X3 0.163 0.194 0.843 0.411 R2  .695 , SEE= 1.701, F=12.170, sig of F=.000b (ຘຓົ ຏຌົ ຘາຓາຈ຋າຌາງໂຈ໅ 69.5%) 111

ຍຈົ ຋ໄ ີ 10 ກາຌ຋ຈົ ຘບຍຘະຊຉິ ິ Nonparametric ແຌກາຌທໂິ ຅຋ໄ ທົ ໂຎ ເຈງຎົກກະຉຏິ ູທ໅ ໂິ ຅ຍໄ ຘາຓາຈ຾ກຍັ ຂຓ໅ ຌ຅າກກໄ ຓຎະຆາກບຌ຋ຄັ ໝຈົ ຾ຑໄ ບຓາຘກຶ ຘາໂຈ຅໅ ໄ ຄ ຉບ໅ ຄຓກີ າຌຘໄ ຓຓາ຾ບຌີ໅ ທໄ າກໄ ຓຉທົ ດໄ າຄ຾ຑໄ ບ຾ກຍັ ຂຓ໅ ຌຓາ຋າກາຌທ຾ິ ຃າະ຿ຖທ໅ ບາ໅ ຄບຄີ ຏຌົ ໂຈປ໅ ຍັ ໂຎງຄັ ກໄ ຓຎະຆາກບຌ ຘະຊຉິ ຋ິ ໄ ແີ ຆປ໅ ບ໅ ຄທໄ າຘະຊຉິ ບິ າ໅ ຄບຄີ ນຼຘະຊຉິ ຑິ າຕາຓ຾ີ ຉີ (parametric statistics) ກາຌຌາແຆຘ໅ ະຊຉິ ຿ິ ຍຍຌ຅ີ໅ ະຓຂີ ໅ ຉກົ ຖຄົ ຾ຍບ໅ ຄຉຌົ໅ ຈໄ ຄັ ຌ:ີ໅ 1. ກໄ ຓຉທົ ດໄ າຄໂຈຓ໅ າ຅າກກາຌຘໄ ຓຎະຆາກບຌ຋ໄ ຓີ ກີ າຌ຿຅ກ຿຅ຄ຿ຍຍຎົກກະຉິ 2. ຂຓ໅ ຌດໄ ູແຌຓາ ກາຌທຈັ ກາຌ຅ຈັ ຾ຎັຌນທໄ າຄ (Interval scale)ຂຌຶ໅ ໂຎ ຿ຉໄ ກາຌທໂິ ຅ຍາຄ຾ຖໄ ບຄຍໄ ຘາຓາຈຎະຉຍິ ຈັ ຉາຓຂຉ໅ ກົ ຖຄົ ຾ຍບ໅ ຄ ຉຌົ ຂາ໅ ຄ຾຋ຄີ ໂຈ໅ ຅ໄ ຄໂຈ຃໅ ຌົ໅ ຃ຈີ ນາທ຋ິ ຿ີ ກໂ໅ ຂຈທ໅ ງກາຌຌາແຆຘ໅ ະຊິຉບິ ກີ ຿ຍຍໜໄ ຄຆໄ ຄປບ໅ ຄທໄ າຘະຊຉິ ິ Nonparametric statistics ຆໄ ຄຘາຓາຈຎຽຍ຋ຽຍຘບຄຘະຊຉິ ຈິ ໄ ຄັ ຉໄ ໂຎຌີ໅ ຘະຊຉິ ິ parametric statistics ຘະຊຉິ ິ Nonparametric statistics - ຉທົ ຎໄ ຽຌ຾ຎັຌ Interval scale ຂຌ໅ ໂຎ - ຉທົ ຎໄ ຽຌ຾ຎັຌ Ordinal scale ຂຌ໅ ໂຎ - ຂຓ໅ ຌ຿຅ກ຿຅ຄ຾ຎັຌເ຃ຄ໅ ຎົກກະຉິ - ຂຓ໅ ຌ຿຅ກ຿຅ຄຍໄ ຾ຎັຌເ຃ຄ໅ ຎົກກະຉິ - ຎຽຍ຋ຽຍ຃ໄ າຘະ຾ຖໄ ງ 2 ກໄ ຓ - ຎຽຍ຋ຽຍຓຈັ ຊະງະຊາຌ 2 ກໄ ຓ - ບາຌາຈ຋ຈົ ຘບຍຉໄ າກທໄ າ - ບາຌາຈ຋ຈົ ຘບຍຘຄກທໄ າ  ກາຌຎຽຍ຋ຽຍຘະຊຉິ ຋ິ ໄ ແີ ຆ໅ ກໄ ຓຉທົ ດໄ າຄ ຘະຊຉິ ິ ຘະຊຉິ ິ One sample parametric z-test Nonparametric t-test Chi-square Kolmogorov-smirnov Runs test Sign test Binomial Wilcoxon signed-ranks test Cox-start test for trend 2- independent z-test Fisher exact Median test sample t-test Probability test Mann-whitney U test Chi-square Kolmogorov-smirnov 2 sample Wald-wolfowitz run test Mood test Siegel-Turkey test moses 2- related sample t-test McNemar Sign test k- independent ANOVA Wilcoxon Matched pairs Marginal Homogeneity test Chi-square Kruskal-wallis one-way ANOVA 112

sample Cochran Q Jonckheere-Terpstra test k- related sample ANOVA Median test Friedman Two-way ANOVA Durbin test for BIBD ກາຌທ຾ິ ຃າະຂຓ໅ ຌ຅ະຌາຘະ຾ໜີ 5 ກຖະຌຉີ ໄໂຎຌ:ີ 10.1 ກາຌ຋ຈົ ຘບຍກຖະຌກີ ໄ ຓຉທົ ດໄ າຄຈຽທ  ຘະຊຉິ ຋ິ ໄ ແີ ຆ຅໅ ະຌາຘະ຾ໜກີ ຖະຌແີ ຆ໅ Chi-square test ຅ຈຎະຘຄົ ຂບຄໂ຃ໄ ຘະ຿຃ໄ 1. ຋ຈົ ຘບຍ຃ທາຓກຓົ ກຌ (Goodness of Fit Test) 2. ຋ຈົ ຘບຍ຃ທາຓ຾ຎັຌ຾ບກະຑາຍ (Test for Homogeneity) 3. ຋ຈົ ຘບຍ຃ທາຓຘາຑຌັ ,ກໄ ຽທຂບ໅ ຄນຼ຃ທາຓ຾ຎັຌບຈິ ຘະນຼະ(Test for Association or Independence)  ຂຉ໅ ກົ ຖຄົ ຾ຍບ໅ ຄຉຌົ໅ : 1. ຉທົ ຎໄ ຽຌຉບ໅ ຄຓຖີ ະຈຍັ ກາຌທຈັ ຿ຍຍກໄ ຓນຖຼ າຈຍັ 2. ຉທົ ຎໄ ຽຌ຋ຄັ ຘບຄຉບ໅ ຄຓບີ ຈິ ຘະນຼະຉໄ ກຌັ 3. ຂຓ໅ ຌຉບ໅ ຄດໄ ູແຌປູຍຉາຉະຖາຄ຃ທາຓຊໄ ີ o ຉທົ ດໄ າຄ: ຉທົ ຎໄ ຽຌກໄ ຓ ຉທົ ຎໄ ຽຌຖາຈຍັ ຾ຑຈ:ຆາງ,ງຄີ ຾ກຈ: A ,B+, B ,C+, C, D+, D, F ຿ຌທຑຌັ ຾ຂາົ໅ : ຉາຂຽຈ,ຎາ໅ ຌຖຓົ ,... ກາຌຘກຶ ຘາ: ບະຌຍາຌ,ຎະຊຓົ ,ຓຈັ ຋ະງຓົ ,... ງໄ ນີ ຖ໅ ຈົ : ເຉເດຉາ,ປບຌຈາ,..... ຆຌັ໅ ຋າຄ຋ະນາຌ:ຌາງຘຍິ ,ຌາງປບ໅ ງ,ຌາງຑຌັ ...  ແຌກາຌ຃ຈີ ໂຖໄ ຅ະຘະ຾ໜຘີ ະ຾ຑາະ ກຖະຌີ Nonparametric ຓຘີ ູຈ຃ຈີ ໂຖໄ ຈໄ ຄັ ຌ:ີ໅   x2  k Oi  Ei 2 Ei1 i Oi ຾ຎັຌ຃ທາຓຊໄ ຋ີ ໄ ໂີ ຈ຅໅ າກກາຌຖທຍຖທຓຂຓ໅ ຌ຅ຄີ ແຌກໄ ຓ i Ei  Npi N ຿ຓໄ ຌ຅າຌທຌຂຓ໅ ຌ pi ຃ທາຓໜາ໅ ຅ະ຾ຎັຌຂບຄກາຌ຾ກຈີ ຾ນຈກາຌແຌກໄ ຓ຋ໄ ີ i o ຉທົ ດໄ າຄ: ຌກັ ຘກຶ ຘາຓາຘບຍ຾ຂາົ໅ ທ຋ິ ະງາໂຖ຃ູນຼທຄຌາ໅ ຋າ຅າຌທຌ 126 ຃ຌົ ຆໄ ຄຓາ຅າກ຾ຓບຄຉໄ າຄໃ 6 ຾ຓບຄ ຾ຆໄ ຌັ : ຾ຓບຄຖບຄ,຾ຓບຄຘຄີ ,຾ຓບຄນຼທຄຌາ໅ ຋າ,຾ຓບຄຑູ຃າ,຾ຓບຄຌາ຿ຖ ຿ຖະ຾ຓບຄນທ໅ ງຆາງ ຅າຌທຌ 25, 18, 20, 12, 27, ຿ຖະ 24 ຉາຓຖາຈຍັ ຏູທ໅ ໂິ ຅ດາກປູທ໅ ໄ າ຅າຌທຌຏູ຾໅ ຂາົ໅ ຘບຍ຋ໄ ຓີ າ຅າກ຿ຉໄ ຖະ຾ຓບຄ຿ຉກຉໄ າຄກຌັ ດໄ າຄຓ຃ີ ທາຓຘາ຃ຌັ ຋າຄຘະຊຉິ ນິ ຼຍໄ ? o ທ຋ິ ຃ີ າຌທຌ: 113

 ຉຄັ໅ ຘຓົ ຓຈຊາຌ H0 : Oi  Ei ຅າຌທຌຏູ຾໅ ຂາົ໅ ຘບຍ຋ໄ ຓີ າ຿ຉໄ ຖະ຾ຓໄ ບຄຍໄ ຿ຉກຉໄ າຄກຌັ H1 : Oi  Ei ຅າຌທຌຏູ຾໅ ຂາົ໅ ຘບຍ຋ໄ ຓີ າ຿ຉໄ ຖະ຾ຓໄ ບຄ຿ຉກຉໄ າຄກຌັ  ຾ຂຈທກິ ຈິ ຅າກຉາຉະຖາຄໂ຃ໄ ຘະ຿຃ໄ ກຖະຌີ α=0.05 df=5 ຅ະໂຈ ໅ x2  11.07 0.05  ຾ຂຈທກິ ຈິ ຉບ໅ ຄຓ຃ີ ໄ າ x2  11.07 0.05 o ຃ທາຓຊໄ ຋ີ ໄ ຃ີ າຈນທຄັ ຿ຓໄ ຌ Ei  126  21 6   o x2  k Oi  Ei 2 Ei1 i ຿຋ຌຉາຓຘູຈຂາ໅ ຄ຾຋ຄີ ຅ະໂຈ໅ x2  25  212  18  212  20  212  12  212  27  212  24  212   7.24   21    ຘະນຍຼ ຏຌົ : ຃ໄ າໂ຃ໄ ຘະ຿຃ໄ ຋ໄ ຃ີ າຌທຌໂຈ໅ 7.24 < 11.07 (຃ໄ າໂ຃ໄ ຘະ຿຃ໄ ແຌຉາຉະຖາຄ)ຈໄ ຄັ ຌຌັ໅ ຏຌົ ກາຌ຋ຈົ ຘບຍ ຅ໄ ຄຎະຉ຾ິ ຘຈ H1 ຿ຖະງບຓປຍັ H0 ຘະນຼຍໂຈທ໅ ໄ າ຅າຌທຌຏູ຾໅ ຂາົ໅ ຘບຍ຅າກ຾ຓບຄຉໄ າຄໃຍໄ ຿ຉກຉໄ າຄກຌັ .  ກຖະຌແີ ຆເ໅ ຎ຿ກຓ SPSS 114

ກໄ ບຌ຅ະທ຾ິ ຃າະໂ຃ໄ ຘະ຿຃ໄ ຾ປາົ ຉບ໅ ຄ຾ໜຌັ໅ ຌາ໅ ໜກັ ຂບຄ຾຃ຈັ ຋ໄ ຾ີ ປາົ ຅ະທ຾ິ ຃າະກໄ ບຌ 115

຾ຓໄ ບ຾ໜຌັ໅ ຌາ໅ ໜກັ ຂບຄ຾຃ຈັ ຿ຖທ໅ ຾ປາົ ຘາຓາຈທ຾ິ ຃າະໂ຃ໄ ຘະ຿຃ໄ ໂຈ໅ 116

sing distric long Observed Expected Residua namtha phoukh N Nl a 25 21.0 4.0 nalia 18 21.0 -3.0 hoisine 20 21.0 -1.0 Total 12 21.0 -9.0 27 21.0 6.0 24 21.0 3.0 126 Test Statistics Chi-Square distric 7.238a df 5 Asymp. Sig. .204 117

a. 0 cells (0.0%) have expected frequencies less than 5. The minimum expected cell frequency is 21.0. ຘະນຼຍຏຌົ : ຾ນຌັ ທໄ າ Sig=0.204 > 0.05 ໝາງທໄ າຍໄ ຆກິ (No sig) ຅ໄ ຄຎະຉ຾ິ ຘຈ H1 ງບຓປຍັ H0 ຘະນຼຍໂຈທ໅ ໄ າ຅າຌທຌຏູ຾໅ ຂາົ໅ ຘບຍ຅າກ຾ຓບຄຉໄ າຄໃຍໄ ຿ຉກຉໄ າຄກຌັ . 10.2 ກາຌ຋ຈົ ຘບຍກຖະຌກີ ໄ ຓຉທົ ດໄ າຄຘບຄກໄ ຓ຋ໄ ຘີ າຑຌັ ກຌັ ຾ຎັຌກາຌ຋ຈົ ຘບຍກາຌຎໄ ຽຌ຿ຎຄເຈງຑ຅ິ າຖະຌາ຃ທາຓ຿ຉກຉໄ າຄຖະນທໄ າຄຂຓ໅ ຌແຌກາຌທຈັ (ກາຌ຋ຈົ ຖບຄ ຘບຄ ຃ຄັ໅ ) ຂບຄກໄ ຓຉທົ ດໄ າຄຈຽທກຌັ (ກໄ ບຌ-ນຼຄັ ) ແຌກາຌ຋ຈົ ຘບຍແຆຘ໅ ູຈ x2   A  D 12 A D A ກໄ ບຌ຾ຎັຌ + ຿ຖະ ນຄຼັ ຾ຎັຌ - (ຓກີ າຌຎໄ ຽຌ຿ຎຄ) B ກໄ ບຌ຾ຎັຌ + ຿ຖະ ນຼຄັ ຾ຎັຌ + (ຍໄ ຓກີ າຌຎໄ ຽຌ຿ຎຄ) C ກໄ ບຌ຾ຎັຌ - ຿ຖະ ນຄຼັ ຾ຎັຌ - (ຍໄ ຓກີ າຌຎໄ ຽຌ຿ຎຄ) D ກໄ ບຌ຾ຎັຌ - ຿ຖະ ນຄຼັ ຾ຎັຌ + (ຓກີ າຌຎໄ ຽຌ຿ຎຄ) o ຉທົ ດໄ າຄ 1: ແຌກາຌ຾ຐິກບຍົ ປຓົ ຓຏີ ູ຾໅ ຂາົ໅ ປໄ ທຓ 100 ຃ຌົ ຏູ຅໅ ຈັ ກາຌບຍົ ປຓົ ໂຈ຋໅ ຈົ ຘບຍກໄ ບຌ຿ຖະນຼຄັ ໂຈປ໅ ຍັ ຏຌົ ຈໄ ຄັ ຌີ໅ ຏູ຾໅ ຂາົ໅ ບຍົ ປຓົ 43 ຃ຌົ ຓ຃ີ ະ຿ຌຌຏໄ າຌ຾ກຌ຋ຄັ ກໄ ບຌ຿ຖະນຄຼັ , 31 ຃ຌົ ຍໄ ຏໄ າຌ຾ກຌກໄ ບຌບຍົ ປຓົ ຿ຉໄ ຏໄ າຌ຾ກຌນຄຼັ ບຍົ ປຓົ , 12 ຃ຌົ ຍໄ ຏໄ າຌ຾ກຌ຋ຄັ ກໄ ບຌ຿ຖະນຄຼັ , 14 ຃ຌົ ຏໄ າຌກໄ ບຌ຿ຉໄ ຍໄ ຏໄ າຌນຼຄັ ທ຋ິ ຃ີ າຌທຌ:  ຘຓົ ຓຈຊາຌ: H0 : PA  PD H1 : PA  PD 118

຿຋ຌຉາຓຘູຈ຃ຈີ ໂຖໄ ຅ະໂຈ໅ x2   A  D 12 A D x2  14  31 12  256  5.689 14  31 45 o ຃ໄ າທກິ ຈິ ຅າກຉາຉະຖາຄ຿ຓໄ ຌ x2  3.84 < 5.689 ຅ໄ ຄຎະ຅຾ິ ຘຈ H0 ງບຓປຍັ H1 0.05,1 ຘະນຼຍຏຌົ ທໄ າ຃ທາຓປູຂ໅ ບຄຏູ຾໅ ຂາົ໅ ບຍົ ປຓົ ກໄ ບຌກໄ ບຌ຿ຖະນຄຼັ ບຍົ ປຓົ ຓກີ າຌຎໄ ຽຌ຿ຎຄ.  ແຆເ໅ ຎ຿ກຓ SPSS ຘະຊຉິ ຋ິ ໄ ແີ ຆ຿໅ ຓໄ ຌ McNemar ກາຌ຃ຂີ ຓ໅ ຌ 119

ກາຌທ຾ິ ຃າະ 120

after & befor befor after pass notpass pass notpass 43 14 31 12 Test Statisticsa after & befor N 100 Chi-Squareb 5.689 Asymp. Sig. .017 a. McNemar Test b. Continuity Corrected o ຅າກຏຌົ ກາຌທ຾ິ ຃າະໂຈ຃໅ ໄ າ Sig=0.017 < 0.05 ຅ໄ ຄຎະ຅຾ິ ຘຈ H0 ງບຓປຍັ H1 ຘະນຼຍຏຌົ ທໄ າ຃ທາຓປູຂ໅ ບຄຏູ຾໅ ຂາົ໅ ບຍົ ປຓົ ກໄ ບຌກໄ ບຌ຿ຖະນຼຄັ ບຍົ ປຓົ ຓກີ າຌຎໄ ຽຌ຿ຎຄ. 121

10.3 ກາຌ຋ຈົ ຘບຍກຖະຌກີ ໄ ຓຉທົ ດໄ າຄຘບຄກໄ ຓ຋ໄ ຾ີ ບກະຖາຈກຌັ ຾ຎັຌທ຋ິ ກີ າຌ຋ຈົ ຘບຍ຾ຑໄ ບຉບ໅ ຄກາຌດາກປູທ໅ ໄ າກໄ ຓຉທົ ດໄ າຄ 2 ກໄ ຓຓຖີ ກັ ຘະຌະນຼ຃ຌຘຓົ ຍຈັ ຂບຄກໄ ຓ຿ຉກຉໄ າຄກຌັ ຅ະຓຏີ ຌົ ຾ປຈັ ແນ຾໅ ກຈີ ຃ທາຓ຿ຉກຉໄ າຄແຌຖກັ ຘະຌະ຋ໄ ຘີ ຌົ ແ຅ຘກຶ ຘານຼງໄ ?  ຘະຊຉິ ຋ິ ໄ ແີ ຆ໅ r   x2  c Oij  Eij 2 Eij df= (r-1)(c-1) i1 j1 Oij ຿ຓໄ ຌ຃ທາຓຊໄ ຋ີ ໄ ໂີ ຈ຅໅ າກກາຌຖທຍຖທຓຂຓ໅ ຌ຅ຄີ ແຌ຿ຊທ຋ໄ ີ I ຿ຖະຊຌັ ຋ໄ ີ j Eij ຿ຓໄ ຌ຃ທາຓຊໄ ຋ີ ໄ ໜີ າ໅ ຅ະ຾ຎັຌແຌ຿ຊທ຋ໄ ີ i ຿ຖະຊຌັ ຋ໄ ີ j Eij  ni  n j N N ຿ຓໄ ຌ຅າຌທຌຂຓ໅ ຌ ni ຿ຓໄ ຌ຅າຌທຌຂຓ໅ ຌແຌ຿ຊທ຋ໄ ີ i nj ຿ຓໄ ຌ຅າຌທຌຂຓ໅ ຌແຌຊຌັ ຋ໄ ີ j o ຉທົ ດໄ າຄ. ຏູທ໅ ໂິ ຅ຉບ໅ ຄກາຌດາກປູທ໅ ໄ າ ຌກັ ຘກຶ ຘາ຾ຑຈຆາງ ຿ຖະ ງຄີ ຓ຃ີ ທາຓທຉິ ກົ ກຄັ ທຌົ ແຌກາຌປຽຌ຿ຉກ ຉໄ າຄກຌັ ນຍຼ ໄ ຅ໄ ຄໂຈ຾໅ ກຍັ ຂຓ໅ ຌໂຈຏ໅ ຌົ ຈໄ ຄັ ຌ:ີ໅ ຾ຑຈ ຃ທາຓທຉິ ກົ ກຄັ ທຌົ ຘຄ ຉໄ າ ຆາງ 62 48 ງຄີ 73 42  ຘຓົ ຓຈຊາຌ: H0 : PA  PB ຃ທາຓທຉິ ກົ ກຄັ ທຌົ ຂບຄ຾ຑຈຆາງ຿ຖະງຄີ ຍໄ ຿ຉກຉໄ າຄກຌັ H1 : PA  PB ຃ທາຓທຉິ ກົ ກຄັ ທຌົ ຂບຄ຾ຑຈຆາງ຿ຖະງຄີ ຿ຉກຉໄ າຄກຌັ ກາຌ຃ຂີ ຓ໅ ຌ 122

ກາຌທ຾ິ ຃າະ 123

ນຼຄັ ຅າກຌຌັ໅ ກຈົ ok ຏຌົ ກາຌທ຾ິ ຃າະ຅າກເຎ຿ກຓ 124

gender * vitoc Crosstabulation vitoc high low Total 110 gender man Count 62 48 110.0 115 Expected 66.0 44.0 115.0 Count 225 225.0 woman Count 73 42 Expected 69.0 46.0 Count Total Count 135 90 Expected 135.0 90.0 Count Value Chi-Square Tests Exact Sig. Exact Sig. 1.186a (2-sided) (1-sided) Asymptotic Pearson Chi-Square Significance .170 Continuity df (2-sided) Correctionb 1 .276 Likelihood Ratio Fisher's Exact Test .908 1 .341 Linear-by-Linear Association 1.186 1 .276 N of Valid Cases .281 1.181 1 .277 225 a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 44.00. b. Computed only for a 2x2 table o ຘະນຼຍ: ຅າກຏຌົ ກາຌທ຾ິ ຃າະໂ຃ໄ ຘະ຿຃ໂຈ຃໅ ໄ າ Sig. (2-sided)=0.281 > 0.05 ຅ໄ ຄຎະ຅ໄ ຄ຾ຘຈ H1 ງບຓປຍັ H0 125

ຘະນຍຼ ຏຌົ ໂຈທ໅ ໄ າ຃ທາຓທຉິ ກົ ກຄັ ທຌົ ຂບຄ຾ຑຈຆາງ຿ຖະ຾ຑຈງຄີ ຍໄ ຿ຉກຉໄ າຄກຌັ . 10.4 ກາຌ຋ຈົ ຘບຍກຖະຌກີ ໄ ຓຉທົ ດໄ າຄນາຼ ງກທໄ າຘບຄກໄ ຓ຋ໄ ຘີ າຑຌັ ກຌັ ແຌກຖະຌກີ ໄ ຓຉທົ ດໄ າຄ k ກໄ ຓຓ຃ີ ທາຓຘາຑຌັ ກຌັ ນ຅ຼ ຍັ ຃ໄ ູ (Matched) ເຈງຖກັ ຘະຌະຂບຄຂຓ໅ ຌຘາຓາຈແນບ໅ ຌັ ຈຍັ ໂຈ.໅  ຘະຊຉິ ຋ິ ໄ ແີ ຆ:໅ 12 k  3nk  1 i1 nkk 1 xr2 Ri2 n ຂະໜາຈກໄ ຓຉທົ ດໄ າຄ຿ຉໄ ຖະກໄ ຓ k ຿ຓໄ ຌ຅າຌທຌກາຌ຋ຈົ ຖບຄນ຅ຼ າຌທຌກໄ ຓ຋ຄັ ໝຈົ Ri ຿ຓໄ ຌຏຌົ ຖທຓຂບຄບຌັ ຈຍັ ແຌກໄ ຓຉທົ ດໄ າຄ຋ໄ ີ i 10.5 ກາຌ຋ຈົ ຘບຍກຖະຌກີ ໄ ຓຉທົ ດໄ າຄນາຼ ງກທໄ າຘບຄກໄ ຓ຋ໄ ຾ີ ບກະຖາຈກຌັ ແຌກາຌ຋ຈົ ຘບຍໂ຃ໄ ຘະ຿຃ໄ ແຌກຖະຌນີ ຼາງກທໄ າຘບຄກໄ ຓ຋ໄ ຾ີ ບກະຖາຈກຌັ ຅ະຓຖີ ກັ ຘະຌະ຿ຍຍຈຽທກຌັ ກຍັ ກາຌ຋ຈົ ຘບຍກຖະຌຘີ ບຄກໄ ຓ຋ໄ ຾ີ ບກະຖາຈຉໄ ກຌັ o ຉທົ ດໄ າຄ: ແຌກາຌກທຈຘບຍ຾ງາົ ທະຆຌົ ຿ຖະໂທໜໄ ຓ຅າຌທຌ 370 ຃ຌົ ກໄ ຽທກຍັ ດາ຾ຘຍຉຈີ ໂຈຏ໅ ຌົ ຈໄ ຄັ ຌ:ີ໅ ຋ໄ ດີ ໄ ູບາໂຘ ກາຌຉຈິ ດາ຾ຘຍຉຈິ ຍໄ ຉຈິ ດາ ຉຈິ ດາ 165 ດໄ ູກຍັ ຃ບຍ຃ທົ (ຑໄ ຿ຓໄ ) 44 ດໄ ູກຍັ ຑໄ ຌີ ບ໅ ຄ 18 11 ດໄ ູຉາຓຖາຑຄັ 27 220 105 ຖທຓ 150 ທ຋ິ ກີ າຌ຃າຌທຌເຈງແຆເ໅ ຎ຿ກຓ:  ຘຓົ ຓຈຊາຌ: H0 : PA  PB  Pc ກາຌຉຈິ ດາ຿ຉໄ ຖະ຾ຂຈຍໄ ຿ຉກຉໄ າຄກຌັ H1 : PA  PB  Pc ກາຌຉຈິ ດາ຿ຉໄ ຖະ຾ຂຈ຿ຉກຉໄ າຄກຌັ ກາຌ຃ຂີ ຓ໅ ຌ 126

ກາຌທ຾ິ ຃າະ: 127

128

ນຄຼັ ຅າກຌຌັ໅ ກຈົ OK ໂຈຏ໅ ຌົ ກາຌທ຾ິ ຃າະ thiuasay * kantit Crosstabulation kantit tit nottit Total thiuasa parend Count 18a 165b 183 y Expected Count 74.2 108.8 183.0 Relative Count 27a 44a 71 s Expected Count 28.8 42.2 71.0 Alone Count 105a 11b 116 Expected Count 47.0 69.0 116.0 Total Count 150 220 370 Expected Count 150.0 220.0 370.0 Each subscript letter denotes a subset of kantit categories whose column proportions do not differ significantly from each other at the .05 level. 129

Chi-Square Tests Pearson Chi-Square Value Asymptotic Likelihood Ratio 191.952a df Significance (2-sided) 214.885 2 .000 2 .000 Linear-by-Linear 187.965 1 .000 Association 370 N of Valid Cases a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 28.78. ຘະນຍຼ ຏຌົ : ຘຄັ ຾ກຈ຾ນຌັ ທໄ າ Significance (2-sided)=0.00 < 0.05 ຅ໄ ຄຎະຉ຾ິ ຘຈ H0 ຿ຖະງບຓປຍັ H1 ຅ໄ ຄ ຘາຓາຈຘະນຼຍໂຈທ໅ ໄ າກາຌຉຈິ ດາ຾ຘຍຉຈິ ຂບຄ຾ງາົ ທະຆຌົ ຿ຖະໂທຖຌ໅ ຋ບີ າໂຘດໄ ູ຿ຉໄ ຖະ຾ຂຈຓ຃ີ ທາຓ຿ຉກຉໄ າຄກຌັ ດໄ າຄຓີ ຃ທາຓຘາ຃ຌັ ຋າຄຘະຊຉິ ຋ິ ໄ ຖີ ະຈຍັ α=0.01 130

ຍຈົ ຋ໄ ີ 11 ກາຌທ຾ິ ຃າະບຄົ ຎະກບຍ (Factor Analysis:FA) ກາຌທ຾ິ ຃າະບຄົ ຎະກບຍ຾ຎັຌທ຋ິ ກີ າຌ຋າຄຘະຊຉິ ຋ິ ໄ ຆີ ໄ ທງແນຌ໅ ກັ ທໂິ ຅ຘາ໅ ຄບຄົ ຎະກບຍ຅າກຉທົ ຎໄ ຽຌນາຼ ງໃຉທົ ເຈງຖທຓກໄ ຓຉທົ ຎໄ ຽຌ຋ໄ ກີ ໄ ຽທຂບ໅ ຄຘາຑຌັ ກຌັ ຾ຎັຌບຄົ ຎະກບຍຈຽທກຌັ ຉທົ ຎໄ ຽຌ຋ໄ ດີ ໄ ູແຌບຄົ ຎະກບຍຈຽທກຌັ ຅ະຓີ ຃ທາຓຘາຑຌັ ກຌັ ນາຼ ງເຈງ຃ທາຓຘາຑຌັ ບາຈ຾ຎັຌໂຈ຋໅ ຄັ ຋າຄຍທກ ຿ຖະ ຋າຄຖຍົ ຘໄ ທຌຉທົ ຎໄ ຽຌ຋ໄ ດີ ໄ ູ຃ຌົ ຖະບຄົ ຎະກບຍ ຅ະຍໄ ຓ຃ີ ທາຓຘາຑຌັ ກຌັ ນຓຼ ຃ີ ທາຓຘາຑຌັ ກຌັ ໜບ໅ ງ ບຄົ ຎະກບຍໜໄ ຄໃ຅ະ຿຋ຌຉທົ ຎໄ ຽຌ຿ຐຄ ບຌັ ຾ຎັຌ຃ຌຖກັ ຘະຌະ຋ໄ ຏີ ູ ໅ ທໂິ ຅ຉບ໅ ຄກາຌຘກຶ ຘາ ກາຌທ຾ິ ຃າະບຄົ ຎະກບຍຓນີ າຼ ງ຿ຍຍ ຿ຉໄ ຎຓ໅ ຾ນຼຓັ໅ ຌ຅ີ໅ ະຘະ຾ໜຑີ ຽຄດໄ າຄຈຽທ 11.1 ກາຌທ຾ິ ຃າະບຄົ ຎະກບຍ຾ຆຄີ ຘານທຼ ຈ (Exploratory Factor Analysis) ເຓ຾ຈທ (Model) ກາຌທ຾ິ ຃າະບຄົ ຎະກບຍ຿ຍໄ ຄຉາຓ຅ຈຎະຘຄົ ຂບຄກາຌທ຾ິ ຃າະບຄົ ຎະກບຍ຿ຍໄ ຄ ໂຈຘ໅ ບຄເຓ຾ຈທ຃ ກາຌທ຾ິ ຃າະບຄົ ຎະກບຍ຾ຆຄີ ຘານຼທຈ ຿ຖະ ກາຌທ຾ິ ຃າະບຄົ ຎະກບຍ຾ຆຄີ ດຌດັຌ ກາຌທ຾ິ ຃າະບຄົ ຎະກບຍ຾ຆຄີ ຘານທຼ ຈຓ຅ີ ຈູ ຎະຘຄົ ຾ຑໄ ບຘານຼທຈ຿ຖະຖະຍບຄົ ຎະກບຍປໄ ທຓ຋ໄ ຘີ າຓາຈບະ຋ຍິ າງ ຃ທາຓຘາຑຌັ ຖະນທໄ າຄຉທົ ຎໄ ຽຌ຋ໄ ຘີ ຄັ ຾ກຈໂຈ໅ ຏຌົ ຋ໄ ໂີ ຈ຃໅ ນຈຼ ຅າຌທຌຉທົ ຎໄ ຽຌ຋ໄ ຘີ ຄັ ຾ກຈໂຈເ໅ ຈງຘາ໅ ຄ຾ຎັຌຉທົ ຎໄ ຽຌແໝໄ ແຌ ປູຍຂບຄບຄົ ຎະກບຍປໄ ທຓ. o ຉທົ ດໄ າຄ: ຘຓົ ຓຈທໄ າ຾ຓໄ ບຌາຉທົ ຎໄ ຽຌຘຄັ ຾ກຈໂຈ໅ 4 ຉທົ ຓາທ຾ິ ຃າະບຄົ ຎະກບຍໂຈບ໅ ຄົ ຎະກບຍ ປໄ ທຓຘບຄບຄົ ຎະກບຍໂຈ຃໅ ໄ າຌາ໅ ໜກັ ບຄົ ຎະກບຍຈໄ ຄັ ຉາຉະຖາຄຖໄ ຓຌີ໅ ຉທົ ຎໄ ຽຌ ຌາ໅ ໜກັ ບຄົ ຎະກບຍ ຾ຎີ຾ຆຌັ ຃ທາຓຏຌັ ຎໄ ຽຌ ຃ທາຓປໄ ທຓກຌັ (communality:h2) F1 F2 F1 F2 0.80 , (3) Z1 0.4 , (1) 0.8 , (1) 0.16 , (2) 0.64 , (2) 0.85 0.58 Z2 0.2 0.9 0.04 0.81 0.73 Z3 0.7 0.3 0.49 0.09 Z4 0.8 0.3 0.64 0.09 ຃ໄ າໂບ຾ກຌ (Eigenvalued) 1.33 , (4) 1.63 , (5) (1) ຿ຓໄ ຌ຃ໄ າຌາ໅ ໜກັ ຂບຄບຄົ ຎະກບຍ Z1 ຾຋ຄີ ບຄົ ຎະກບຍ F1 ຿ຖະ F2 ໝາງ຃ທາຓທໄ າຉທົ ຎໄ ຽຌ Z1 ຓ຃ີ ທາຓຘາຑຌັ ກຍັ ບຄົ ຎະກບຍ F1 ຿ຖະ F2 ຾ຎັຌ 0.4 ຿ຖະ 0.8 ຉາຓຖາຈຍັ (2) ຾ຎັຌ຃ໄ າກາຖຄັ ຘບຄໜາ໅ ໜກັ ບຄົ ຎະກບຍ ໝາງ຃ທາຓທໄ າຉທົ ຎໄ ຽຌ Z1 ບະ຋ຍິ າງ຃ທາຓຏຌັ ຎໄ ຽຌບຄົ ຎະກບຍ F1 ຿ຖະ F2 ໂຈ໅ 16% ຿ຖະ 64% ຉາຓຖາຈຍັ (3) ຾ຎັຌ຃ໄ າ຃ທາຓປໄ ທຓກຌັ ຂບຄຉທົ ຎໄ ຽຌ Z1 ໝາງ຃ທາຓທໄ າບຄົ ຎະກບຍ F1 ຿ຖະ F2 ບະ຋ຍິ າງ ຃ທາຓຏຌັ ຎໄ ຽຌຂບຄຉທົ ຎໄ ຽຌ Z1 ໂຈ໅ 0.80 ຘໄ ທຌບກີ 0.20 ຃຃ທາຓຏຌັ ຎໄ ຽຌຘະ຾ຑາະ (4) ຿ຖະ (5) ຃຃ໄ າໂບ຾ກຌບຄົ ຎະກບຍ F1 ຿ຖະ F2 ນ຃ຼ ທາຓຏຌັ ຎໄ ຽຌຂບຄ F1 ຿ຖະ F2 ຋ໄ ບີ ະ຋ຍິ າງໂຈຈ໅ ທ໅ ງ Z1 , Z2 , Z3 ຿ຖະ Z4 ຆໄ ຄຓ຃ີ ໄ າ຾຋ໄ າົ ກຍັ 1.33 ຿ຖະ 1.63 ຉາຓຖາຈຍັ 131

ເຓ຾ຈທທ຾ິ ຃າະບຄົ ຎະກບຍ຾ຆຄີ ຘານທຼ ຈ  ຂຉ໅ ກົ ຖຄົ ຾ຍບ໅ ຄຉຌົ໅ :  ບຄົ ຎະກບຍປໄ ທຓ຋ກຉທົ ຾ຎັຌ຾ບກະຖາຈຉໄ ກຌັ ນຉຼ ທົ ຎະກບຍປໄ ທຓ຋ກຉທົ ຓ຃ີ ທາຓຘາຑຌັ ກຌັ  ຉທົ ຎໄ ຽຌຘຄັ ຾ກຈໂຈ຋໅ ກຉທົ ໂຈປ໅ ຍັ ບຈິ ຋ຑິ ຌົ ເຈງກຄົ ຅າກ຋ກບຄົ ຎະກບຍ (F)  ຉທົ ຎໄ ຽຌຘຄັ ຾ກຈໂຈ຋໅ ກຉທົ ໂຈປ໅ ຍັ ບຈິ ຋ຑິ ຌົ ຅າກບຄົ ຎະກບຍຘະ຾ຑາະນຼຓ຃ີ ທາຓ຃າຈ຾຃ໄ ບຌຑຽຄຉທົ ຈຽທ (e)  ຃ທາຓ຃າຈ຾຃ໄ ບຌ຋ກຉທົ ຾ຎັຌ຾ບກະຖາຈຉໄ ກຌັ ຿ຖະ຾ຎັຌບຈິ ຘະນຼະ຅າກບຄົ ຎະກບຍ຋ກຉທົ  ຂຌັ໅ ຉບຌກາຌທ຾ິ ຃າະບຄົ ຎະກບຍ  ກາຌຘະກຈັ ບຄົ ຎະກບຍຂຌັ໅ ຉຌົ໅ . (factor extraction): ທ຋ິ ຘີ ະກຈັ ບຄົ ຎະກບຍຌງິ ຓົ ແຆ໅ 2 ທ຋ິ ຃ີ : ກ. ທ຋ິ ີ Component Analysis ຾ໝາະຘາຖຍັ ກາຌທ຾ິ ຃າະ຋ໄ ຉີ ບ໅ ຄກາຌບຄົ ຎະກບຍ຅າຌທຌ ໜບ໅ ງ຋ໄ ຅ີ ະບະ຋ຍິ າງ຃ທາຓຏຌັ ຎໄ ຽຌ຅ບຄຉທົ ຎໄ ຽຌຘຄັ ຾ກຈໂຈ໅ ໂຈນ໅ ຼາງ຋ໄ ຘີ ຈ ຿ຖະ ຏູທ໅ ໂິ ຅ປູທ໅ ໄ າ຃ທາຓຏຌັ ຎໄ ຽຌຘະ຾ຑາະ (U= p + e ) ຓ຃ີ ໄ າໜບ໅ ງ຾ຓໄ ບ຋ຽຍກຍັ ຃ໄ າ຃ທາຓຏຌັ ຎໄ ຽຌ຋ຄັ ໝຈົ ແຌ຾ຍບ໅ ຄຉຌົ໅ ຂບຄກາຌ຃າຌທຌກາຌຈົ ແນ຃໅ ໄ າ຃ທາຓ ປໄ ທຓກຌັ ຾຋ໄ າົ 1 ຂ. ທ຋ິ ີ Common Factor ຾ໝາະຘາຖຍັ ກາຌທ຾ິ ຃າະ຾ຑໄ ບຖະຍຓຉິ ຿ິ ຐຄ (Intent dimension) ນເຼ ຃ຄຘາ໅ ຄ຋ໄ ີ ຾ຎັຌຉທົ ຿຋ຌຂບຄຆຈຂບຄຉທົ ຎໄ ຽຌຘຄັ ຾ກຈເຈງຏູທ໅ ໂິ ຅ຓ຃ີ ທາຓປູ຾໅ ຖໄ ບຄ຃ທາຓຏຌັ ຎໄ ຽຌຘະ຾ຑາະໜບ໅ ງນຼາງ ຿ຖະ ຉບ໅ ຄກາຌກາ຅ຈັ ຃ທາຓຏຌັ ຎໄ ຽຌ຾ນໄ າຼົ ຌບີ໅ ບກໂຎ ຿ຉໄ ທ຋ິ ຌີ ນີ໅ ງຄ໅ ງາກນຼາງກທໄ າ ຅ໄ ຄໂຈປ໅ ຍັ ຃ທາຓຌງິ ຓົ ໜບ໅ ງ  ຾ກຌກາຌກາຌຈົ ຅າຌທຌບຄົ ຎະກບຍຑ຅ິ າຖະຌາ຅າກ:  ຃ໄ າໂບ຾ກຌ (Eigenvalues) ນຼາງກທໄ າ 1  ກາຌຈົ ຅າຌທຌຉທົ ຎະກບຍຖໄ ທຄໜາ໅  ຾ຎີ຾ຆຌັ ຃ທາຓຏຌັ ຎໄ ຽຌຘະຘຓົ 60% ຂຌ໅ ໂຎ  Scree plot ຊາ໅ ຾ຘຌັ໅ ຘະ຿ຈຄ຾ຖໄ ຓີ ຋ໄ ຅ີ ະ຾ຎັຌ຾ຘຌັ໅ ຆໄ ຂະໜາຌກຌັ ກຍັ ຿ກຌຌບຌຊທໄ າ຾ຎັຌ຅າຌທຌບຄົ ຎະກບຍຘຄຘຈ  ກາຌໝຌ຿ກຌບຄົ ຎະກບຍ (factor rotation) ຾ຑໄ ບແນໂ໅ ຈບ໅ ຄົ ຎະກບຍປໄ ທຓ຋ໄ ຆີ ຈັ ຾຅ຌຓີ 2 ທ຋ິ ີ ກ. Orthogonal ຾ຎັຌກາຌໝຌ຿ກຌ຋ໄ ຿ີ ກຌຂບຄບຄົ ຎະກບຍງຄັ ຉຄັ໅ ຘາກກຌັ ໝາງ຃ທາຓທໄ າບຄົ ຎະກບຍ຋ໄ ີ ໂຈ຾໅ ຎັຌບຈິ ຘະນະຼ ຉໄ ກຌັ 132

ຂ. Oblique ຾ຎັຌກາຌໝຌ຿ກຌ຋ໄ ຿ີ ກຌຂບຄບຄົ ຎະກບຍຍໄ ຉຄັ໅ ຘາກກຌັ ຆໄ ຄໝາງ຃ທາຓທໄ າບຄົ ຎະກບຍ຋ໄ ີ ຘະກຈັ ໂຈຓ໅ ຃ີ ທາຓຘາຑຌັ ກຌັ 3. ກາຌຘາ໅ ຄ຃ະ຿ຌຌບຄົ ຎະກບຍ (Factor Score) ຾ຑໄ ບຘາ໅ ຄຉທົ ຎໄ ຽຌ຿ຐຄ຅າກຉທົ ຎໄ ຽຌຘຄັ ຾ກຈໂຈ໅ 4. ກາຌຉຄັ໅ ຆໄ ບຄົ ຎະກບຍແນຘ໅ ໄ ຃ທາຓໝາງ຾ຊຄີ ຉທົ ຎໄ ຽຌ຋ຄັ ໝຈົ ແຌບຄົ ຎະກບຍ  ກາຌທ຾ິ ຃າະບຄົ ຎະກບຍຈທ໅ ງເຎ຿ກຓ SPSS o ຉທົ ດໄ າຄ: ກາຌທໂິ ຅ກໄ ຽທກຍັ ຎັຈໂ຅຋ໄ ຓີ ບີ ຈິ ຋ຑິ ຌົ ຉໄ ກາຌຉຈັ ຘຌີ ແ຅ຆຖ໅ ຈົ ງຌົ ຏູທ໅ ໂິ ຅ໂຈຑ໅ ຅ິ າຖະຌາ຾ຊຄີ ຉທົ ຎໄ ຽຌ຋ໄ ຃ີ າຈທໄ າ຅ະຓບີ ຈິ ຋ຑິ ຌົ ຉໄ ກາຌຉຈັ ຘຌິ ແ຅ແຌກາຌຆຓ໅ າໂຈ໅ 9 ຉທົ ຎໄ ຽຌ຿ຖທ໅ ຘາ໅ ຄ຿ຍຍຘບຍຊາຓແນຏ໅ ູ ໅ ຆຖ໅ ຈົ ຉບຍຈໄ ຄັ ຉາຉະຖາຄຂາ໅ ຄຖໄ ຓ: ຃າຊາຓ ຃ະ຿ຌຌ ຉທົ ຎໄ ຽຌ 1. ຓຘີ ນີ ຼາກນາຼ ງແນ຾໅ ຖບກ 5 4 321 2. ຋ຌັ ຘະໂໝ 3. ຄາຓຘະຈຈຉາ OPIN1 4. ນບ໅ ຄຏເູ ໅ ຈງຘາຌກທ໅ າຄຂທາຄ 5. ຂຍັ ຄໄາງ OPIN2 6. ຉທົ ຊຄັ ແນງໄ ກທ໅ ຄຂທາຄ 7. ຃ໄ າຘບ໅ ຓ຿ຎຄຉໄ າ OPIN3 8. ຎະງຈັ ຌາ໅ ຓຌັ 9. ຂາງຉໄ ໂຈຖ໅ າ຃າຘຄ OPIN4 OPIN5 OPIN6 OPIN7 OPIN8 OPIN9 ຏູທ໅ ໂິ ຅ຖທຍຖທຓຂຓ໅ ຌ຅າກຏູຆ໅ ຖ໅ ຈົ 1267 ຃ຌົ ຿ຖທ໅ ຌາຓາທ຾ິ ຃າະບຄົ ຎະກບຍ ກາຌ຃ຂີ ຓ໅ ຌ 133

ກາຌທ຾ິ ຃າະ  ຾ຖບກ຃າຘໄ ຄັ Analyze – Dimension Reduction – Factor 134

ກຈົ ຎຓ Extraction ຿ຖທ໅ ຾ຖບກຈໄ ຄັ ປູຍ 135

ກຈົ ຎຓ Rotation ຿ຖທ໅ ຾ຖບກຈໄ ຄັ ປູຍ ກຈົ ຎຓ Score ຿ຖທ໅ ຾ຖບກຉາຓປູຍ 136

ນຄຼັ ຅າກຌຌັ໅ ກຈົ OK ຅ະໂຈຏ໅ ຌົ ກາຌທ຾ິ ຃າະຈໄ ຄັ ຌ:ີ໅ Descriptive Statistics Mean Std. Deviation Analysis N OPIN1 3.79 .977 1267 OPIN2 1267 OPIN3 3.69 .642 1267 OPIN4 3.71 .776 1267 OPIN5 1267 OPIN6 3.71 .637 1267 OPIN7 3.62 1.120 1267 OPIN8 1267 OPIN9 3.82 1.250 1267 3.39 .796 3.11 .832 3.82 .876 ຉາຉະຖາຄຌຘີ໅ ະ຿ຈຄ຃ໄ າຘະ຾ຖໄ ງ ຿ຖະ ຘໄ ທຌຍໄ ຽຄ຾ຍຌຓາຈຉະຊາຌຂບຄຉທົ ຎໄ ຽຌ຋ຄັ ໝຈົ ຑບ໅ ຓ຋ຄັ ຍບກ຅າຌທຌກໄ ຓ ຉທົ ດໄ າຄ Correlation Matrix Correlation OPIN1 OPIN1 OPIN2 OPIN3 OPIN4 OPIN5 OPIN6 OPIN7 OPIN8 OPIN9 1.000 -.395 .047 -.099 -.357 -.449 .358 -.232 .051 OPIN2 -.395 1.000 -.183 .256 -.185 -.088 -.144 -.701 .049 .047 -.183 1.000 -.180 -.244 .150 .510 .357 .499 OPIN3 -.099 .256 -.180 1.000 -.019 -.565 -.550 -.495 .432 -.357 -.185 -.244 -.019 1.000 .456 -.274 .380 -.155 OPIN4 -.449 -.088 .150 -.565 .456 1.000 .470 .604 -.374 .358 -.144 .510 -.550 -.274 .470 1.000 .229 -.179 OPIN5 -.232 -.701 .357 -.495 .380 .604 .229 1.000 -.221 .051 .049 .499 .432 -.155 -.374 -.179 -.221 1.000 OPIN6 .000 .048 .000 .000 .000 .000 .000 .035 .000 OPIN7 .048 .000 .000 .000 OPIN8 .000 .000 OPIN9 Sig. (1- OPIN1 .000 .000 .000 .001 .000 .000 .042 tailed) OPIN2 OPIN3 .000 .000 .000 .000 .000 .000 OPIN4 .000 .245 .000 .000 .000 .000 OPIN5 .000 .245 .000 .000 .000 .000 137

OPIN6 .000 .001 .000 .000 .000 .000 .000 .000 OPIN7 OPIN8 .000 .000 .000 .000 .000 .000 .000 .000 OPIN9 .000 .000 .000 .000 .000 .000 .000 .000 .035 .042 .000 .000 .000 .000 .000 .000 ຉາຉະຖາຄຌຘີ໅ ະ຿ຈຄ຃ທາຓຘາຑຌັ ຖະນທໄ າຄຉທົ ຎໄ ຽຌ ຅ຈັ ຉທົ ຎໄ ຽຌ຋ໄ ຓີ ຃ີ ໄ າ຃ທາຓຘາຑຌັ ແກ຃໅ ຽຄກຌັ ດໄ ູແຌບຄົ ຎະກບຍຈຽທ ກຌັ ຾ຆຌັ : OPIN8,OPIN7,OPIN6,OPIN3 ດໄ ູບຄົ ຎະກບຍຈຽທກຌັ OPIN5,OPIN6,OPIN8 ດູບຄົ ຎະກບຍ ຈຽທ OPIN5,OPIN8 ຿ຖະ OPIN9,OPIN3 ດໄ ູບຄົ ຎະກບຍຈຽທ ຿ຍໄ ຄ຾ຎັຌ 4 ບຄົ ຎະກບຍ KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .346 10896.627 Bartlett's Test of Approx. Chi-Square 36 Sphericity df .000 Sig. ຃ໄ າ KMO ຓ຃ີ ໄ າ 0.346 ຘະ຿ຈຄທໄ າຓ຃ີ ທາຓ຾ໝາະຘຓົ ຋ໄ ຅ີ ະທ຾ິ ຃າະບຄົ ຎະກບຍຎາຌກາຄ ຃ໄ າ Bartlett's Test of Sphericity ແນ຃໅ ໄ າ sig = 0.00 ຘະ຿ຈຄທໄ າຓາຉຖຈິ ຘາຎະຘຈິ ຘະນະຘາຑຌັ ຍໄ ຾ຎັຌຓາຉ ຖຈິ ຾ບກະຖກັ (identity matrix) ຉທົ ຎໄ ຽຌ຋ໄ ຓີ ຃ີ ທາຓຘາຑຌັ ກຌັ ຘາຓາຈຌາຓາທ຾ິ ຃າະບຄົ ຎະກບຍໂຈ.໅ Communalities OPIN1 Initial Extraction OPIN2 OPIN3 1.000 .904 OPIN4 1.000 .968 OPIN5 1.000 .942 OPIN6 1.000 .760 OPIN7 1.000 .735 OPIN8 1.000 .915 OPIN9 1.000 .881 1.000 .921 1.000 .879 Extraction Method: Principal Component Analysis. 138

຃ໄ າ Communalities ຾ຎັຌຘຈັ ຘໄ ທຌ຃ທາຓຏຌັ ຎໄ ຽຌຂບຄຉທົ ຎໄ ຽຌ຋ໄ ບີ ະ຋ຍິ າງໂຈຈ໅ ທ໅ ງບຄົ ຎະກບຍປໄ ທຓ຃ໄ າ Communalities ຾ຖໄ ຓີ ຉຌົ໅ ຃ 1.000 ຉາຓນກຼັ ກາຌຂບຄກາຌຘະກຈັ ບຄົ ຎະກບຍ຿ຍຍ Principal Component Analysis Total Variance Explained Initial Eigenvalues Extraction Sums of Rotation Sums of Squared Squared Loadings Loadings Com % of po nent Total Varian Cumula % of % of (1) ce tive % Varianc Cumula Total Varianc Cumulativ 1 2 (2) (3) (5) Total e tive % (6) e e% 3 4 3.037 33.740 33.740 3.037 33.740 33.740 2.436 27.070 27.070 5 6 2.097 23.301 57.040 2.097 23.301 57.040 1.985 22.055 49.125 7 1.446 16.066 73.106 1.446 16.066 73.106 1.850 20.555 69.680 8 9 1.326 14.728 87.834 1.326 14.728 87.834 1.634 18.154 87.834 .530 5.889 93.723 .350 3.884 97.608 .171 1.899 99.507 .028 .309 99.816 .017 .184 100.000 Extraction Method: Principal Component Analysis. (1) ໝາງ຾ຊຄີ ຅າຌທຌບຄົ ຎະກບຍ຋ໄ ຘີ ະກຈັ ໂຈຆ໅ ໄ ຄຓ຅ີ າຌທຌ຾຋ໄ າົ ກຍັ ຉທົ ຎໄ ຽຌ຋ໄ ຌີ າໂຎຘະກຈັ ບຄົ ຎະກບຍ (2) ໝາງ຾ຊຄີ ຃ໄ າໂບ຾ກຌ (eigenvalues) ນຼ຃ທາຓຏຌັ ຎໄ ຽຌຂບຄບຄົ ຎະກບຍ຿ຉໄ ຖະບຄົ ຎະກບຍ຋ໄ ບີ ະ຋ຍິ າງ ໂຈຈ໅ ທ໅ ງຉທົ ຎໄ ຽຌຘຄັ ຾ກຈ຋ກຉທົ ຉທົ ຎໄ ຽຌ຋ໄ ຌີ າຓາຘະກຈັ 9 ຉທົ ຈໄ ຄັ ຌຌັ໅ ຃ທາຓຏຌັ ຎໄ ຽຌ຋ຄັ ໝຈົ 9 ບຄົ ຎະກບຍ຋ໄ 1ີ ບະ຋ຍິ າງ຃ທາຓຏຌັ ຎໄ ຽຌໂຈ໅ 3.037 ຃ຈີ ຾ຎີ຾ຆຌັ ຿ຓໄ ຌ 3.037x100/9 = 33.74 ຆໄ ຄ ຘະ຿ຈຄໂທແ໅ ຌຊຌັ ຋ໄ ີ (3) (4) ຃ທາຓຏຌັ ຎໄ ຽຌຘະຘຓົ ຋ໄ ບີ ະ຋ຍິ າງໂຈຈ໅ ທ໅ ງບຄົ ຎະກບຍ຋ຄັ ນາຼ ງ຾ຆຌັ : ຉທົ ຎໄ ຽຌ຋ຄັ ໝຈົ ບະ຋ຍິ າງ຃ທາຓ ຏຌັ ຎໄ ຽຌຂບຄບຄົ ຎະກບຍ຋ໄ ີ 1 ໂຈ໅ 33.74% ຿ຖະບະ຋ຍິ າງບຄົ ຎະກບຍ຋ໄ ີ 2 ໂຈ໅ 23.301% ຃ທາຓຏຌັ ຎໄ ຽຌ ຘະຘຓົ ຋ໄ ບີ ະ຋ຍິ າງຈທ໅ ງບຄົ ຎະກບຍ຋ໄ 1ີ ຿ຖະ2 ໂຈ໅ 57.040%຿ຖະຉໄ ໃໂຎ (5) ບຄົ ຎະກບຍ຋ໄ ຓີ ີ Eigenvalues ນຼາງກທໄ າ 1 ຓ຅ີ າຌທຌ 4 ບຄົ ຎະກບຍ (6) Eigenvalues ຂບຄບຄົ ຎະກບຍຉໄ າຄໃ຋ໄ ໝີ ຌ຿ກຌ຿ຖທ໅ 139

Scree Plot ຾ຎັຌ຾ຘຌັ໅ ຘະ຿ຈຄ Eigenvalues ຂບຄບຄົ ຎະກບຍ຿ຉໄ ຖະຉທົ ເຈງຖຽຄ຅າກນາຼ ງໂຎນາໜບ໅ ງ ກາຌ ຑ຅ິ າຖະຌາທໄ າຓ຅ີ ກັ ບຄົ ຎະກບຍຘາຓາຈຑ຅ິ າຖະຌາ຅າກກຕາຍ຋ໄ ຾ີ ຖໄ ຓີ ຂະໜາຌກຍັ ຿ກຌຌບຌ຾ຖໄ ຓີ ຅າກບຄົ ຎະກບຍ຋ໄ ີ 5 ບຄົ ຎະກບຍຘຄຘຈ຃ບຄົ ຎະກບຍ຋ໄ ີ 9 Component Matrixa Component 1 23 4 OPIN1 -.096 .724 -.368 -.485 .766 OPIN2 -.520 -.259 -.211 .330 OPIN3 .329 .625 .578 -.109 OPIN4 -.778 -.152 .347 -.264 OPIN5 .329 -.697 .268 .357 OPIN6 .824 -.328 .017 .340 OPIN7 .591 .589 -.261 -.247 OPIN8 .854 -.091 .350 .095 OPIN9 -.420 .379 .741 Extraction Method: Principal Component Analysis. 140

a. 4 components extracted. ຾ຎັຌ຃ໄ າຌາ໅ ໜກັ ບຄົ ຎະກບຍກໄ ບຌກາຌໝຌ຿ກຌບຄົ ຎະກບຍ Rotated Component Matrixa Component 1 23 4 OPIN1 .082 -.915 .240 -.039 OPIN2 -.086 -.041 OPIN3 .348 .214 -.956 .895 OPIN4 -.825 -.024 .136 .148 OPIN5 -.160 -.276 OPIN6 .681 -.023 -.237 -.056 OPIN7 .869 .240 OPIN8 .400 .665 .436 .124 OPIN9 -.463 .648 .164 .809 -.259 .004 .402 .764 -.086 -.051 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 6 iterations. ຾ຎັຌ຃ໄ າຌາ໅ ໜກັ ບຄົ ຎະກບຍນຄຼັ ໝຌ຿ກຌບຄົ ຎະກບຍ຿ຖທ໅ ຾ປຈັ ແນຉ໅ ທົ ຎໄ ຽຌຘາຑຌັ ກຍັ ບຄົ ຎະກບຍແຈບຄົ ຎະກບຍໜໄ ຄຆຈັ ຾຅ຌງໄ ຄີ ຂຌ໅ ກາຌຑ຅ິ າຖະຌາທໄ າຉທົ ຎໄ ຽຌແຈ຃ທຌດໄ ູແຌບຄົ ຎະກບຍແຈຑ຅ິ າຖະຌາ຅າກ຃ໄ າຌາ໅ ໜກັ ຾ກຌີ 0.30 Component Transformation Matrix Component 1 2 3 4 1 .775 .318 .546 -.008 2 .292 -.776 .046 .556 3 -.387 .417 .317 .759 4 .406 .349 -.774 .338 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. ຾ຎັຌຓາຉຖຈິ ຋ໄ ແີ ຆແ໅ ຌກາຌໝຌບຄົ ຎະກບຍ຾ຑໄ ບຎໄ ຽຌ຃ໄ າຌາ໅ ໜກັ ບຄົ ຎະກບຍ 141

ຘະ຿ຈຄຌາ໅ ໜກັ ບຄົ ຎະກບຍຂບຄ຿ຉໄ ຖະບຄົ ຎະກບຍ ຊາ໅ ບຄົ ຎະກບຍຘາຓາຈ຿຋ຌຉທົ ຎໄ ຽຌໂຈດ໅ ໄ າຄຈຉີ ທົ ຎໄ ຽຌຉບ໅ ຄຌາ໅ ໜກັ ບຄົ ຎະກບຍນາຼ ງຊາ໅ ຓຉີ ທົ ຎໄ ຽຌແກ຅໅ ຈ Intersection (0,0,0) ຘະ຿ຈຄທໄ າຉທົ ຎໄ ຽຌ຾ນໄ ຼາົ ຌຌັ໅ ຍໄ ຘາຑຌັ ກຍັ ບຄົ ຎະກບຍແຈ ຾ຖງີ Component Score Coefficient Matrix Component 1 23 4 OPIN1 .026 -.512 .201 -.124 OPIN2 .122 .017 OPIN3 .118 .182 -.593 .552 OPIN4 -.346 .057 .007 .116 OPIN5 -.166 -.112 OPIN6 .269 .046 -.004 .010 OPIN7 .407 .300 .257 .105 OPIN8 .036 .307 -.064 .094 OPIN9 -.224 .515 -.142 -.136 .159 .372 .055 .040 142

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Component Scores. ຃ໄ າ Component Score Coefficient Matrix ແຆຘ໅ າຖຍັ ຘາ໅ ຄຉທົ ຎໄ ຽຌແໝໄ ແນ຾໅ ນບຼ 3 ບຄົ ຎະກບຍ຅າກຘູຈ Fik  Wi1Z1k  Wi2 Z2k  ....  Wip Z pk , k=1,2,3……..,n i=1,2,3…….,m n = ຅າຌທຌຂຓ໅ ຌ m = ຅າຌທຌບຄົ ຎະກບຍ Wij = ຌາ໅ ໜກັ ບຄົ ຎະກບຍຂບຄຉທົ ຎໄ ຽຌ຋ໄ ີ j ແຌຉທົ ຎະກບຍ຋ໄ ີ i Fik = ຃ະ຿ຌຌບຄົ ຎະກບຍຂບຄບຄົ ຎະກບຍ຋ໄ ີ I ຂບຄ case ຋ໄ ີ k 143

ຍຈົ ຋ໄ ີ 12 ກາຌ຋ຈົ ຘບຍຂຉ໅ ກົ ຖຄົ ຾ຍບ໅ ຄຉຌົ໅ 7.2.1.1 ກາຌ຋ຈົ ຘບຍຂຓ໅ ຌທໄ າຓກີ າຌ຿຅ກດາງ຾ຎັຌເ຃ຄ໅ ຎົກກະຉິ ແຆເ໅ ຎ຿ກຓ SPSS ຅ະຎະຉຍິ ຈັ ຈໄ ຄັ ຉໄ ໂຎຌ:ີ໅ ແຆຘ໅ ະຊຉິ ິ Skewness ຃ທາຓ຾ຍ໅ Kurtosis ຃ທາຓເຈໄ ຄ ຃ໄ າຂບຄ຃ທາຓ຾ຍ,໅ ຃ທາຓຈໄ ຄົ ຉບ໅ ຄດໄ ູແຌນທໄ າຄ ± 1.96 ຅ໄ ຄ຾ຎັຌເ຃ຄ໅ ຎົກກະຉິ ຉທົ ດໄ າຄ: ຏູທ໅ ໂິ ຅ດາກປູທ໅ ໄ າກາຌ຿຅ກ຿຅ຄຂບຄ຃ະ຿ຌຌຆຈໜໄ ຄ຾ຎັຌເ຃ຄ໅ ຎົກກະຉນິ ຼຍໄ ຅ໄ ຄໂຈ຾໅ ບາົ ຂຓ໅ ຌຓາ຋າກາຌທ຾ິ ຃າະ ຉທົ ດໄ າຄ: ຂຓ໅ ຌ຃ະ຿ຌຌຌກັ ປຽຌ຋ໄ ຾ີ ກຍັ ໂຈຈ໅ ໄ ຄັ ຖໄ ຓຌ:ີ໅ ຖ/ຈ IQ Score 1 1 26 2 1 41 3 1 28 4 1 92 5 1 14 6 1 16 7 1 29 8 1 31 9 2 41 10 2 26 11 2 19 12 2 59 13 2 82 14 2 86 15 2 45 16 2 37 17 3 36 18 3 39 19 3 59 20 3 27 21 3 87 22 3 99 144

23 3 126 24 3 104 25 1 51 26 1 96 27 1 97 28 1 22 29 1 35 30 1 36 31 1 28 32 1 76 33 2 39 34 2 104 35 2 130 36 2 122 37 2 114 38 2 92 39 2 87 40 2 64 41 3 42 42 3 92 43 3 156 44 3 144 45 3 133 46 3 124 47 3 68 48 3 142 ທ຋ິ ກີ າຌທ຾ິ ຃າະຆໄ ຄຉທົ ຎໄ ຽຌ຋ໄ ຾ີ ປາົ ຉບ໅ ຄກາຌທ຾ິ ຃າະ຿ຓໄ ຌ຃ະ຿ຌຌຂບຄຌກັ ປຽຌທໄ າຓກີ າຌ຿຅ກ຿຅ຄ຾ຎັຌເ຃ຄ໅ ຎົກກະຉນິ ຼ ຍໄ ? ຎບ໅ ຌຂຓ໅ ຌ຾ຂາົ໅ ເຎ຿ກຓ SPSS ຿ຖທ໅ ທ຾ິ ຃າະຉາຓ຃າຘໄ ຄັ ຈໄ ຄັ ຌ:ີ໅ 145

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Statistics 48 score 1 N Valid 69.65 40.482 Missing .433 Mean .343 Std. Deviation -1.048 Skewness .674 Std. Error of Skewness Kurtosis Std. Error of Kurtosis ຏຌົ ກາຌທ຾ິ ຃າະໂຈ຃໅ ໄ າ Skewness = 0.433 < 1.96 ຿ຖະ຃ໄ າ Kurtosis = -1.048 < -1.96 ຾ນຌັ ທໄ າ຃ທາຓ຾ຍ຿໅ ຖະ຃ທາຓເຈໄ ຄດໄ ູແຌ຾ກຌ ຅ໄ ຄຘະນຼຍໂຈທ໅ ໄ າຂຓ໅ ຌຂບຄ຃ະ຿ຌຌຆຈຌຓີ໅ ກີ າຌ຿຅ກດາງດໄ າຄ຾ຎັຌເ຃ຄ໅ ຎົກກະຉິ 147

ຍຈົ ຋ໄ ີ 13 ກາຌທ຾ິ ຃າະປູຍ຿ຍຍກາຌທໂິ ຅຾ຆຄິ ຋ຈົ ຖບຄ 13.1 ປູຍ຿ຍຍ A-B Design ປູຍ຿ຍຍຌ຅ີ໅ ະ຿ຍໄ ຄ຾ຎັຌ 2 ຆໄ ທຄ຃ຆໄ ທຄກໄ ບຌກາຌ຋ຈົ ຖບຄ຅ະປບ໅ ຄຆໄ ທຄຌທີ໅ ໄ າ baseline period ແຌ ຆໄ ທຄຌກີ໅ ໄ ຓຉທົ ດໄ າຄ຅ະຊກຘຄັ ຾ກຈຑຈຉກິ າ຅ຌົ ກໄ ຓຉທົ ດໄ າຄຘະ຿ຈຄຑຈຶ ຉກິ າບບກຓາ຿ຖທ໅ ຍຌັ ຋ກີ ຾ບາົ ໂທ໅ ໂຖງະ຋ໄ ີ 2 ຾ຎັຌ ໂຖງະ຋ໄ ຋ີ າກາຌ຋ຈົ ຖບຄນຼທໄ າແຆຉ໅ ທົ ຎໄ ຽຌ຋ຈົ ຖບຄ treatment period ໂຖງະຌ຅ີ໅ ະຘຄັ ຾ກຈ຾ຎັຌຆໄ ທຄໃເຈງຏູທ໅ ໂິ ຅ ຿ຖທ໅ ຍຌັ ຋ກກາຌຎໄ ຽຌ຿ຎຄຂະຌະ຋ໄໂີ ຈປ໅ ຍັ ຉທົ ຎໄ ຽຌ຋ຈົ ຖບຄຈໄ ຄັ ປູຍ຿ຍຍ  ຉທົ ດໄ າຄ: ກາຌທໂິ ຅ແຌປູຍ຿ຍຍ A-B Design ກໄ ຓຉທົ ດໄ າຄໂຈ຿໅ ກໄ ຌກັ ປຽຌບາຌຍາຌໝທຈບຈຓົ ຘຌິ ຾ຓບຄຌາ໅ ຋າ ຿ຂທຄນທຼ ຄຌາ໅ ຋າ ກໄ ຓຉທົ ດໄ າຄໂຈຓ໅ າ຅າກກາຌຘໄ ຓ຿ຍຍ random ຉທົ ຎໄ ຽຌຉຌົ໅ (treatment) ຆຈບຎະກບຌຐຶກຆບ໅ ຓຈຌົ ຉີ ຉທົ ຎໄ ຽຌຉາຓ ຏຌົ ກາຌຘຄັ ຾ກຈກາຌຎໄ ຽຌ຿ຎຄຂບຄ຾ຈກັ ກາຌທໂິ ຅ແຆ຾໅ ທຖາ 5 ບາ຋ຈີ ເຈງ຿ຍໄ ຄ຾ຎັຌ 2 ໂຖງະໂຈຏ໅ ຌົ ຈໄ ຄັ ຉາຉະຖາຄຖໄ ຓຌ:ີ໅ ຖາຈຍັ ຌກັ ປຽຌ baseline period treatment period 1 21 22 2 20 19 86 6 3 17 15 4 25 30 10 4 4 5 30 27 6 19 27 54 5 7 26 16 8 17 18 13 12 17 9 26 24 10 28 15 13 8 6 87 4 52 5 81 5 14 8 9 96 10 ທ຋ິ ທີ ຾ິ ຃າະຂຓ໅ ຌ  ຉຄັ໅ ຘຓົ ຓຈຊາຌ 148

H0: ຏຌົ ຃ະ຿ຌຌ baseline period ຍໄ ຿ຉກຉໄ າຄ຅າກຏຌົ ຃ະ຿ຌຌ treatment period H1: ຏຌົ ຃ະ຿ຌຌ baseline period ຿ຉກຉໄ າຄ຅າກຏຌົ ຃ະ຿ຌຌ treatment period α = 0.05 ກາຌ຃ຂີ ຓ໅ ຌ: ຎບ໅ ຌຂຓ໅ ຌ຿ຖທ໅ ຈາ຾ຌຌີ ກາຌທ຾ິ ຃າະຈໄ ຄັ ປູຍຉໄໂຎຌ:ີ໅ 149

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