ວິຊາ software

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Within-Subjects Factors Measure: MEASURE_1 factor1 Dependent Variable 1 X1 2 X2 3 X3 4 X4 5 X5 Descriptive Statistics test1 Mean Std. Deviation N test2 test3 22.90 4.677 10 test4 21.30 5.498 10 test5 9.30 3.199 10 5.80 3.225 10 7.10 4.012 10 Multivariate Testsa Effect Value F Hypoth Error df Sig. Partial Eta 104.990b esis df 6.000 .000 Squared facto Pillai's Trace .986 104.990b 4.000 6.000 .000 r1 Wilks' Lambda .014 104.990b 4.000 6.000 .000 .986 4.000 .986 Hotelling's Trace 69.994 .986 Roy's Largest 69.994 104.990b 4.000 6.000 .000 .986 Root a. Design: Intercept Within Subjects Design: factor1 b. Exact statistic 152

Tests of Within-Subjects Effects Measure: MEASURE_1 Type I Sum Mean Partial Eta of Squares Source df Square F Sig. Squared factor1 Sphericity 2668.480 4 667.120 76.897 .000 .895 Assumed Greenhouse- 2668.480 2.347 1137.107 76.897 .000 .895 Geisser Huynh-Feldt 2668.480 3.227 827.032 76.897 .000 .895 Lower-bound 2668.480 1.000 2668.480 76.897 .000 .895 Error(fa Sphericity ctor1) Assumed 312.320 36 8.676 Greenhouse- Geisser 312.320 21.121 14.787 Huynh-Feldt Lower-bound 312.320 29.039 10.755 312.320 9.000 34.702 Tests of Within-Subjects Contrasts Measure: MEASURE_1 Type I Sum Mean Partial Eta of Squares Source factor1 df Square F Sig. Squared factor1 Linear 2218.410 1 146.064 1 2218.410 234.092 .000 .963 Quadratic 231.040 1 Cubic 72.966 1 146.064 13.107 .006 .593 Order 4 85.290 9 Error(fa Linear 100.293 9 231.040 32.612 .000 .784 ctor1) Quadratic 63.760 9 Cubic 62.977 9 72.966 10.427 .010 .537 Order 4 9.477 11.144 7.084 6.997 153

Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average Source Type I Sum df Mean F Sig. Partial Eta of Squares Square .000 Squared Intercept 8817.920 1 8817.920 162.866 .948 Error 487.280 9 54.142 ຌາ຾ບາົ ຃ໄ າ຋ໄ ໂີ ຈຓ໅ າແຘໄ ຉາຉະຖາຄ ຿ນໄ ຼຄ຃ທາຓຏຌັ ຎໄ ຽຌ SS DF MS F sig 76.897* 0.00 Between subject 487.28 9 54.142 Within subject 2668.48 4 667.12 Within cell 312.32 36 8.676 ຏຌົ ຎາກຈົ ທໄ າແນ຃໅ ໄ າ sig < 0.05 ຈໄ ຄັ ຌຌັ໅ ຅ໄ ຄຉບ໅ ຄ຋ຈົ ຘບຍ຾ຎັຌຖາງ຃ໄ ູຉໄ 154

ກຈົ ຎຓ continue and OK Pairwise Comparisons Measure: MEASURE_1 Mean 95% Confidence Interval Difference for Differenceb (I) (I-J) Std. Sig.b Lower Upper factor1 (J) factor1 Error .440 Bound Bound 12 1.600 1.979 .000 13.600* 1.267 .000 -2.876 6.076 3 17.100* 1.345 .000 4 15.800* 1.459 .440 10.735 16.465 5 1.979 .000 21 -1.600 1.247 .000 14.057 20.143 3 12.000* 1.088 .000 4 15.500* 1.645 .000 12.499 19.101 5 14.200* 1.267 .000 31 -13.600* 1.247 .001 -6.076 2.876 2 -12.000* .734 .075 4 3.500* 1.093 .000 9.179 14.821 5 1.345 .000 41 2.200 1.088 .001 13.039 17.961 2 -17.100* .734 .158 3 -15.500* .844 .000 10.478 17.922 5 -3.500* 1.459 .000 51 1.645 .075 -16.465 -10.735 2 -1.300 1.093 .158 3 -15.800* .844 -14.821 -9.179 4 -14.200* 1.839 5.161 -2.200 -.273 4.673 1.300 -20.143 -14.057 -17.961 -13.039 -5.161 -1.839 -3.209 .609 -19.101 -12.499 -17.922 -10.478 -4.673 .273 -.609 3.209 Based on estimated marginal means *. The mean difference is significant at the .05 level. 155

b. Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments). Multivariate Tests Hypoth Partial Eta Squared Pillai's trace Value F esis df Error df Sig. Wilks' lambda .986 4.000 6.000 .000 .986 Hotelling's .014 104.990a 4.000 6.000 .000 .986 trace 104.990a .000 Roy's largest .986 root 69.994 104.990a 4.000 6.000 .000 .986 69.994 104.990a 4.000 6.000 Each F tests the multivariate effect of factor1. These tests are based on the linearly independent pairwise comparisons among the estimated marginal means. a. Exact statistic ຏຌົ ກາຌຎຽຍ຋ຽຍ຃ທາຓ຿ຉກຉໄ າຄ຃ໄ າຘະ຾ຖໄ ງ຾ຎັຌຖາງ຃ໄ ູຑຍົ ທໄ າ ຃ໄ ູຂບຄກາຌທຈັ ຆາ໅ ຾຋ໄ ບ຋ໄ ີ (1 ຿ຖະ 2) , (3 ຿ຖະ 5), (4 ຿ຖະ 5) ຓ຃ີ ໄ າຘະ຾ຖໄ ງຍໄ ຿ຉກຉໄ າຄກຌັ ຌບກ຅າກຌຌັ໅ ຿ຉກຉໄ າຄກຌັ ດໄ າຄຓ຃ີ ທາຓຘາ຃ຌັ ຋າຄຘະຊຉິ ຋ິ ໄ ຖີ ະຈຍັ α = 0.01 ຘະນຍຼ : ຅າກຏຌົ ໂຈປ໅ ຍັ ຆໄ ທຄ baseline period ຿ຖະ treatment period ແນຏ໅ ຌົ ຿ຉກຉໄ າຄກຌັ ຅ໄ ຄຘະນຼຍ ໂຈທ໅ ໄ າ ຉທົ ຎໄ ຽຌ຋ຈົ ຖບຄ (ຆຈ຾຃ໄ ບຄຈຌົ ຉ)ີ ຓຏີ ຌົ ຉໄ ຉທົ ຎໄ ຽຌ຋ໄ ຾ີ ປາົ ຘກຶ ຘາ (ຏຌົ ກາຌຘຄັ ຾ກຈ) 156

ຍຈົ ຋ໄ ີ 14 ກາຌ຅ຈັ ກາຌກຍັ ຉາຉະຖາຄ output ແຌ SPSS 14.1 ກາຌ຅ຈັ ກາຌກຍັ ຉາຉະຖາຄ຋ໄ ທີ ຾ິ ຃າະໂຈ໅ 1. ກາຌເກຎີ (copy): ຉາຉະຖາຄ຋ກໃຉາຉະຖາຄຘາຓາຈ ກບຍຎໂີ ຎທາຄແຘໄ Word ຿ຖະ excel ຘໄ ທຌຖະຍຍົ ຾ຘຌັ໅ ຘະ຿ຈຄກບຍໂຎໂຈ຿໅ ຉໄ ຎໄ ຽຌ຿ຎຄຍໄໂຈ໅ 2. ຉາຉະຖາຄຉໄ າຄໃຘາຓາຈຉຈັ ຿ຎຄໂຈ຃໅ າ໅ ຃ excel 3. ຍຌັ ນາຊາ໅ ຉາຉະຖາຄງາທ຅ະຉບ໅ ຄຈຈັ ຿ຎຄກໄ ບຌ຅ໄ ຄກບຍຎີ ຉທົ ດໄ າຄ: Mauchly's Test of Sphericitya Measure: MEASURE_1 Within Subjects Mauchly's Approx. Chi- df Greenhouse- Epsilonb Effect W Square 9 Sig. Geisser Huynh- Feldt factor1 .167 13.268 .160 .587 .807 Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed depen variables is proportional to an identity matrix. a. Design: Intercept Within Subjects Design: factor1 b. May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected te displayed in the Tests of Within-Subjects Effects table. ຾ນຌັ ທໄ າຉາຉະຖາຄ຾ນບຼ ຂະໜາຈຂບຄ຾຅ງ໅ ຅ໄ ຄຉບ໅ ຄຈຈັ ຿ຎຄກໄ ບຌ຅ໄ ຄກບຍ ກກິ ຂທາແຘໄ ຿ຖທ໅ ຾ຖບກຉາຓປູຍ 157

14.2 ກາຌຉຈັ ຿ຎຄຉາຉະຖາຄ ໜາ໅ ຉໄ າຄແໝໄ ຅ະຎະກຈົ ຂຌ໅ ຆໄ ຄ຾ປາົ ຘາຓາຈຈຈັ ຿ຎຄໂຈ໅ ນຄຼັ ຿ຎຄ຿ຖທ໅ ກຈົ ຒໄ າງ຿ຖະ ຃ຖຈົ ຾ປາົ ຅ະໂຈປ໅ ຍັ ເຉ຋ໄ ຾ີ ປາົ ຿ຎຄ ຿ຖທ໅ ຅າກຌຌັ໅ ກກບຍຉາຓ຋ໄ ຉີ ບ໅ ຄກາຌ Mauchly's Test of Sphericitya Measure: MEASURE_1 Epsilonb Within Approx. Greenho Subjects Effect Mauchl Chi- use- Huynh- Lower- y's W Square df Sig. Geisser Feldt bound factor1 .167 13.268 9 .160 .587 .807 .250 Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix. a. Design: Intercept Within Subjects Design: factor1 b. May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table. ຾ປາົ ກ຅ະໂຈຂ໅ ຓ໅ ຌ຃ຍົ ຊທ໅ ຌຉາຓຉບ໅ ຄກາຌ 158

ຍຈົ ຋ໄ ີ 15 ກາຌທ຾ິ ຃າະຂຓ໅ ຌຌາ຾ຂາົ໅ ຓາແຌປູຍຉາຉະຖາຄ 15.1 ກາຌຌາ຾ບາົ ຂຓ໅ ຌຓາ຅າກ excel ທ຋ິ ກີ າຌ ຾ຑໄ ບແນຘ໅ ະຈທກແນ຅໅ ຈັ ຂຓ໅ ຌ excel ຋ໄ ຉີ ບ໅ ຄກາຌທ຾ິ ຃າະໂທດ໅ ໄ ູ Desktop ຿ຖທ໅ ຎະຉຍິ ຈັ ຈໄ ຄັ ຌີ໅  ຾ຎີຈເຎ຿ກຓ SPSS ຉທົ ດໄ າຄ຾ບກະຘາຌ຋ໄ ດີ ໄ ູ Desktop ຿ຓໄ ຌແຘໄ ຆໄ 3gggg ຿ຖທ໅ ຾ຖບກ 3gggg ຋ໄ ຾ີ ປາົ ຉບ໅ ຄກາຌ຿ຖທ໅ ກຈົ ເບ຾ຑໄ ຌີ 159

຅ະໂຈຂ໅ ຓ໅ ຌ຋ໄ ຉີ ບ໅ ຄກາຌ ຿ຉໄ ຉບ໅ ຄໂຎຎັຍຂຓ໅ ຌດໄ ູຆໄ ບຄ 160

ຎັຍຂຓ໅ ຌແນ຃໅ ຈໄ ຄັ ປູຍຉາ໅ ຄຖໄ ຓ຿ຖທ໅ ຅ໄ ຄທ຾ິ ຃າະຉາຓ຋ໄ ຉີ ບ໅ ຄກາຌ 161

ນຼຄັ ຅າກທ຾ິ ຃າະ຿ຖທ໅ ກຘະນຼຍຏຌົ ຂບຄກາຌທ຾ິ ຃າະຉາຓ຅ຈຎະຘຄົ ຂບຄກາຌທໂິ ຅ 162

຾ບກະຘາຌບາ໅ ຄບຄີ  ຘະຊຉິ ຑິ ຌ໅ ຊາຌຘາຖຍັ ກາຌທໂິ ຅ ປຽຍປຽຄເຈງ ບາ຅າຌ ຿ຈຄ ຋ບຄຘຄີ ຘກົ ປຽຌ 2016-2017  ຘະຊຉິ ທິ ຾ິ ຃າະຘາຖຍັ ກາຌທໂິ ຅຋າຄຘຄັ ຃ຓົ ຘາຈ ຿ຖະ ຑຈຶ ຉກິ າຘາຈ ຾຋ກັ ຌກິ າຌແຆເ໅ ຎ຿ກຓ LISREL ປບຄຘາຈຘະຈາ຅າຌ ຈຕ ຘຑະຓາຈ ບຄັ ຘເຆຈ ປບຄຘາຈຘະຈາ຅າຌ ຈຕ ຘຓົ ຊະທຌີ ທ຅ິ ຈິ ທຌັ ຌາ ຈຕ ຕຈັ ຘະຌກີ ຌ ຑຌີ ເງຑາຌທຈັ ຑຓີ ຃ຄັ໅ ຋ໄ ີ 2 ຑຘ 2552  ຘະຊຉິ ຎິ ະງກ ຘາຖຍັ ກາຌທໂິ ຅຋າຄຘຄັ ຃ຓົ ຘາຈ ຏຘ.ຈຕ ທາເຕ ຾ຑຄຘະນທຈັ ຑຓີ 2553  ຘະຊຉິ ຑິ ຌ໅ ຊາຌຑບ໅ ຓຉທົ ດໄ າຄກາຌທ຾ິ ຃າະຈທ໅ ງເຎ຿ກຓ SPSS ຿ຖະ SAS ປຘ. ຆຈັ ຘະທາຌ ຾ປບຄຎະຑຌັ ຓະນາທ຋ິ ະງາໂຖຂບຌ຿ກໄ ຌ  ຘະຊຉິ ທິ ຾ິ ຃າະ຾ຑໄ ບກາຌທໂິ ຅ Statistical Analysis for research ຘ. ຍຌ຋າ ກຈິ ຅ະຎີຈາຍຖຘິ ຈ ຘ຅. ຑາກທຆິ າຘຘຶ າຘາຈ ຃ະຌະຘຄັ ຃ຓົ ຘາຈ ຿ຖະ ຓະຌຈຘາຈ ຓະນາທ຋ິ ະງາໂຖຓະນຈິ ຌົ  http//www.google.com 163