†Â<Œ^ŠÖ]<Ø’ËÖ]<<<< l]‚â^¹]<°e<ˆééÛjÖ]æ<êÏfŞÖ]<Øé×vjÖ] Discriminant Analysis
ﻣﻘﺪﻣﺔ.1 .16 أﻧﻮاع اﻟﺘﺤﻠﻴﻞ اﻟﻄﺒﻘﻲ.2 .16 SPSS اﻟﺘﺤﻠﻴﻞ اﻟﻄﺒﻘﻲ ﺑﺎﺳﺘﺨﺪام ﻧﻈﺎم.3 .16
) (16ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ
554
) (16ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ
555
]†Â<Œ^ŠÖ]<Ø’ËÖ ]l]‚â^¹]<°e<ˆééÛjÖ]æ<êÏfŞÖ]<Øé×vjÖ Discriminant Analysis .1 .16ﻣﻘﺪﻣﺔ : ﻓﻲ ﺠﻤﻴﻊ ﺍﻟﺘﺠﺎﺭﺏ ﺍﻟﻤﺫﻜﻭﺭﺓ ﻓﻲ ﺃﻤﺜﻠﺔ ﺍﻟﻔﺼﻭل ﺍﻟﺴﺎﺒﻘﺔ )ﺒﺎﺴﺘﺜﻨﺎﺀ ﺍﻟﺘﺤﻠﻴل
ﺍﻟﻠﻭﻏﺎﺭﻴﺘﻤﻲ ﺍﻟﺨﻁﻲ ﻓﻲ ﺍﻟﻔﺼل ﺍﻟﺜﺎﻟﺙ ﻋﺸﺭ( ﻜﺎﻥ ﻫﻨﺎﻙ ﻓﻘﻁ ﻤﺘﻐﻴﺭ ﺘﺎﺒﻊ ﻭﺍﺤﺩ،
ﻭﻟﻜﻥ ﻫﻨﺎﻙ ﺤﺎﻻﺕ ﻗﺩ ﻨﺘﻌﺭﺽ ﻟﻬﺎ ﻋﻤﻠﻴﹰﺎ ﻴﻜﻭﻥ ﻫﻨﺎﻙ ﺃﻜﺜﺭ ﻤﻥ ﻤﺘﻐﻴﺭ ﺘﺎﺒﻊ ﻭﺍﺤﺩ
ﻼ ﻓﻲ ﺤﺎﻟﺔ ﺘﺤﻠﻴل ﺍﻟﺘﺒﺎﻴﻥ ﺍﻟﻌﺎﻤﻠﻲ ﺘﺅﺜﺭ ﻋﻠﻴﻪ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﺴﺘﻘﻠﺔ ﻗﻴﺩ ﺍﻟﺒﺤﺙ ،ﻓﻤﺜ ﹰ
factorial ANOVAﻴﻜﻭﻥ ﻫﻨﺎﻙ ﻤﺘﻐﻴﺭ ﺘﺎﺒﻊ ﻭﺍﺤﺩ ﻭﻗﺩ ﻴﻜﻭﻥ ﺃﻜﺜﺭ ﻤﻥ ﻤﺘﻐﻴﺭ ﻤﺴﺘﻘل ﻭﺍﺤﺩ )ﻋﻭﺍﻤل ﻤﺨﺘﻠﻔﺔ( ،ﻭﻟﺫﺍ ﻓﺈﻥ ﺘﺤﻠﻴل ﺍﻟﺘﺒﺎﻴﻥ ﺍﻟﻌﺎﻤﻠﻲ ﻴﻌﺘﺒﺭ ﻓﻲ ﻋﻠﻡ
ﺍﻹﺤﺼﺎﺀ ﺍﺨﺘﺒﺎﺭ ﺃﺤﺎﺩﻱ ﺍﻟﻤﺘﻐﻴﺭ ،Univariate Statistical testﻭﻟﻜﻥ ﻫﻨﺎﻙ ﻤﻥ
ﺍﻷﺴﺎﻟﻴﺏ ﺍﻹﺤﺼﺎﺌﻴﺔ ﻤﺎ ﻴﻬﺘﻡ ﺒﺎﻟﺤﺎﻻﺕ ﺍﻟﺘﻲ ﻴﻜﻭﻥ ﺒﻬﺎ ﺃﻜﺜﺭ ﻤﻥ ﻤﺘﻐﻴﺭ ﺘﺎﺒﻊ ﻭﺍﺤﺩ،
ﻫﺫﻩ ﺍﻷﺴﺎﻟﻴﺏ ﺘﺩﺨل ﻀﻤﻥ ﻓﺭﻉ ﺍﻹﺤﺼﺎﺀ ﺍﻟﺫﻱ ﻴﻌﺭﻑ ﺒﺎﺴﻡ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻤﺘﻌﺩﺩ
ﺍﻟﻤﺘﻐﻴﺭﺍﺕ Multivariate Analysisﺃﻭ ﺒﺎﺨﺘﺼﺎﺭ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻤﺘﻌﺩﺩ. ﻓﻬﻨﺎﻙ ﻋﻠﻰ ﺴﺒﻴل ﺍﻟﻤﺜﺎل ﺃﺴﻠﻭﺏ ﺘﺤﻠﻴل ﺍﻟﺘﺒﺎﻴﻥ ﺍﻟﻤﺘﻌﺩﺩ
Multivariate
) Analysis of Variance (MANOVAﺍﻟﺫﻱ ﻴﺴﺘﺨﺩﻡ ﻋﻨﺩﻤﺎ ﻴﻜﻭﻥ ﻟﺩﻴﻨﺎ ﺘﺠﺭﺒﺔ
ﺒﺄﻜﺜﺭ ﻤﻥ ﻤﺘﻐﻴﺭ ﺘﺎﺒﻊ ﻭﺍﺤﺩ ،ﻭﻓﻲ ﻫﺫﺍ ﺍﻷﺴﻠﻭﺏ ﻴﺘﻡ ﺩﻤﺞ ﺠﻤﻴﻊ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺘﺎﺒﻌﺔ ﻓﻲ ﻤﺘﻐﻴﺭ ﺠﺩﻴﺩ ﺒﻁﺭﻴﻘﺔ ﺘﺠﻌل ﻤﺘﻭﺴﻁ ﻗﻴﻡ ﻫﺫﺍ ﺍﻟﻤﺘﻐﻴﺭ ﻓﻲ ﺍﻟﻤﺠﻤﻭﻋﺎﺕ ﺍﻟﻤﺨﺘﻠﻔﺔ ﻤﺘﺠﺎﻨﺴﺔ ﺩﺍﺨﻠﻴﹰﺎ ﻭﻏﻴﺭ ﻤﺘﺠﺎﻨﺴﺔ ﻓﻴﻤﺎ ﺒﻴﻨﻬﺎ ،ﻭﻴﺘﻡ ﺒﺎﺴﺘﺨﺩﺍﻡ ﻫﺫﺍ ﺍﻷﺴﻠﻭﺏ ﺍﺨﺘﺒﺎﺭ
ﻤﻌﻨﻭﻴﺔ ﺍﻟﻔﺭﻕ ﺒﻴﻥ ﻤﺘﻭﺴﻁﺎﺕ ﺍﻟﻤﺘﻐﻴﺭ ﺍﻟﺠﺩﻴﺩ ﻓﻲ ﺍﻟﻤﺠﻤﻭﻋﺎﺕ ﺍﻟﻤﺨﺘﻠﻔﺔ ﺒﻨﻔﺱ ﻓﻜﺭﺓ ﻭﻓﻠﺴﻔﺔ ﺍﻻﺨﺘﺒﺎﺭ ﻓﻲ ﺘﺤﻠﻴل ﺍﻟﺘﺒﺎﻴﻥ ﺍﻟﻌﺎﻤﻠﻲ ﺍﻟﻤﻌﺭﻭﻑ . ANOVA
) (16ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ
556
ﻭﻋﻠﻰ ﺍﻟﺭﻏﻡ ﻤﻥ ﺫﻟﻙ ،ﻭﻨﻅﺭﹰﺍ ﻟﺘﻌﻘﻴﺩ ﺃﺴﻠﻭﺏ ﺘﺤﻠﻴل ﺍﻟﺘﺒﺎﻴﻥ ﺍﻟﻤﺘﻌﺩﺩ ) Multivariate Analysis of Variance (MANOVAﻓﺈﻨﻨﺎ ﻟﻥ ﻨﺘﻌﺭﺽ ﻫﻨﺎ
ﺒﺎﻟﺘﻔﺼﻴل ﻟﺸﺭﺡ ﻜﻴﻔﻴﺔ ﺍﺴﺘﺨﺩﺍﻡ ﻨﻅﺎﻡ SPSSﻓﻲ ﺘﻁﺒﻴﻘﻪ ،ﻭﻨﻨﺼﺢ ﺍﻟﻤﺴﺘﺨﺩﻡ ﺒﺎﺴﺘﺨﺩﺍﻡ ﺃﺴﻠﻭﺏ ﺘﺤﻠﻴل ﺍﻟﺘﺒﺎﻴﻥ ﺍﻟﻌﺎﻤﻠﻲ factorial ANOVAﺍﻟﺫﻱ ﺘﻡ ﻤﻨﺎﻗﺸﺘﻪ ﺒﺎﻟﺘﻔﺼﻴل ﻓﻲ
ﺜﻼﺙ ﻓﺼﻭل ﻤﻥ ﻫﺫﺍ ﺍﻟﻜﺘﺎﺏ ﻋﻠﻰ ﻜل ﻤﺘﻐﻴﺭ ﺘﺎﺒﻊ ﻋﻠﻰ ﺤﺩﻩ ،ﻓﻬﺫﺍ ﺍﻷﺴﻠﻭﺏ ﺍﻷﻴﺴﺭ
ﻼ ﻤﻥ ﺫﻟﻙ ﺍﻷﺴﻠﻭﺏ ﺍﻟﻤﻌﻘﺩ. ﻓﻲ ﺍﻟﻔﻬﻡ ﻭﺍﻻﺴﺘﻌﻤﺎل ﻗﺩ ﻴﻭﻓﺭ ﻤﻌﻠﻭﻤﺎﺕ ﺃﻜﺜﺭ ﺘﻔﺼﻴ ﹰ ﻭﻴﻌﺘﺒﺭ ﺃﺴﻠﻭﺏ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ
Discriminant
Analysisﻫﻭ ﺍﻟﻭﺠﻪ ﺍﻵﺨﺭ ﻷﺴﻠﻭﺏ ﺘﺤﻠﻴل ﺍﻟﺘﺒﺎﻴﻥ ﺍﻟﻤﺘﻌﺩﺩ ، MANOVAﻓﺎﻟﻤﺘﻐﻴﺭ ﺍﻟﺠﺩﻴﺩ ﺍﻟﺫﻱ ﻴﺘﻡ ﺍﺸﺘﻘﺎﻗﻪ ﻓﻲ ﺤﺎﻟﻪ ﺘﺤﻠﻴل ﺍﻟﺘﺒﺎﻴﻥ ﺍﻟﻤﺘﻌﺩﺩ MANOVAﻫﻭ ﺫﺍﺘﻪ ﺍﻟﺫﻱ ﻨﺤﺘﺎﺝ ﺇﻟﻴﻪ ﻫﻨﺎ ﻭﻟﻜﻥ ﻟﻐﺭﺽ ﺁﺨﺭ ،ﻓﻬﺫﺍ ﺍﻟﻤﺘﻐﻴﺭ ﻗﺩ ﻴﻜﻭﻥ ﻤﻔﻴﺩﹰﺍ ﻓﻲ ﺍﻟﺘﻨﺒﺅ ﺒﺎﻟﻤﺠﻤﻭﻋﺔ
ﺍﻟﺘﻲ ﺘﻨﺘﻤﻲ ﺇﻟﻴﻬﺎ ﻤﻔﺭﺩﺓ ﻤﻌﻴﻨﺔ ﻤﻥ ﻨﻔﺱ ﺍﻟﻤﺠﺘﻤﻊ ،ﻓﺈﺫﺍ ﻜﺎﻨﺕ ﻟﺩﻴﻨﺎ ﻋﻴﻨﺔ ﻤﻥ ﺍﻟﺒﻴﺎﻨﺎﺕ ﻨﺘﺠﺕ ﻤﻥ ﺘﺠﺭﺒﺔ ﺘﻨﻘﺴﻡ ﻓﻴﻬﺎ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ ﺇﻟﻰ ﻤﺠﻤﻭﻋﺎﺕ ﻤﻥ ﺨﻼل ﻤﺘﻐﻴﺭﻴﻥ ﺃﻭ ﺃﻜﺜﺭ
ﻓﺈﻨﻪ ﻴﻤﻜﻥ ﺩﻤﺞ ﻫﺫﻩ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﻭﺨﻠﻕ ﻤﺘﻐﻴﺭ ﻭﺍﺤﺩ ﺘﺎﺒﻊ ﺠﺩﻴﺩ ﻴﺠﻌل ﺍﻟﺩﻗﺔ ﻓﻲ ﺘﻤﻴﻴﺯ ﺍﻟﻤﻔﺭﺩﺍﺕ ﻓﻲ ﺍﻟﻌﻴﻨﺔ ﺇﻟﻰ ﻤﺠﻤﻭﻋﺎﺘﻬﺎ ﺃﻜﺒﺭ ﻤﺎ ﻴﻤﻜﻥ ،ﻭﻫﺫﺍ ﺍﻟﻤﺘﻐﻴﺭ ﺍﻟﺠﺩﻴﺩ ﻓﻲ ﻫﺫﻩ
ﺍﻟﺤﺎﻟﺔ ﺴﻭﻑ ﻴﺼﺒﺢ ﺍﻟﻤﺘﻐﻴﺭ ﺍﻟﺘﺎﺒﻊ Regressorﺍﻟﺫﻱ ﻴﻤﻜﻥ ﻤﻥ ﺨﻼﻟﻪ ﺍﻟﺘﻨﺒﺅ ﺒﺎﻟﻤﺠﻤﻭﻋﺔ ﺍﻟﺘﻲ ﺘﻨﺘﻤﻲ ﺇﻟﻴﻬﺎ ﻤﻔﺭﺩﺓ ﺠﺩﻴﺩﺓ ﻓﻲ ﺍﻟﻤﺠﺘﻤﻊ )ﻟﻴﺴﺕ ﻀﻤﻥ ﺍﻟﻌﻴﻨﺔ(.
ﻓﻲ ﺍﻟﻭﺍﻗﻊ ﻴﺴﺘﺨﺩﻡ ﺃﺴﻠﻭﺏ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ Discriminant Analysisﺨﻠﻔﻴﺔ
ﺭﻴﺎﻀﻴﺔ ﻨﻅﺭﻴﺔ ﻤﺸﺎﺒﻬﺔ ﺘﻤﺎﻤﹰﺎ ﻷﺴﻠﻭﺏ ﺘﺤﻠﻴل ﺍﻟﺘﺒﺎﻴﻥ ﺍﻟﻤﺘﻌﺩﺩ ، MANOVAﺇﻻ ﺃﻨﻪ
ﻓﻲ ﺤﺎﻟﺔ ﺘﺤﻠﻴل ﺍﻟﺘﺒﺎﻴﻥ ﺍﻟﻤﺘﻌﺩﺩ MANOVAﻨﻜﻭﻥ ﻨﻌﻠﻡ ﺇﻟﻰ ﺃﻱ ﻤﺠﻤﻭﻋﺔ ﺘﻨﺘﻤﻲ ﻜل ﻤﻔﺭﺩﺓ ﻭﻨﺒﺤﺙ ﻋﻥ ﻤﺘﻐﻴﺭ ﻭﺍﺤﺩ ﻤﺭﻜﺏ ﺠﺩﻴﺩ ﻴﻤﻜﻨﻪ ﻟﻭﺤﺩﻩ ﺃﻥ ﻴﺒﻴﻥ ﺍﻟﻔﺭﻭﻕ ﺒﻴﻥ
ﺍﻟﻤﺠﻤﻭﻋﺎﺕ ،ﻭﻟﻜﻥ ﻓﻲ ﺃﺴﻠﻭﺏ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ Discriminant Analysisﻴﻜﻭﻥ
ﺍﻫﺘﻤﺎﻤﻨﺎ ﺤﻭل ﺍﻟﺘﻌﺭﻑ ﻋﻠﻰ ﺍﻟﻤﺠﻤﻭﻋﺔ ﺍﻟﺘﻲ ﺘﻨﺘﻤﻲ ﻟﻬﺎ ﻜل ﻤﻔﺭﺩﺓ ﻋﻠﻰ ﺃﺴﺎﺱ ﻗﻴﻡ
ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺘﺎﺒﻌﺔ ،ﻓﻌﻠﻰ ﺴﺒﻴل ﺍﻟﻤﺜﺎل ﺴﻭﻑ ﻴﻜﻭﻥ ﻫﻨﺎﻙ ﻓﺎﺌﺩﺓ ﻋﻅﻴﻤﺔ ﻤﻥ ﺇﻤﻜﺎﻨﻴﺔ
ﺍﻟﺘﻨﺒﺅ ﺒﺄﻱ ﺍﻷﻁﻔﺎل ﺴﻭﻑ ﻴﺘﻤﻜﻥ ﻤﻥ ﺍﺴﺘﻜﻤﺎل ﺘﻌﻠﻴﻤﻪ ﺍﻟﺠﺎﻤﻌﻲ ﻭﺃﻴﻬﻡ ﺴﻭﻑ ﻴﺘﻤﻜﻥ
) (16ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ
557
ﻤﻥ ﺇﻴﺠﺎﺩ ﻓﺭﺼﺔ ﻋﻤل ﻭﺃﻴﻬﻡ ﺴﻴﻨﻀﻡ ﺇﻟﻰ ﻗﺎﻓﻠﺔ ﺍﻟﻌﺎﻁﻠﻴﻥ ﻋﻥ ﺍﻟﻌﻤل ﻭﺫﻟﻙ ﺒﺎﻻﻋﺘﻤﺎﺩ ﻋﻠﻰ ﺒﻴﺎﻨﺎﺕ ﻋﻴﻨﺔ ﻤﻥ ﺍﻷﻁﻔﺎل ﺤﻭل ﻤﺠﻤﻭﻋﺔ ﻤﻥ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺘﺘﻌﻠﻕ ﺒﺄﺩﺍﺌﻬﻡ ﻓﻲ
ﺍﻟﻤﺭﺍﺤل ﺍﻷﻭﻟﻰ ﻤﻥ ﺩﺭﺍﺴﺘﻬﻡ ،ﻭﻴﻘﺩﻡ ﺃﺴﻠﻭﺏ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ Discriminant Analysisﺇﺠﺎﺒﺔ ﻟﻤﺜل ﻫﺫﻩ ﺍﻟﺘﺴﺎﺅﻻﺕ.
ﻭﻓﻲ ﺤﺎﻟﺔ ﺘﺤﻠﻴل ﺍﻟﺘﺒﺎﻴﻥ ﺍﻟﻤﺘﻌﺩﺩ MANOVAﻴﻁﻠﻕ ﻋﻠﻰ ﺍﻟﻤﺘﻐﻴﺭ ﺍﻟﻤﺭﻜﺏ
ﺍﻟﺠﺩﻴﺩ ﺍﻟﺫﻱ ﻴﺘﻡ ﺍﺸﺘﻘﺎﻗﻪ ﺍﺴﻡ ﺩﺍﻟﺔ ﺍﻟﺘﻤﻴﻴﺯ Discriminant functionﻷﻨﻪ ﻋﺒﺎﺭﺓ ﻋﻥ
ﻤﺠﻤﻭﻉ ﻗﻴﻡ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺘﺎﺒﻌﺔ ﻤﺭﺠﺢ ﺒﺄﻭﺯﺍﻥ ﺍﺨﺘﻴﺭﺕ ﺒﺤﻴﺙ ﺘﺠﻌل ﺘﻭﺯﻴﻊ
ﺍﻟﻤﺠﻤﻭﻋﺎﺕ ﺍﻟﻤﺨﺘﻠﻔﺔ ﻤﻨﻔﺼﻠﺔ ﻋﻥ ﺒﻌﻀﻬﺎ ﺍﻟﺒﻌﺽ ﺇﻟﻰ ﺃﺒﻌﺩ ﺤﺩ ﻤﻤﻜﻥ ،ﺒﻴﻨﻤﺎ ﻓﻲ
ﺃﺴﻠﻭﺏ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ Discriminant Analysisﻴﺘﻡ ﺍﺸﺘﻘﺎﻕ ﻨﻔﺱ ﺍﻟﻤﺘﻐﻴﺭ ﺍﻟﻤﺭﻜﺏ
ﺍﻟﺠﺩﻴﺩ ﺒﺤﻴﺙ ﻴﺠﻌل ﺒﺎﻹﻤﻜﺎﻥ ﺍﻟﺘﻨﺒﺅ ﺒﺎﻟﻤﺠﻤﻭﻋﺔ ﺍﻟﺘﻲ ﺘﻨﺘﻤﻲ ﻟﻬﺎ ﻜل ﻤﻔﺭﺩﺓ ﺒﺄﻋﻠﻰ ﺩﻗﺔ
ﻤﻤﻜﻨﺔ ،ﻟﺫﻟﻙ ﻓﺈﻨﻪ ﻤﻥ ﺍﻟﻨﺎﺤﻴﺔ ﺍﻟﻨﻅﺭﻴﺔ )ﺍﻟﺭﻴﺎﻀﻴﺔ( ﻫﻨﺎﻙ ﺍﻟﻜﺜﻴﺭ ﻤﻥ ﺍﻟﺠﻭﺍﻨﺏ ﺍﻟﻤﺸﺘﺭﻜﺔ ﺒﻴﻥ ﺃﺴﻠﻭﺏ ﺘﺤﻠﻴل ﺍﻟﺘﺒﺎﻴﻥ ﺍﻟﻤﺘﻌﺩﺩ MANOVAﻭﺃﺴﻠﻭﺏ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ
Discriminant Analysisﺇﻻ ﺃﻥ ﺍﻻﻫﺘﻤﺎﻡ ﻴﻜﻭﻥ ﺤﻭل ﺍﻟﺘﻨﺒﺅ ﺒﺎﻟﻤﺠﻤﻭﻋﺔ ﺍﻟﺘﻲ ﺘﻨﺘﻤﻲ ﻟﻬﺎ ﻜل ﻤﻔﺭﺩﺓ ﻴﻜﻭﻥ ﻤﺭﻜﺯ ﺍﻻﻫﺘﻤﺎﻡ ﻓﻲ ﺃﺴﻠﻭﺏ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ Discriminant
،Analysisﻭﻫﻨﺎﻙ ﻓﺭﻭﻕ ﺃﺨﺭﻯ ﺒﻴﻥ ﺍﻷﺴﻠﻭﺒﻴﻥ ﻭﻟﻜﻥ ﻟﻥ ﻨﻨﺎﻗﺸﻬﺎ ﻫﻨﺎ ﻭﻟﻜﻥ ﻴﻤﻜﻥ ﺍﻟﺭﺠﻭﻉ ﺇﻟﻴﻬﺎ ﻓﻲ ﺒﻌﺽ ﺍﻟﻜﺘﺏ ﻤﺜل ) Tabachnick & Fidell (1996ﻭ & Flury
) ، Riedwyl (1988ﻭﺫﻟﻙ ﻷﻨﻪ ﻤﻥ ﺍﻟﻨﺎﺤﻴﺔ ﺍﻟﺘﻁﺒﻴﻘﻴﺔ ﻫﻨﺎ ﺘﻜﻭﻥ ﺠﻭﺍﻨﺏ ﺍﻟﺘﺸﺎﺒﻪ ﺒﻴﻥ ﺍﻷﺴﻠﻭﺒﻴﻥ ﺃﻫﻡ ﻤﻥ ﺠﻭﺍﻨﺏ ﺍﻻﺨﺘﻼﻑ ،ﺇﺫ ﻴﻤﻜﻥ ﺍﺴﺘﺨﺩﺍﻡ ﻁﺭﻕ ﺘﺤﻠﻴل ﺍﻟﺘﺒﺎﻴﻥ ﺍﻟﻤﺘﻌﺩﺩ
MANOVAﻓﻲ ﺇﺠﺭﺍﺀ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ . Discriminant Analysis
ﻗﺩ ﻴﻼﺤﻅ ﺍﻟﻘﺎﺭﺉ ﺃﻨﻨﺎ ﻓﻲ ﻫﺫﺍ ﺍﻟﻜﺘﺎﺏ ﺍﺴﺘﺨﺩﻤﻨﺎ ﺘﻌﺭﻴﻑ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺘﺎﺒﻌﺔ
Dependent Variablesﻭﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﺴﺘﻘﻠﺔ Independent Variablesﺒﺎﻟﻤﻔﻬﻭﻡ ﺍﻟﻤﺴﺘﺨﺩﻡ ﻓﻲ ﺘﺤﻠﻴل ﺍﻟﺘﺠﺎﺭﺏ ﻭﻟﻴﺱ ﺒﺫﺍﺕ ﺍﻟﻤﻔﻬﻭﻡ ﺍﻟﻤﺴﺘﺨﺩﻡ ﻓﻲ ﺘﺤﻠﻴل ﺍﻻﺭﺘﺒﺎﻁ ﻭﺍﻻﻨﺤﺩﺍﺭ ،ﺇﺫ ﻴﻌﺭﻑ ﺍﻟﻤﺘﻐﻴﺭ ﺍﻟﻤﺴﺘﻘل ﻋﻠﻰ ﺃﻨﻪ ﺫﻟﻙ ﺍﻟﻤﺘﻐﻴﺭ ﺍﻟﺫﻱ ﻗﺩ ﻴﻜﻭﻥ ﻟﻪ ﺃﺜﺭ
ﻋﻠﻰ ﻤﺘﻐﻴﺭ ﺁﺨﺭ ﺒﺼﺭﻑ ﺍﻟﻨﻅﺭ ﻋﻤﺎ ﺇﺫﺍ ﻜﺎﻥ ﻨﺘﻴﺠﺔ ﻹﺠﺭﺍﺀ ﺒﻌﺽ ﺍﻟﺤﺴﺎﺒﺎﺕ ﺃﻭ ﻤﻥ
) (16ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ
558
ﺍﻟﻘﻴﺎﺱ ﺍﻟﻤﺒﺎﺸﺭ ،ﻭﺒﻬﺫﺍ ﻓﺈﻨﻪ ﻴﺠﺩﺭ ﺒﺎﻟﻤﻼﺤﻅﺔ ﺃﻨﻪ ﻋﻨﺩ ﺇﺠﺭﺍﺀ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ Discriminant Analysisﻟﺒﻴﺎﻨﺎﺕ ﺘﺠﺭﺒﺔ ﺘﺤﺘﻭﻱ ﻋﻠﻰ ﻤﺘﻐﻴﺭﻴﻥ ﺘﺎﺒﻌﻴﻥ ﺃﻭ ﺃﻜﺜﺭ ﺘﺘﺤﻭل ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺘﺎﺒﻌﺔ ﺇﻟﻰ ﻤﺘﻐﻴﺭﺍﺕ ﻤﺴﺘﻘﻠﺔ ﻭﻴﺼﺒﺢ ﻤﺘﻐﻴﺭ ﺍﻟﺘﻤﻴﻴﺯ ﺍﻟﺠﺩﻴﺩ ﻫﻭ
ﺍﻟﻤﺘﻐﻴﺭ ﺍﻟﺘﺎﺒﻊ ﺍﻟﻭﺤﻴﺩ.
ﻭﻴﻤﻜﻥ ﺘﻭﻀﻴﺢ ﺍﻟﻬﺩﻑ ﻤﻥ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ Discriminant Analysis
ﺒﻁﺭﻴﻘﺔ ﻨﻅﺭﻴﺔ ﺒﺈﻋﺎﺩﺓ ﺼﻴﺎﻏﺔ ﺍﻟﻤﺸﻜﻠﺔ ﻋﻠﻰ ﺍﻟﻨﺤﻭ ﺍﻟﺘﺎﻟﻲ :ﺇﺫﺍ ﻜﺎﻥ ﻟﺩﻴﻨﺔ ﻤﺠﻤﻭﻋﺔ ﻤﻥ
ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﺴﺘﻘﻠﺔ X1,X2, ......,Xpﻓﺈﻨﻪ ﻹﻴﺠﺎﺩ ﺩﺍﻟﺔ ﺨﻁﻴﺔ Dﻓﻲ ﻫﺫﻩ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﺴﺘﻘﻠﺔ ﻭﺍﻟﺘﻲ ﻋﻨﺩ ﺇﺠﺭﺍﺀ ﺘﺤﻠﻴل ﺍﻟﺘﺒﺎﻴﻥ ﻓﻲ ﺍﺘﺠﺎﻩ ﻭﺍﺤﺩ One-Way Analysis of
Varianceﺒﻬﺩﻑ ﺍﻟﻤﻘﺎﺭﻨﺔ ﺒﻴﻥ ﺍﻟﻔﺌﺎﺕ ﺍﻟﻤﺨﺘﻠﻔﺔ ﻟﻤﺘﻐﻴﺭ ﻭﺼﻔﻲ )ﺃﻭ ﻤﺘﻐﻴﺭ ﺘﺼﻨﻴﻑ( ﺘﺎﺒﻊ ﻤﻘﺎﺭﻨﺔ ﻤﻊ Dﻻﺒﺩ ﻤﻥ ﺃﻥ ﺘﺼﺒﺢ ﺍﻟﻨﺴﺒﺔ SSbetween/SStotalﺃﻜﺒﺭ ﻤﺎ ﻴﻤﻜﻥ، ﻭﺴﻭﻑ ﺘﻜﻭﻥ ﺍﻟﺩﺍﻟﺔ Dﻋﻠﻰ ﺍﻟﺼﻭﺭﺓ ﺍﻟﻌﺎﻤﺔ ﺍﻟﺘﺎﻟﻴﺔ:
D = b0 + b1 X1 + b2 X2 + ……. + bp Xp
ﻭﻜﻤﺎ ﻫﻭ ﺍﻟﺤﺎل ﻓﻲ ﺃﺴﻠﻭﺏ ﺍﻻﻨﺤﺩﺍﺭ ﺍﻟﻤﺘﻌﺩﺩ ﻓﺈﻨﻪ ﻴﻤﻜﻥ ﺍﻟﺘﻌﺭﻑ ﻋﻠﻰ
ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺘﻲ ﺘﻘﺩﻡ ﻤﺴﺎﻫﻤﺔ ﻤﻌﻨﻭﻴﺔ ﻓﻲ ﻋﻤﻠﻴﺔ ﺍﻟﺘﻨﺒﺅ ﻭﺤﺫﻑ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻷﺨﺭﻯ ﻤﻥ ﺍﻟﺩﺍﻟﺔ ﺍﻟﻨﻬﺎﺌﻴﺔ ،ﻭﻫﻨﺎﻙ ﻋﺩﺩ ﻜﺒﻴﺭ ﻤﻥ ﺍﻷﻤﻭﺭ ﺍﻟﻤﺘﻭﺍﺯﻴﺔ ﻓﻲ ﺍﻷﺴﻠﻭﺒﻴﻥ ﺍﻹﺤﺼﺎﺌﻴﻴﻥ.
ﻭﺇﺫﺍ ﺘﺫﻜﺭﻨﺎ ﺃﻨﻪ ﻓﻲ ﺃﺴﻠﻭﺏ ﺘﺤﻠﻴل ﺍﻟﺘﺒﺎﻴﻥ ﻓﻲ ﺍﺘﺠﺎﻩ ﻭﺍﺤﺩ ANOVAﻴﻤﻜﻥ
ﻗﺴﻤﺔ ﻤﺠﻤﻭﻉ ﺍﻟﻤﺭﺒﻌﺎﺕ ﺍﻟﻜﻠﻲ SStotalﻭﺍﻟﺫﻱ ﻴﻘﻴﺱ ﺍﻟﺘﺒﺎﻴﻥ ﺍﻟﻜﻠﻲ ﻟﻠﻘﻴﻡ ﺤﻭل
ﺍﻟﻤﺘﻭﺴﻁ ﺍﻟﻌﺎﻡ ﺇﻟﻰ ﻗﺴﻤﻴﻥ ﻫﻤﺎ :
(1) SSbetween (2) SSwithin ﻓﺈﻥ ﺍﻟﻘﺴﻡ ﺍﻷﻭل ﻫﻭ ﻋﺒﺎﺭﺓ ﻋﻥ ﺍﻟﺘﺒﺎﻴﻥ ﺒﻴﻥ ﻤﺘﻭﺴﻁﺎﺕ ﺍﻟﻤﺠﻤﻭﻋﺎﺕ ﺤﻭل
ﺍﻟﻤﺘﻭﺴﻁ ﺍﻟﻌﺎﻡ ﻭﺍﻟﻘﺴﻡ ﺍﻟﺜﺎﻨﻲ ﻫﻭ ﻋﺒﺎﺭﺓ ﻋﻥ ﺍﻟﺘﺒﺎﻴﻥ ﺒﻴﻥ ﺍﻟﻘﻴﻡ ﺤﻭل ﻤﺘﻭﺴﻁﺎﺕ ﺍﻟﻤﺠﻤﻭﻋﺎﺕ ﺍﻟﺘﻲ ﺘﻨﺘﻤﻲ ﺇﻟﻴﻬﺎ ،ﻭﺘﺭﺘﺒﻁ ﺍﻟﻤﺠﻤﻭﻋﺎﺕ ﺍﻟﺜﻼﺜﺔ ﻤﻌﹰﺎ ﻓﻲ ﺍﻟﻌﻼﻗﺔ :
SStotal = SSbetween + SSwithin
) (16ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ
559
ﻭﻜﻠﻤﺎ ﻜﺎﻨﺕ ﺍﻟﻨﺴﺒﺔ SSbetween/SStotalﻜﺒﻴﺭﺓ ﻭﺍﻗﺘﺭﺒﺕ ﻤﻥ ﺍﻟﻭﺍﺤﺩ ﺍﻟﺼﺤﻴﺢ ﻓﻲ ﺃﺴﻠﻭﺏ ﺘﺤﻠﻴل ﺍﻟﺘﺒﺎﻴﻥ ANOVAﻜﻠﻤﺎ ﻜﺎﻨﺕ ﻗﻴﻤﺔ ﺍﻟﻤﻌﻨﻭﻴﺔ p-valueﺃﺼﻐﺭ ،ﻫﺫﻩ
ﺍﻟﻨﺴﺒﺔ ) (SSbetween/SStotalﻴﻁﻠﻕ ﻋﻠﻴﻬﺎ ﺃﺤﻴﺎﻨﹰﺎ ﻤﺭﺒﻊ ﺇﻴﺘﺎ ) eta squared ( η2ﺃﻭ ﻨﺴﺒﺔ ﺍﻻﺭﺘﺒﺎﻁ Correlation Ratioﻭﺘﻌﺩ ﻤﻥ ﺃﻗﺩﻡ ﺍﻟﻤﻘﺎﻴﻴﺱ ﻟﻘﻭﺓ ﺃﺜﺭ ﻋﺎﻤل ﺍﻟﺘﺠﺭﺒﺔ.
ﻭﻴﺘﻡ ﻋﺎﺩﺓ ﺘﻜﻭﻴﻥ ﻨﺴﺏ ﺍﻟﺘﺒﺎﻴﻨﺎﺕ ﺒﻬﺩﻑ ﺍﺨﺘﺒﺎﺭ ﻤﻌﻨﻭﻴﺔ ﺍﻵﺜﺎﺭ ﺍﻟﺭﺌﻴﺴﻴﺔ
Main effectsﻭﺍﻟﺘﻔﺎﻋﻼﺕ interactionsﺒﺎﺴﺘﺨﺩﺍﻡ ﺇﺤﺼﺎﺀ Fﺍﻟﻤﻌﺭﻭﻑ ،ﻭﻫﺫﺍ ﻴﻤﻜﻥ ﺍﻟﺘﻌﺒﻴﺭ ﻋﻨﻪ ﺒﻁﺭﻴﻘﺔ ﺃﺨﺭﻯ ،ﻓﻲ ﺤﺎﻟﺔ ﺍﻟﺘﺤﻠﻴل ﺍﻷﺤﺎﺩﻱ )ﺤﻴﺙ ﻴﻜﻭﻥ ﻤﺘﻐﻴﺭ ﺘﺎﺒﻊ
ﻭﺍﺤﺩ( ﺘﻜﻭﻥ ﺍﻟﻨﺴﺒﺔ SSwithin/SStotalﻫﻲ ﻗﻴﻤﺔ ﺇﺤﺼﺎﺀ ﻴﻌﺭﻑ ﺒﺎﺴﻡ "ﺇﺤﺼﺎﺀ ﻻﻤﺩﺍ
ﻟﻭﻴﻠﻜﺱ" ) ، Wilks' Lambda ( Λﻭﺒﺎﻟﺘﺎﻟﻲ ﻓﺈﻥ :
SS between SS within SS between + SS within + = =1 SS total SS total SS total
= η2 + Λ
ﻭﺒﺎﻟﺘﺎﻟﻲ ﻓﺈﻨﻪ ﻓﻲ ﺤﺎﻟﺔ ﺍﻟﺘﺤﻠﻴل ﺍﻷﺤﺎﺩﻱ ﺘﻜﻭﻥ ﻗﻴﻤﺔ Λﻤﺴﺎﻭﻴﺔ ، η2ﻭﻨﻅﺭﹰﺍ
ﻷﻥ ﺍﻟﺘﺒﺎﻴﻥ ﺍﻟﺼﻐﻴﺭ ﻨﺴﺒﻴﹰﺎ ﻟﻠﻘﻴﻡ ﺤﻭل ﻤﺘﻭﺴﻁﺎﺕ ﻤﺠﻤﻭﻋﺎﺘﻬﺎ ﻴﺘﻀﻤﻥ ﺘﺒﺎﻴﻥ ﻜﺒﻴﺭ ﻨﺴﺒﻴﹰﺎ ﺒﻴﻥ ﻤﺘﻭﺴﻁﺎﺕ ﺍﻟﻤﺠﻤﻭﻋﺎﺕ ﻓﺈﻨﻪ ﻴﺭﺠﺢ ﺃﻥ ﺘﻜﻭﻥ ﻗﻴﻤﺔ Λﺍﻟﺼﻐﻴﺭﺓ ﻤﻌﻨﻭﻴﺔ ﺇﺤﺼﺎﺌﻴﹰﺎ.
ﻭﻓﻲ ﺤﺎﻟﺔ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻤﺘﻌﺩﺩ )ﻭﺒﺎﻟﺘﺤﺩﻴﺩ ﻋﻨﺩﻤﺎ ﻴﻜﻭﻥ ﻫﻨﺎﻙ ﻤﺘﻐﻴﺭﻴﻥ ﺘﺎﺒﻌﻴﻥ ﺃﻭ
ﺃﻜﺜﺭ( ﻜﻤﺎ ﻫﻭ ﺍﻟﺤﺎل ﻓﻲ ﺤﺎﻟﺔ ﺃﺴﻠﻭﺏ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ Discriminant Analysis ﺘﺼﺒﺢ ﻗﻴﻤﺔ Λﻋﺒﺎﺭﺓ ﻋﻥ ﻨﺴﺒﺔ ﺍﻟﻤﺤﺩﺩﻴﻥ Determinantsﻟﻤﺼﻔﻭﻓﺘﻲ Matrices
ﻤﺠﻤﻭﻉ ﺍﻟﻤﺭﺒﻌﺎﺕ ﻭﺤﺎﺼل ﺍﻟﻀﺭﺏ ﺍﻟﺘﺒﺎﺩﻟﻲ ، cross productsﻭﺘﺴﺘﺨﺩﻡ ﻓﻲ
ﺍﻟﺤﻜﻡ ﻋﻠﻰ ﻤﺎ ﺇﺫﺍ ﻜﺎﻨﺕ ﺩﺍﻟﺔ ﻤﺎ ﻓﻲ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﺴﺘﻘﻠﺔ )ﻭﺘﻌﺭﻑ ﺒﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺘﻤﻴﻴﺯ
(discriminating variablesﻴﻤﻜﻥ ﺍﻻﻋﺘﻤﺎﺩ ﻋﻠﻴﻬﺎ ﻓﻲ ﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﻓﺌﺎﺕ ﺍﻟﻤﺘﻐﻴﺭ ﺍﻟﺘﺎﺒﻊ ﺃﻡ ﻻ ،ﻭﺤﻴﺙ ﺃﻥ ﺘﻭﺯﻴﻊ ﺍﻟﻤﻌﺎﻴﻨﺔ ﻟﻠﻤﻘﺩﺍﺭ Λﻤﻌﻘﺩ ﻓﺈﻨﻪ ﻤﻥ ﺍﻟﻤﻨﺎﺴﺏ ﻭﺒﺎﻹﻤﻜﺎﻥ ﺍﺴﺘﺨﺩﺍﻡ ﺘﻘﺭﻴﺏ ﺘﻭﺯﻴﻌﻪ ﺇﻟﻰ ﺘﻭﺯﻴﻊ ﻜﺎﻱ ﺘﺭﺒﻴﻊ ) Chi-square (χ2ﺒﺄﻤﺎﻥ .
) (16ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ
560
ﻭﺴﻭﻑ ﻴﻜﻭﻥ ﻫﻨﺎﻙ ﺘﻭﺯﻴﻊ ﺍﺤﺘﻤﺎﻟﻲ ﻟﻠﻤﻘﺩﺍﺭ Dﻟﻤﻔﺭﺩﺍﺕ ﻜل ﻓﺌﺔ ﻤﻥ ﻓﺌﺎﺕ ﺍﻟﻤﺘﻐﻴﺭ ﺍﻟﺘﺎﺒﻊ )ﺴﻭﻑ ﻨﻔﺘﺭﺽ ﺃﻨﻪ ﺍﻟﺘﻭﺯﻴﻊ ﺍﻟﻁﺒﻴﻌﻲ( ،ﻭﻋﺎﺩﺓ ﺘﺘﻘﺎﻁﻊ ﻫﺫﻩ ﺍﻟﺘﻭﺯﻴﻌﺎﺕ
ﺒﺎﻟﻁﺒﻊ ،ﻭﻟﻜﻥ ﺍﻟﻬﺩﻑ ﻤﻥ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ Discriminant analysisﻫﻭ ﺇﻴﺠﺎﺩ ﻗﻴﻡ
ﻟﻠﺜﻭﺍﺒﺕ ) (b1 , b2 , ........ , bpﻓﻲ ﺩﺍﻟﺔ ﺍﻟﺘﻤﻴﻴﺯ Discriminant functionﻭﺍﻟﺘﻲ ﺘﺠﻌل ﺍﻟﺘﻘﺎﻁﻊ ﺒﻴﻥ ﺘﻭﺯﻴﻌﺎﺕ ﺍﻟﻤﻘﺎﺩﻴﺭ Dﻓﻲ ﺍﻟﻔﺌﺎﺕ ﺍﻟﻤﺨﺘﻠﻔﺔ ﺃﺼﻐﺭ ﻤﺎ ﻴﻤﻜﻥ ،ﺒﻌﺒﺎﺭﺓ
ﺃﺨﺭﻯ ﺍﻟﻔﻜﺭﺓ ﻫﻲ ﺇﺒﻌﺎﺩ ﺍﻟﺘﻭﺯﻴﻌﺎﺕ ﺍﻻﺤﺘﻤﺎﻟﻴﺔ ﻟﻠﻤﻘﺎﺩﻴﺭ Dﺒﺄﻗﺼﻰ ﻗﺩﺭ ﻤﻤﻜﻥ ،ﻭﺇﺫﺍ ﻜﺎﻥ ﻫﻨﺎﻙ ﻓﺌﺘﻴﻥ ﻓﻘﻁ ﻓﻲ ﺍﻟﻤﺘﻐﻴﺭ ﺍﻟﺘﺎﺒﻊ ﻟﻥ ﻴﻜﻭﻥ ﻫﻨﺎﻙ ﺴﻭﻯ ﺩﺍﻟﺔ ﺘﻤﻴﻴﺯ
Discriminant functionﻭﺍﺤﺩﺓ ﻴﻤﻜﻥ ﺘﻜﻭﻴﻨﻬﺎ.
.2 .16أﻧﻮاع اﻟﺘﺤﻠﻴﻞ اﻟﻄﺒﻘﻲ : Types of Discriminant Analysis : ﻫﻨﺎﻙ ﺜﻼﺙ ﺃﻨﻭﺍﻉ ﻤﺘﺎﺤﺔ ﻟﻠﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺫﻟﻙ ﺤﺴﺏ ﻁﺭﻴﻘﺔ ﺇﺩﺨﺎل
ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺇﻟﻰ ﺍﻟﺘﺤﻠﻴل ،ﻭﻫﺫﻩ ﺍﻟﻁﺭﻕ ﻫﻲ :
.1ﺍﻟﺘﺤﻠﻴل ﺍﻟﻤﺒﺎﺸﺭ ) : Direct Discriminant Analysis (DDAﻭﻴﺘﻡ ﺒﻪ ﺇﺩﺨﺎل ﺠﻤﻴﻊ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﺴﺘﻘﻠﺔ ﺇﻟﻰ ﺍﻟﺘﺤﻠﻴل ﻤﺭﺓ ﻭﺍﺤﺩﺓ ﻭﺩﻭﻥ ﺍﺴﺘﺜﻨﺎﺀ ﻭﺩﻭﻥ ﺇﻋﻁﺎﺀ ﺃﻱ ﺃﻫﻤﻴﺔ ﻟﺘﺭﺘﻴﺏ ﺩﺨﻭﻟﻬﺎ .
.2ﺍﻟﺘﺤﻠﻴل ﺍﻟﻬﺭﻤﻲ ): Hierarchical Discriminant Analysis (HDA ﻭﻫﻨﺎ ﻴﺘﻡ ﺇﺩﺨﺎل ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﻟﻠﺘﺤﻠﻴل ﺘﺒﻌﹰﺎ ﻟﻤﺎ ﻴﺭﺍﻩ ﺍﻟﺒﺎﺤﺙ ﻤﻥ ﺃﻫﻤﻴﺔ
ﻟﻠﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﺴﺘﻘﻠﺔ ﻭﺒﺎﻟﺘﺭﺘﻴﺏ ﺍﻟﺫﻱ ﻴﻌﺘﻘﺩ ﺃﻨﻪ ﻤﻨﺎﺴﺒﹰﺎ .
.3ﺍﻟﺘﺤﻠﻴل ﺍﻟﺘﺩﺭﻴﺠﻲ ) : Stepwise Discriminant Analysis (SDAﻭﻓﻲ ﻫﺫﻩ ﺍﻟﺤﺎﻟﺔ ﻴﻜﻭﻥ ﺘﺭﺘﻴﺏ ﺇﻀﺎﻓﺔ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﺴﺘﻘﻠﺔ ﺇﻟﻰ ﺍﻟﺘﺤﻠﻴل ﻭﺍﺴﺘﺒﻌﺎﺩﻫﺎ ﻤﻨﻪ ﺘﺒﻌﹰﺎ ﻟﻤﻌﺎﻴﻴﺭ ﺇﺤﺼﺎﺌﻴﺔ ﻓﻘﻁ .
ﻓﻲ ﺃﻏﻠﺏ ﺍﻟﺤﺎﻻﺕ ﺍﻟﻌﻤﻠﻴﺔ ﻻ ﻴﻜﻭﻥ ﺃﻱ ﺴﺒﺏ ﻟﺩﻯ ﺍﻟﺒﺎﺤﺙ ﻹﻋﻁﺎﺀ ﺃﻱ ﻤﻥ
ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﺴﺘﻘﻠﺔ ﺃﻭﻟﻭﻴﺔ ﺃﻜﺒﺭ ﻓﻲ ﺩﺨﻭﻟﻬﺎ ﻟﻠﺘﺤﻠﻴل ﻤﻥ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻷﺨﺭﻯ ،ﻭﻟﺫﻟﻙ
) (16ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ
561
ﺘﻌﺘﺒﺭ ﺍﻟﻁﺭﻴﻘﺔ ﺍﻟﺜﺎﻟﺜﺔ )ﺍﻟﺘﺤﻠﻴل ﺍﻟﺘﺩﺭﻴﺠﻲ (SDAﻫﻲ ﺍﻟﻁﺭﻴﻘﺔ ﺍﻷﻜﺜﺭ ﺃﻫﻤﻴﺔ ﻭﺍﻷﻜﺜﺭ ﺘﻁﺒﻴﻘﺎﹰ ،ﻟﺫﻟﻙ ﺴﺘﻜﻭﻥ ﺍﻟﻁﺭﻴﻘﺔ ﺍﻟﻭﺤﻴﺩﺓ ﺍﻟﺘﻲ ﺴﺘﺘﻌﺭﺽ ﻟﻬﺎ ﺒﺎﻟﺸﺭﺡ ﻭﺍﻟﺘﻭﻀﻴﺢ ﻫﻨﺎ. ﺘﻌﺘﺒﺭ ﺍﻷﺴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ﻟﻁﺭﻴﻘﺔ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﺍﻟﺘﺩﺭﻴﺠﻲ SDAﻤﺸﺎﺒﻬﺔ
ﺇﻟﻰ ﺤﺩ ﻜﺒﻴﺭ ﻤﻥ ﺃﺴﺱ ﺍﻟﻁﺭﻴﻘﺔ ﺍﻟﺘﺩﺭﻴﺠﻴﺔ ﻟﻼﻨﺤﺩﺍﺭ ﺍﻟﻤﺘﻌﺩﺩ
Stepwise
،Regressionﻓﺄﺜﺭ ﺇﻀﺎﻓﺔ ﺃﻭ ﺤﺫﻑ ﺃﻱ ﻤﺘﻐﻴﺭ ﻤﺴﺘﻘل ﻴﺘﻡ ﻤﺭﺍﻗﺒﺘﻪ ﻋﻥ ﻁﺭﻴﻕ
ﺍﺨﺘﺒﺎﺭ ﺇﺤﺼﺎﺌﻲ ،ﻓﺎﻟﻨﺘﻴﺠﺔ ﺍﻟﺘﻲ ﻨﺤﺼل ﻋﻠﻴﻬﺎ ﻤﻥ ﺫﻟﻙ ﺍﻻﺨﺘﺒﺎﺭ ﺘﺴﺘﺨﺩﻡ ﻜﺄﺴﺎﺱ ﻹﺩﺨﺎل ﺫﻟﻙ ﺍﻟﻤﺘﻐﻴﺭ ﺇﻟﻰ ﺍﻟﺘﺤﻠﻴل ،ﻓﻌﻨﺩﻤﺎ ﻴﻜﻭﻥ ﻟﺩﻴﻨﺎ ﻓﺌﺘﻴﻥ ﻓﻘﻁ ﻓﺴﻭﻑ ﻴﻜﻭﻥ ﻟﺩﻴﻨﺎ
ﺩﺍﻟﺔ ﺘﻤﻴﻴﺯ Discriminant functionﻭﺍﺤﺩﺓ ﻓﻘﻁ ،ﻭﻟﻜﻥ ﻋﻨﺩﻤﺎ ﻴﻜﻭﻥ ﻫﻨﺎﻙ ﺍﻜﺜﺭ ﻤﻥ ﻓﺌﺘﻴﻥ ﻓﺈﻨﻪ ﻴﻤﻜﻥ ﺃﻥ ﻴﻜﻭﻥ ﻟﺩﻴﻨﺎ ﻋﺩﺩ ﻜﺒﻴﺭ ﻤﻥ ﺩﻭﺍل ﺍﻟﺘﻤﻴﻴﺯ ﻭﺫﻟﻙ ﻋﻠﻰ ﺍﻟﺭﻏﻡ ﺃﻨﻪ ﻤﻥ
ﻻ ﻤﻔﻴﺩﺓ ﺃﻜﺜﺭ ﻤﻥ ﺍﻟﺜﻼﺜﺔ ﺍﻷﻭﻟﻰ ﻓﻘﻁ. ﻏﻴﺭ ﺍﻟﻤﺄﻟﻭﻑ ﺃﻥ ﺘﻜﻭﻥ ﻫﻨﺎﻙ ﺩﻭ ﹰ
ﻭﻫﻨﺎﻙ ﺍﻟﻌﺩﻴﺩ ﻤﻥ ﺍﻻﺨﺘﺒﺎﺭﺍﺕ ﺍﻹﺤﺼﺎﺌﻴﺔ ﺍﻟﻤﺘﺎﺤﺔ ﻻﺘﺨﺎﺫ ﺍﻟﻘﺭﺍﺭ ﺒﺸﺄﻥ ﺇﻀﺎﻓﺔ
ﺃﻭ ﺤﺫﻑ ﻤﺘﻐﻴﺭ ﻤﺴﺘﻘل ﺇﻟﻰ ﺍﻟﺘﺤﻠﻴل ،ﻭﺃﻜﺜﺭ ﺍﻻﺨﺘﺒﺎﺭﺍﺕ ﺍﺴﺘﺨﺩﺍﻤﹰﺎ ﻓﻲ ﻫﺫﺍ ﺍﻟﻤﻀﻤﺎﺭ
ﻫﻭ ﺇﺤﺼﺎﺀ ﻻﻤﺩﺓ ﻟﻭﻴﻠﻜﺱ ) ، Wilks' Lambda (Λﻭﻴﺴﺘﺨﺩﻡ ﺘﻭﺯﻴﻊ Fﻓﻲ ﺍﻟﺤﻜﻡ
ﻋﻠﻰ ﻤﻌﻨﻭﻴﺔ Significanceﺍﻟﺘﻐﻴﺭ ﻓﻲ ﻗﻴﻤﺔ ﺍﻹﺤﺼﺎﺀ Λﺍﻟﻨﺎﺘﺞ ﻋﻥ ﺇﻀﺎﻓﺔ ﺃﻭ ﺤﺫﻑ
ﺃﻱ ﻤﻥ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﺴﺘﻘﻠﺔ ﺇﻟﻰ ﺍﻟﺘﺤﻠﻴل ،ﻭﻓﻲ ﻜل ﺨﻁﻭﺓ ﻤﻥ ﺍﻟﺨﻁﻭﺍﺕ ﻴﻀﺎﻑ ﺇﻟﻰ
ﺍﻟﺘﺤﻠﻴل ﺫﻟﻙ ﺍﻟﻤﺘﻐﻴﺭ ﺍﻟﺫﻱ ﻴﺘﻤﺘﻊ ﺒﺄﻜﺒﺭ ﻗﻴﻤﺔ ﻟﺩﺍﻟﺔ ﺍﻻﺨﺘﺒﺎﺭ ،(F TO ENTER) F ﻭﻴﺘﻡ ﺘﻜﺭﺍﺭ ﻋﻤﻠﻴﺔ ﺇﻀﺎﻓﺔ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺇﻟﻰ ﺍﻟﺘﺤﻠﻴل ﻟﺤﻴﻥ ﻋﺩﻡ ﺒﻘﺎﺀ ﺃﻱ ﻤﺘﻐﻴﺭ ﻴﺘﻤﺘﻊ
ﺒﻘﻴﻤﺔ ﻟﺩﺍﻟﺔ ﺍﻻﺨﺘﺒﺎﺭ Fﺃﻜﺒﺭ ﻤﻥ ﺍﻟﻘﻴﻤﺔ ﺍﻟﺤﺭﺠﺔ ﻟﻬﺫﺍ ﺍﻻﺨﺘﺒﺎﺭ ﻋﻨﺩ ﻤﺴﺘﻭﻯ ﻤﻌﻨﻭﻴﺔ
)ﺩﻻﻟﺔ( Significance levelﻤﺤﺩﺩ ﻤﺴﺒﻘﺎﹰ ،ﻭﻓﻲ ﻨﻔﺱ ﺍﻟﻭﻗﺕ ﻴﺘﻡ ﻓﺤﺹ ﻜل ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﺴﺘﻘﻠﺔ ﺍﻟﺘﻲ ﺘﻡ ﺇﻀﺎﻓﺘﻬﺎ ﻓﻲ ﺍﻟﺨﻁﻭﺍﺕ ﺍﻟﺴﺎﺒﻘﺔ ﻭﺘﺤﺫﻑ ﺘﻠﻙ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ
ﺍﻟﺘﻲ ﻟﻡ ﺘﻌﺩ ﺘﺴﺎﻫﻡ ﻓﻲ ﺘﻌﻅﻴﻡ ﺍﻟﺩﻗﺔ ﻓﻲ ﺘﻤﻴﻴﺯ ﺍﻟﻔﺌﺎﺕ ﺍﻟﺼﺤﻴﺤﺔ ﺍﻟﺘﻲ ﺘﻨﺘﻤﻲ ﺇﻟﻴﻬﺎ
ﻤﻔﺭﺩﺍﺕ ﺍﻟﺒﺤﺙ ﻨﻅﺭﹰﺍ ﻷﻥ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺘﻲ ﺃﻀﻴﻔﺕ ﻟﻠﺘﺤﻠﻴل ﻤﺘﺄﺨﺭﹰﺍ ﻗﺩ ﺴﻠﺒﺘﻬﺎ ﻫﺫﺍ
ﺍﻟﺩﻭﺭ ،ﻭﻫﺫﺍ ﻴﺘﻡ ﻋﻨﺩﻤﺎ ﺘﻨﺨﻔﺽ ﻗﻴﻤﺔ ﺩﺍﻟﺔ ﺍﻻﺨﺘﺒﺎﺭ (F TO REMOVE) Fﺇﻟﻰ ﻤﺴﺘﻭﻯ ﺃﻗل ﻤﻥ ﺍﻟﻘﻴﻤﺔ ﺍﻟﺤﺭﺠﺔ ﻟﻬﺫﺍ ﺍﻻﺨﺘﺒﺎﺭ ﻋﻨﺩ ﻤﺴﺘﻭﻯ ﺍﻟﻤﻌﻨﻭﻴﺔ ﺍﻟﻤﺤﺩﺩ .
) (16ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ
562
ﻓﻲ ﺁﺨﺭ ﺍﻷﻤﺭ ،ﺘﻨﺘﻬﻲ ﻋﻤﻠﻴﺔ ﺇﻀﺎﻓﺔ ﻭﺤﺫﻑ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺇﻟﻰ ﻭﻤﻥ ﺍﻟﺘﺤﻠﻴل ﻭﻴﺘﻡ ﺇﺩﺭﺍﺝ ﺠﺩﻭل ﻴﻠﺨﺹ ﺍﻟﻨﺘﺎﺌﺞ ﻭﻴﺒﻴﻥ ﺃﻱ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺘﻲ ﺃﻀﻴﻔﺕ ﺇﻟﻰ ﻭﺃﻴﻬﺎ
ﺤﺫﻓﺕ ﻤﻥ ﺍﻟﺘﺤﻠﻴل ﻓﻲ ﻜل ﺨﻁﻭﺓ ﻤﻥ ﺨﻁﻭﺍﺘﻪ ،ﻭﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺘﻲ ﺘﺒﻘﺕ ﻓﻲ ﺍﻟﺘﺤﻠﻴل
ﻫﻲ ﺍﻟﺘﻲ ﺘﺴﺘﺨﺩﻡ ﻓﻲ ﺒﻨﺎﺀ ﺩﺍﻟﺔ )ﺩﻭﺍل( ﺍﻟﺘﻤﻴﻴﺯ ) ، Discriminant function(sﺜﻡ
ﻴﺒﻴﻥ ﺍﻟﺠﺩﻭل ﺍﻟﺫﻱ ﻴﻠﻴﻪ ﻤﺒﺎﺸﺭﺓ ﻓﻲ ﻗﺎﺌﻤﺔ ﺍﻟﻨﺘﺎﺌﺞ ﺃﻱ ﺍﻟﺩﻭﺍل ﻴﻤﻜﻥ ﺍﻻﻋﺘﻤﺎﺩ ﻋﻠﻴﻬﺎ
ﺇﺤﺼﺎﺌﻴﺎﹰ ،ﻭﺘﻌﻁﻲ ﺍﻟﺩﺍﻟﺔ ﺍﻷﻭﻟﻰ ﻓﻲ ﻫﺫﺍ ﺍﻟﺠﺩﻭل ﺍﻟﻭﺴﻴﻠﺔ ﺍﻷﻓﻀل ﻟﻠﺘﻨﺒﺅ ﺒﺎﻟﻔﺌﺎﺕ
ﺍﻟﺼﺤﻴﺤﺔ ﺍﻟﺘﻲ ﺘﻨﺘﻤﻲ ﺇﻟﻴﻬﺎ ﻤﻔﺭﺩﺍﺕ ﺍﻟﻤﺠﺘﻤﻊ ،ﺒﻴﻨﻤﺎ ﻗﺩ ﺘﻜﻭﻥ ﺍﻟﺩﻭﺍل ﺍﻟﺘﺎﻟﻴﺔ ﻟﻬﺎ ﻤﻔﻴﺩﺓ ﻓﻲ ﻋﻤﻠﻴﺔ ﺍﻟﺘﻨﺒﺅ ﻭﻗﺩ ﻻ ﺘﻜﻭﻥ ﻜﺫﻟﻙ ،ﻭﻴﻤﻜﻥ ﻟﻠﻤﺴﺘﺨﺩﻡ ﻁﻠﺏ ﺠﺩﺍﻭل ﺃﺨﺭﻯ
)ﺍﺨﺘﻴﺎﺭﻴﹰﺎ( ﺘﺒﻴﻥ ﺍﻟﺩﻭﺍل ﺍﻷﺨﺭﻯ ﻭﻤﻌﺩﻻﺕ ﻨﺠﺎﺤﻬﺎ ﻓﻲ ﻋﻤﻠﻴﺔ ﺍﻟﺘﻨﺒﺅ ،ﻜﻤﺎ ﻴﻤﻜﻥ ﺃﻴﻀﹰﺎ ﻁﻠﺏ ﺍﻟﻤﺯﻴﺩ ﻤﻥ ﺍﻟﺘﻭﻀﻴﺢ ﻟﻬﺫﻩ ﺍﻟﺩﻭﺍل ﺒﺎﺴﺘﺨﺩﺍﻡ ﺍﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﺒﻴﺎﻨﻴﺔ ،ﺤﻴﺙ ﻴﻭﻓﺭ ﺍﻟﺒﺭﻨﺎﻤﺞ ﺇﻤﻜﺎﻨﻴﺔ ﻁﻠﺏ ﺍﻟﻌﺩﻴﺩ ﻤﻥ ﺍﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﺒﻴﺎﻨﻴﺔ ﺍﻟﺨﺎﺼﺔ ﺒﺫﻟﻙ ﺍﻷﻤﺭ.
.3 .16اﻟﺘﺤﻠﻴﻞ اﻟﻄﺒﻘﻲ ﺑﺎﺳﺘﺨﺪام ﻧﻈﺎم : SPSS Discriminant Analysis with SPSS : ﻟﺘﻭﻀﻴﺢ ﻜﻴﻔﻴﺔ ﺍﺴﺘﺨﺩﺍﻡ ﻨﻅﺎﻡ SPSSﻓﻲ ﺍﻟﺘﺤﻠﻴﻠﻲ ﺍﻟﻁﺒﻘﻲ Discriminant
Analysisﺴﻨﺄﺨﺫ ﻤﺠﻤﻭﻋﺔ ﺒﻴﺎﻨﺎﺕ ﺤﻘﻴﻘﻴﺔ ﺘﺘﻌﻠﻕ ﺒﻤﺸﻜﻠﺔ ﺒﺤﺙ ﺤﻘﻴﻘﻴﺔ ،ﻭﻴﻤﻜﻥ ﺘﻭﻀﻴﺢ ﻜل ﻤﻥ ﺍﻟﺒﻴﺎﻨﺎﺕ ﻭﻤﺸﻜﻠﺔ ﺍﻟﺒﺤﺙ ﻜﻤﺎ ﻴﻠﻲ:
ﻴﺭﻏﺏ ﻤﺭﺸﺩ ﺃﻜﺎﺩﻴﻤﻲ ﻓﻲ ﺃﺤﺩ ﺍﻟﺠﺎﻤﻌﺎﺕ ﺍﻟﻭﺼﻭل ﺇﻟﻰ ﻗﺎﻋﺩﺓ ﺘﻤﻜﻨﻪ ﻤﻥ
ﻤﺴﺎﻋﺩﺓ ﺍﻟﻁﻠﺒﺔ ﺍﻟﺠﺩﺩ ﺒﺈﺭﺸﺎﺩﻫﻡ ﺇﻟﻰ ﺍﻟﺘﺨﺼﺹ ﺍﻟﺠﺎﻤﻌﻲ ﺍﻟﺫﻱ ﻴﺘﻨﺎﺴﺏ ﻤﻊ ﺇﻤﻜﺎﻨﺎﺘﻬﻡ
ﻭﻤﻭﺍﻫﺒﻬﻡ ،ﻭﻓﻲ ﺒﺤﺜﻪ ﻋﻥ ﻤﻌﻠﻭﻤﺎﺕ ﻤﻔﺼﻠﺔ ﻋﻥ ﺍﻟﻁﻠﺒﺔ ﺍﺴﺘﻁﺎﻉ ﺘﻭﻓﻴﺭ ﻤﺠﻤﻭﻋﺔ ﻤﻨﻬﺎ ﻜﺎﻨﺕ ﻗﺩ ﺠﻤﻌﺕ ﻓﻲ ﺃﺤﺩ ﺍﻟﺒﺤﻭﺙ ﺍﻟﺴﺎﺒﻘﺔ ﻋﻥ ﺨﻠﻔﻴﺔ ﻭﻨﺘﻴﺠﺔ ﺍﻟﺜﺎﻨﻭﻴﺔ ﺍﻟﻌﺎﻤﺔ ﻟﻌﺩﺩ ﻤﻥ
ﺍﻟﻁﻠﺒﺔ ﻓﻲ ﺜﻼﺙ ﺃﻗﺴﺎﻡ ﺘﺨﺼﺼﻴﺔ ،ﻭﻫﻲ ﺍﻟﻬﻨﺩﺴﺔ ﺍﻟﻤﻌﻤﺎﺭﻴﺔ Architectureﻭﺍﻟﻬﻨﺩﺴﺔ ﺍﻟﻤﺩﻨﻴﺔ Civil engineeringﻭﻋﻠﻡ ﺍﻟﻨﻔﺱ ،Psychologyﻜﻤﺎ ﺍﺴﺘﻁﺎﻉ ﻫﺫﺍ ﺍﻟﻤﺭﺸﺩ ﺘﻭﻓﻴﺭ ﺒﻴﺎﻨﺎﺕ ﻜﺎﻨﺕ ﻗﺩ ﺠﻤﻌﺕ ﻤﻥ ﻭﺍﻗﻊ ﺍﺴﺘﺒﻴﺎﻥ ﻋﺎﻡ ﻟﺠﻤﻴﻊ ﺍﻟﻁﻠﺒﺔ )ﻭﻤﻥ ﺒﻴﻨﻬﻡ ﺍﻟﻁﻠﺒﺔ
) (16ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ
563
ﻓﻲ ﺍﻟﻌﻴﻨﺔ( ﻴﺸﻤل ﺃﺴﺌﻠﺔ ﺘﺘﻌﻠﻕ ﺒﻬﻭﺍﻴﺎﺘﻬﻡ ﻭﺘﻭﺠﻬﺎﺘﻬﻡ ،ﻭﺍﻟﺠﺩﻭل ﻓﻲ ﺍﻟﺸﻜل 2-16 ﻴﻭﻀﺢ ﺘﻔﺎﺼﻴل ﻋﻥ ﺠﻤﻴﻊ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺘﻲ ﺍﺴﺘﻁﺎﻉ ﻫﺫﺍ ﺍﻟﻤﺭﺸﺩ ﺘﻭﻓﻴﺭﻫﺎ ﻤﻥ ﺠﻤﻴﻊ ﺍﻟﻤﺼﺎﺩﺭ ﻟﻌﻴﻨﺔ ﺍﻟﻁﻠﺒﺔ ﻭﺍﻟﺠﺩﻭل ﻓﻲ ﺸﻜل 3-16ﻴﻌﻁﻲ ﺍﻟﺒﻴﺎﻨﺎﺕ ﺫﺍﺘﻬﺎ ﻋﻥ ﻫﺅﻻﺀ
ﺍﻟﻁﻠﺒﺔ.
ﻭﻴﻤﻜﻥ ﺍﻵﻥ ﺘﺤﺩﻴﺩ ﻭﺇﻋﺎﺩﺓ ﺼﻴﺎﻏﺔ ﻤﺸﻜﻠﺔ ﺍﻟﺒﺤﺙ ﻤﺭﻜﺯ ﺍﻫﺘﻤﺎﻡ ﺍﻟﻤﺭﺸﺩ ﻜﻤﺎ
ﻴﻠﻲ :ﻫل ﻴﻤﻜﻥ ﺍﻟﺘﻨﺒﺅ ﺒﺎﻟﺘﺨﺼﺹ ﺍﻟﺠﺎﻤﻌﻲ ﻟﻠﻁﺎﻟﺏ ﺒﻌﺩ ﺍﻨﺘﻬﺎﺌﻪ ﻤﻥ ﺩﺭﺍﺴﺔ ﺍﻟﺜﺎﻨﻭﻴﺔ ﺍﻟﻌﺎﻤﺔ )ﻗﺒل ﺩﺨﻭﻟﻪ ﺍﻟﺠﺎﻤﻌﺔ( ﻤﻥ ﺨﻼل ﺒﻴﺎﻨﺎﺕ ﺘﺘﻌﻠﻕ ﺒﻌﺩﺩ ﻤﻥ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﻋﻥ ﻫﻭﺍﻴﺎﺘﻪ ﻭﺘﺤﺼﻴﻠﻪ ﺍﻟﺴﺎﺒﻕ؟ ﻭﺒﺎﻟﺘﺎﻟﻲ ﻓﺈﻨﻪ ﻓﻲ ﻫﺫﺍ ﺍﻟﺒﺤﺙ ﻴﻌﺘﺒﺭ ﺘﺨﺼﺹ ﺍﻟﻁﺎﻟﺏ
ﻤﺘﻐﻴﺭﹰﺍ ﺘﺎﺒﻌﹰﺎ Dependent Variableﻭﺘﻌﺘﺒﺭ ﺒﺎﻗﻲ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻷﺨﺭﻯ ﻤﺘﻐﻴﺭﺍﺕ ﻤﺴﺘﻘﻠﺔ . Independent Variables
ﻭﺍﻟﺨﻁﻭﺓ ﺍﻷﻭﻟﻰ ،ﻭﻜﻤﺎ ﻫﻲ ﺍﻟﻌﺎﺩﺓ ،ﻫﻲ ﺇﺩﺨﺎل ﺍﻟﺒﻴﺎﻨﺎﺕ ﻟﻨﻅﺎﻡ SPSSﻓﻲ
ﻤﺤﺭﺭ ﺍﻟﺒﻴﺎﻨﺎﺕ ،Data Editorﻓﻴﺘﻡ ﺘﻌﺭﻴﻑ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﻭﺩﻟﻴل ﻜل ﻤﺘﻐﻴﺭ ﺒﺎﻹﻀﺎﻓﺔ
ﺇﻟﻰ ﺩﻟﻴل ﺍﻟﻘﻴﻡ ﻟﻠﻤﺘﻐﻴﺭﻴﻥ ﺘﺨﺼﺹ ﺍﻟﻁﺎﻟﺏ ﻭﻨﻭﻋﻪ ،ﻭﻻ ﺤﺎﺠﺔ ﻹﺩﺨﺎل ﺭﻗﻡ ﺍﻟﻁﺎﻟﺏ ﺇﺫ ﺃﻨﻪ ﻴﺘﻡ ﺘﺭﻗﻴﻡ ﺍﻟﻤﻔﺭﺩﺍﺕ ﻓﻲ ﻤﺤﺭﺭ ﺍﻟﺒﻴﺎﻨﺎﺕ ﺘﻠﻘﺎﺌﻴﺎﹰ ،ﻭﻴﺠﺩﺭ ﺒﺎﻟﺫﻜﺭ ﺃﻥ ﻤﺘﻐﻴﺭ ﺍﻟﺘﺤﺼﻴل ﺍﻟﻤﺩﺭﺴﻲ ﻭﻫﻭ ﺍﻟﻤﺘﻐﻴﺭ ﺍﻷﺨﻴﺭ ﻴﺤﺘﻭﻱ ﻋﻠﻰ ﻋﺩﺩ ﻤﻥ ﺍﻟﻘﻴﻡ ﺍﻟﻤﻔﻘﻭﺩﺓ
Missing
valuesﻓﻴﺘﻡ ﺘﻌﺭﻴﻑ ﺍﻟﻘﻴﻡ ﺍﻟﻤﻔﻘﻭﺩﺓ ﻓﻲ ﻨﺎﻓﺫﺓ ﺘﻌﺭﻴﻑ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﻋﻠﻰ ﺃﻨﻬﺎ ﺍﻟﻘﻴﻤﺔ 99 ﻓﻘﻁ ﻓﻲ ﺍﻟﻨﺎﻓﺫﺓ ﺍﻟﻔﺭﻋﻴﺔ ﻟﺘﺤﺩﻴﺩ ﺍﻟﻘﻴﻡ ﺍﻟﻤﻔﻘﻭﺩﺓ ﺘﺤﺕ ﺒﻨﺩ ﻗﻴﻡ ﻤﻔﻘﻭﺩﺓ ﻤﺘﻘﻁﻌﺔ Discrete
missing valuesﻭﺫﻟﻙ ﻜﻤﺎ ﺘﻡ ﺘﻭﻀﻴﺤﻪ ﻓﻲ ﺍﻟﻘﺴﻡ ﺍﻟﺜﺎﻨﻲ ﻤﻥ ﺍﻟﻔﺼل ﺍﻟﺜﺎﻨﻲ ﻤﻥ ﻫﺫﺍ ﺍﻟﻜﺘﺎﺏ ،ﻭﻓﻲ ﺍﻟﻨﻬﺎﻴﺔ ﻴﺘﻡ ﺇﺩﺨﺎل ﺠﻤﻴﻊ ﺍﻟﺒﻴﺎﻨﺎﺕ ﻤﻔﺭﺩﺓ ﺘﻠﻭ ﺍﻷﺨﺭﻯ ﻟﺘﻅﻬﺭ ﺸﺎﺸﺔ ﻤﺤﺭﺭ ﺍﻟﺒﻴﺎﻨﺎﺕ Data Editorﻜﻤﺎ ﺒﺎﻟﺸﻜل 1-16ﺃﺩﻨﺎﻩ . ﺸﻜل : 1-16ﺠﺎﻨﺏ ﻤﻥ ﺸﺎﺸﺔ ﻤﺤﺭﺭ ﺍﻟﺒﻴﺎﻨﺎﺕ Data Editorﻴﺒﻴﻥ
ﺃﺴﻤﺎﺀ ﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺩﺭﺍﺴﺔ ﺒﺎﻹﻀﺎﻓﺔ ﺇﻟﻰ ﺍﻷﺭﺒﻊ ﻗﻴﻡ ﺍﻷﻭﻟﻰ ﻓﻲ ﺍﻟﺒﻴﺎﻨﺎﺕ.
) (16ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ
564
ﺸﻜل :2-16ﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺩﺭﺍﺴﺔ ﻜﻤﺎ ﺘﻤﻜﻥ ﺍﻟﻤﺭﺸﺩ ﺍﻷﻜﺎﺩﻴﻤﻲ ﻤﻥ ﺠﻤﻌﻬﺎ ﻤﻥ ﻋﻴﻨﺔ ﺍﻟﻁﻠﺒﺔ*
ﺍﻟﻌﻤﻭﺩ C1
ﻤﺤﺘﻭﻴﺎﺕ ﺍﻟﻤﺘﻐﻴﺭ ﺭﻗﻡ ﺍﻟﻁﺎﻟﺏ
ﺍﺴﻡ
ﺩﻟﻴل ﺍﻟﻤﺘﻐﻴﺭ
ﺍﻟﻤﺘﻐﻴﺭ
Case Number
nucase
ﺘﺨﺼﺹ ﺍﻟﻁﺎﻟﺏ: .1ﺍﻟﻌﻤﺎﺭﺓ
C2
.2ﺍﻟﻬﻨﺩﺴﺔ ﺍﻟﻤﺩﻨﻴﺔ
Study Subject: 1. Architects 2. Psychologists 3. Civil Engineer
studsubj
.3ﻋﻠﻡ ﺍﻟﻨﻔﺱ ﻨﻭﻉ ﺍﻟﻁﺎﻟﺏ
.1ﺫﻜﺭ
C3
Sex of Student : 1. Male 2. Female
sex
.2ﺃﻨﺜﻰ
Interest in Construction Kits Interest in Modeling Kits
C4
ﺍﻻﻫﺘﻤﺎﻡ ﻓﻲ ﺃﺩﻭﺍﺕ ﺍﻟﺒﻨﺎﺀ
conkit
C5
ﺍﻻﻫﺘﻤﺎﻡ ﻓﻲ ﺃﺩﻭﺍﺕ ﺍﻟﺘﺨﻁﻴﻁ
model
C6
ﺍﻻﻫﺘﻤﺎﻡ ﻓﻲ ﺍﻟﺭﺴﻡ ﺍﻟﻬﻨﺩﺴﻲ
draw
Interest in Drawing
paint
Interest in Painting
outdoor
Interest in Outdoor Pursuits
C7
ﺍﻻﻫﺘﻤﺎﻡ ﻓﻲ ﺍﻟﺭﺴﻡ ﺍﻟﻔﻨﻲ
ﺍﻻﻫـﺘﻤﺎﻡ ﻓـﻲ ﺍﻟﻌﻤل ﺍﻟﺤﺭﻓﻲ
C8
ﺍﻟﺨﺎﺭﺠﻲ
C9
ﺍﻻﻫﺘﻤﺎﻡ ﻓﻲ ﺍﻟﺤﺎﺴﻭﺏ
comput
C10
ﺍﻟﻘﺩﺭﺓ ﻋﻠﻰ ﺘﺨﻴل ﺍﻟﻨﻤﺎﺫﺝ
vismod
C11
ﺍﻟﺘﺤﺼﻴل ﺍﻟﻤﺩﺭﺴﻲ **
quals
Interest in Computing Ability to Visualize Model ** School Qualifications
* ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﻤﻥ C4ﺇﻟﻰ C10ﻋﺒﺎﺭﺓ ﻋﻥ ﻤﺘﻐﻴﺭﺍﺕ ﻜﻤﻴﺔ ﺘﺭﺘﻴﺒﻴﺔ ﺘﺄﺨﺫ ﻗﻴﻤﹰﺎ ﻤﻥ 0ﺇﻟﻰ 10ﻭﺘﻌﺒﺭ ﻋﻥ ﻤﺴﺘﻭﻯ ﺍﻟﻁﺎﻟﺏ ﻓﻲ ﻫﺫﻩ ﺍﻟﻬﻭﺍﻴﺔ ﺒﻴﻨﻤﺎ ﺍﻟﻤﺘﻐﻴﺭ C11ﻜﻤﻲ ﺃﻴﻀﹰﺎ ﻭﻴﺄﺨﺫ ﻗﻴﻤﹰﺎ ﺒﻴﻥ 0ﻭ .30
** ﻫﻨﺎﻙ ﻋﺩﺩ ﻤﻥ ﺍﻟﺤﺎﻻﺕ ﺍﻟﻤﻔﻘﻭﺩﺓ Missing Valuesﻓﻲ ﻫﺫﺍ ﺍﻟﻤﺘﻐﻴﺭ ، C11ﻭﻗﺩ
ﺃﻋﻁﻴﺕ ﺍﻟﻘﻴﻤﺔ 99ﻟﻠﺩﻻﻟﺔ ﻋﻠﻰ ﺃﻨﻬﺎ ﻤﻔﻘﻭﺩﺓ ﻓﻲ ﺠﺩﻭل ﺍﻟﺒﻴﺎﻨﺎﺕ.
) (16ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ
565
ﺸﻜل : 3-16ﺍﻟﺒﻴﺎﻨﺎﺕ ﺍﻟﺘﻲ ﺘﻤﻜﻥ ﺍﻟﻤﺭﺸﺩ ﺍﻷﻜﺎﺩﻴﻤﻲ ﻤﻥ ﺠﻤﻌﻬﺎ ﻋﻥ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺴﺎﺒﻘﺔ C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 99 10 8 99 7 10 5 5 6 7 4 7 8 6 8 1 99 16 9 5 5 8 6 2 2 6 8 5 4 8 7 9 7 9 9 5 14 6 99 14
5 6 4 6 6 4 3 6 4 7 6 6 6 6 6 7 5 5 4 9 7 5 6 4 6 6 6 4 6 5 6 4 6 2 6 3 4 5 5 4
0 0 3 3 2 0 0 3 1 0 1 0 4 0 2 3 0 3 3 4 4 3 4 2 3 2 0 1 0 2 0 2 1 1 1 4 4 3 2 4
1 2 0 4 0 1 0 3 4 0 5 3 0 0 3 0 4 1 4 3 3 6 2 1 2 4 1 0 2 4 8 2 5 1 1 1 4 6 3 0
0 2 7 1 2 1 0 0 3 6 3 4 4 0 5 0 0 2 0 0 0 2 3 3 2 4 2 0 1 4 8 4 3 0 1 0 2 1 0 1
2 6 6 7 3 7 0 2 7 10 5 8 6 4 7 0 6 6 5 4 4 5 8 4 7 4 5 2 3 5 6 7 7 0 3 0 6 6 0 3
3 2 5 6 2 2 2 1 1 4 6 8 4 2 3 0 4 6 2 3 6 7 6 1 10 5 1 4 6 2 6 2 10 2 4 2 1 3 1 1
2 3 5 5 3 2 2 4 3 5 4 3 2 2 2 0 4 4 6 2 5 5 6 3 6 3 2 4 0 2 4 4 4 2 2 2 4 4 4 3
1 1 2 1 1 2 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1 1 1 1 1 1 2 1 1 2 2 1 2 1 2 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
) (16ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ
566
ﺘﺎﺒﻊ ﺸﻜل ...... 3-16 C8 C9 C10 C11
C7
C6
C5
C4
C3
C2
C1
0 5 3 2 3 2 0 0 4 3 0 2 2 3 1 1 1 2 3 0 0 0 3 0 2 1 1 2 0 1 1 2 1 5 1 0 3 2 0 0
0 7 4 3 5 4 1 0 7 6 3 6 5 5 3 1 4 2 5 5 5 7 8 1 6 2 2 5 0 5 2 4 5 5 2 7 5 4 0 0
4 6 3 5 7 2 1 2 7 6 1 3 4 3 2 2 2 3 9 3 0 6 8 0 2 3 5 8 2 4 1 7 3 4 4 1 5 4 2 0
1 1 3 1 1 4 1 1 2 3 6 2 3 1 0 1 2 1 3 4 1 3 3 1 1 1 1 2 2 5 3 5 4 4 2 1 6 8 1 4
2 2 2 2 2 1 1 2 2 2 1 2 2 2 2 2 2 2 2 1 1 2 2 1 1 2 2 2 1 1 2 2 1 1 2 1 1 1 1 1
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3
41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
12 8 13 11 15 8 9 6 11 12 9 11 10 15 9 3 13 4 6 9 11 99 99 99 9 4 10 99 8 9 1 12 9 10 11 10 13 9 12 99
6 6 3 3 5 5 4 3 5 6 6 3 5 5 2 4 2 1 3 3 3 5 5 3 4 3 4 5 6 2 3 6 5 5 6 3 4 7 3 2
2 2 1 0 0 2 4 0 0 3 3 0 2 0 1 0 0 1 0 4 2 0 0 2 4 0 2 0 4 4 1 1 2 3 1 4 0 0 1 0
2 5 3 5 4 6 1 2 2 8 5 2 2 6 1 4 4 2 6 1 4 4 7 0 0 1 4 3 8 0 4 6 5 7 4 3 4 0 0 0
) (16ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ
567
ﺘﺎﺒﻊ ﺸﻜل ...... 3-16 C8 C9 C10 C11
C7
C6
C5
C4
C3
C2
C1
4 1 0 1 0 0 2 1 0 3 0 1 0 0 0 0 0 1 0 0 1 0 1 2 0 1 3 0 1 2 0 1 0 0 0 2 0 0
10 2 0 2 1 3 3 6 5 6 2 3 3 4 1 4 2 5 0 2 5 3 0 2 0 4 6 4 3 5 4 2 9 0 2 6 0 1
4 3 3 1 4 9 9 2 1 5 1 3 6 2 2 2 2 1 0 2 2 2 3 5 4 6 3 3 4 3 4 2 1 3 0 2 2 5
4 3 3 3 6 6 4 2 4 6 2 6 5 3 4 4 3 4 1 6 4 2 5 3 4 5 5 5 6 6 2 5 1 3 6 4 3 3
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1
3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118
13 12 5 8 14 99 13 8 5 6 6 11 0 7 10 7 0 7 6 7 9 16 9 23 9 13 8 13 11 11 17 12 8 12 8 11 10 10
5 7 5 5 5 6 7 7 6 4 4 4 3 4 5 4 4 7 2 4 4 3 3 6 5 6 4 4 5 4 4 5 2 6 5 5 4 3
0 1 1 1 3 3 3 2 3 0 3 1 3 1 4 1 2 2 0 2 1 1 1 1 0 3 2 3 3 2 2 4 4 2 0 3 3 0
0 1 0 4 3 6 5 2 6 3 4 0 4 0 3 2 0 0 6 4 4 0 1 0 2 5 3 1 3 1 0 5 2 4 0 6 4 2
) (16ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ
568
ﻭﺒﻌﺩ ﺍﻻﻨﺘﻬﺎﺀ ﻤﻥ ﺇﺩﺨﺎل ﺍﻟﺒﻴﺎﻨﺎﺕ ﻴﺤﺴﻥ ﻤﺭﺍﺠﻌﺘﻬﺎ ﻭﺍﻟﺘﺤﻘﻕ ﻤﻥ ﺼﺤﺔ ﺇﺩﺨﺎﻟﻬﺎ ﻋﻥ ﻁﺭﻴﻕ ﺍﺴﺘﻜﺸﺎﻑ ﺒﻌﺽ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﻭﺘﻜﻭﻴﻥ ﺒﻌﺽ ﺍﻟﺠﺩﺍﻭل ﺍﻟﻤﺯﺩﻭﺠﺔ ﻭﻓﺤﺼﻬﺎ،
ﻭﺒﻌﺩﻫﺎ ﻴﻤﻜﻥ ﺍﻟﺒﺩﺀ ﻓﻲ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ،Discriminant Analysisﻭﻨﻘﻁﺔ ﺍﻟﺒﺩﺀ ﻓﻲ
ﺠﻤﻴﻊ ﺍﻷﺤﻭﺍل ﻫﻲ ﺍﺨﺘﻴﺎﺭ ﺃﻤﺭ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ Discriminant Analysisﻤﻥ ﻗﺎﺌﻤﺔ ﺍﻟﺘﺼﻨﻴﻑ Classifyﻓﻲ ﻗﺎﺌﻤﺔ ﺍﻟﺘﺤﻠﻴل ﺍﻹﺤﺼﺎﺌﻲ ) Analyzeﺃﻭ Statisticsﻓﻲ ﺇﺼﺩﺍﺭ 8.0ﻟﻠﻨﻅﺎﻡ( ﻓﻲ ﺸﺎﺸﺔ ﻤﺤﺭﺭ ﺍﻟﺒﻴﺎﻨﺎﺕ Data Editorﻭﺫﻟﻙ ﻜﻤﺎ ﻓﻲ ﺸﻜل
4-16ﺃﺩﻨﺎﻩ ،ﻭﺍﺨﺘﻴﺎﺭ ﻫﺫﺍ ﺍﻷﻤﺭ ﺴﻭﻑ ﻴﻔﺘﺢ ﻨﺎﻓﺫﺓ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ Discriminant
، Analysisﻭﺸﻜل 5-16ﻴﺒﻴﻥ ﻫﺫﻩ ﺍﻟﻨﺎﻓﺫﺓ ﺒﻌﺩ ﺇﺩﺨﺎل ﺍﻟﻤﻌﻠﻭﻤﺎﺕ ﺍﻟﻤﻁﻠﻭﺒﺔ ﻟﻬﺎ. ﺸﻜل : 4-16ﺍﻟﻭﺼﻭل ﺇﻟﻰ ﺃﻤﺭ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ Discriminant Analysisﻤﻥ ﺨﻼل ﻗﺎﺌﻤﺔ ﺍﻟﺘﺤﻠﻴل ﺍﻹﺤﺼﺎﺌﻲ ﻓﻲ ﻤﺤﺭﺭ ﺍﻟﺒﻴﺎﻨﺎﺕ . Data Editor
) (16ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ
569
ﺸﻜل : 5-16ﻨﺎﻓﺫﺓ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ Discriminant Analysisﺒﻌﺩ ﺇﺩﺨﺎل ﺠﻤﻴﻊ ﺍﻟﻤﻌﻠﻭﻤﺎﺕ ﺍﻟﻤﻁﻠﻭﺒﺔ ﻟﻬﺎ.
ﻓﻲ ﺘﻠﻙ ﺍﻟﻨﺎﻓﺫﺓ ﺍﺨﺘﺭ ﺍﺴﻡ ﺍﻟﻤﺘﻐﻴﺭ ﺍﻟﺘﺎﺒﻊ )ﻫﻨﺎ ﺘﺨﺼﺹ ﺍﻟﻁﺎﻟﺏ (studsubj ﻭﺃﺩﺨﻠﻪ ﻓﻲ ﻤﺭﺒﻊ ﻤﺘﻐﻴﺭ ﺍﻟﺘﺼﻨﻴﻑ Grouping Variableﺜﻡ ﻟﺘﺤﺩﻴﺩ ﻓﺌﺎﺕ ﻫﺫﺍ
ﺍﻟﻤﺘﻐﻴﺭ ﺍﻀﻐﻁ ﻋﻠﻰ ﺘﺤﺩﻴﺩ ﻤﺩﻯ ﺍﻟﻤﺘﻐﻴﺭ Define Rangeﻭﺍﻜﺘﺏ ﺍﻟﺤﺩ ﺍﻷﺩﻨﻰ
) Minimumﻭﻫﻭ ﻫﻨﺎ (1ﻭﻜﺫﻟﻙ ﺍﻟﺤﺩ ﺍﻷﻋﻠﻰ ) Maximumﻭﻫﻭ ﻫﻨﺎ (3ﻭﺫﻟﻙ ﻓﻲ ﻨﺎﻓﺫﺓ ﺍﻟﺤﺩﻴﻥ ﺍﻷﺩﻨﻰ ﻭﺍﻷﻋﻠﻰ ﺍﻟﻔﺭﻋﻴﺔ ﺍﻟﺼﻐﻴﺭﺓ ،ﺜﻡ ﺍﻨﻘل ﺠﻤﻴﻊ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻷﺨﺭﻯ
)ﺍﻟﻤﺴﺘﻘﻠﺔ( ﺇﻟﻰ ﻤﺭﺒﻊ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﺴﺘﻘﻠﺔ ،Independent Variablesﻭﺤﻴﺙ ﺃﻨﻨﺎ ﺴﻨﻘﻭﻡ ﺒﺎﺴﺘﺨﺩﺍﻡ ﺍﻟﻁﺭﻴﻘﺔ ﺍﻟﺘﺩﺭﻴﺠﻴﺔ ﻓﻲ ﺍﻟﺘﺤﻠﻴل ﻓﻘﻡ ﺒﺘﺤﺩﻴﺩ ﺨﻴﺎﺭ ﺍﺴﺘﺨﺩﻡ ﺍﻟﻁﺭﻴﻘﺔ ﺍﻟﺘﺩﺭﻴﺠﻴﺔ Use stepwise methodﻓﻲ ﺃﺴﻔل ﺍﻟﻨﺎﻓﺫﺓ.
ﻫﻨﺎﻙ ﺒﻌﺽ ﺍﻟﺨﻴﺎﺭﺍﺕ ﺍﻟﻤﺘﺎﺤﺔ ﻤﻊ ﻫﺫﺍ ﺍﻷﻤﺭ ﻭﻤﻥ ﺃﻫﻤﻬﺎ ﺠﺩﺍﻭل ﺘﺤﻠﻴل ﺍﻟﺘﺒﺎﻴﻥ
ANOVAsﻟﻜل ﻤﻥ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﻭﻓﻲ ﺍﻟﻤﺴﺘﻭﻴﺎﺕ ﺍﻟﺜﻼﺙ ﻟﻠﻤﺘﻐﻴﺭ ﺍﻟﺘﺎﺒﻊ ﻭﻴﺤﺴﻥ ﺍﺨﺘﻴﺎﺭﻩ ﻭﺍﺨﺘﻴﺎﺭ ﺠﺩﻭل ﺘﻠﺨﻴﺼﻲ Summary tableﻴﺒﻴﻥ ﻋﺩﺩ ﻤﺭﺍﺕ ﺍﻟﻨﺠﺎﺡ ﻭﺍﻟﻔﺸل
ﻓﻲ ﺍﻟﺘﻨﺒﺅ ﺒﺎﻟﻔﺌﺎﺕ ﺍﻟﺘﻲ ﺘﻨﺘﻤﻲ ﺇﻟﻴﻬﺎ ﺍﻟﻤﻔﺭﺩﺍﺕ ﻓﻲ ﺍﻟﺘﺤﻠﻴل ،ﻭﻟﻠﺤﺼﻭل ﻋﻠﻰ ﺠﺩﺍﻭل
ﺘﺤﻠﻴل ﺍﻟﺘﺒﺎﻴﻥ ANOVAsﻟﻜل ﻤﻥ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻀﻐﻁ ﻋﻠﻰ ﻤﺭﺒﻊ ﺤﻭﺍﺭ ﺍﻹﺤﺼﺎﺀﺍﺕ
Statisticsﻭﻤﻥ ﺨﻼل ﻤﺭﺒﻊ ﺍﻹﺤﺼﺎﺀﺍﺕ ﺍﻟﻭﺼﻔﻴﺔ Descriptivesﺍﺨﺘﺭ ﺠﺩﺍﻭل
ﺘﺤﻠﻴل ﺍﻟﺘﺒﺎﻴﻥ ﺍﻷﺤﺎﺩﻴﺔ ﺍﻟﻤﺘﻐﻴﺭ Univariate ANOVAsﺜﻡ ﺍﻀﻐﻁ ﻤﺭﺒﻊ ﺍﻻﺴﺘﻤﺭﺍﺭ
) (16ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ
570
، Continueﻭﻟﻠﺤﺼﻭل ﻋﻠﻰ ﺠﺩﻭل ﺘﻠﺨﻴﺼﻲ Summary tableﻴﺒﻴﻥ ﻋﺩﺩ ﻤﺭﺍﺕ ﺍﻟﻨﺠﺎﺡ ﻭﺍﻟﻔﺸل ﻓﻲ ﺍﻟﺘﻨﺒﺅ ﺒﺎﻟﻔﺌﺎﺕ ﺍﺨﺘﺭ ﻤﺭﺒﻊ ﺍﻟﺤﻭﺍﺭ ﺘﺼﻨﻴﻑ ، Classifyﻭﻤﻥ
ﺨﻼل ﻤﺭﺒﻊ ﺍﻟﻌﺭﺽ Displayﺍﺨﺘﺭ ﺍﻟﺠﺩﻭل ﺍﻟﺘﻠﺨﻴﺼﻲ Summary tableﺜﻡ
ﺍﻀﻐﻁ ﻤﺭﺒﻊ ﺍﻻﺴﺘﻤﺭﺍﺭ . Continue
ﺍﻟﺨﻁﻭﺓ ﺍﻟﺘﺎﻟﻴﺔ ﻫﻲ ﺘﺤﺩﻴﺩ ﺍﻟﻤﻌﺎﻴﻴﺭ ﻭﺍﻻﺨﺘﺒﺎﺭﺍﺕ ﺍﻟﻤﺴﺘﺨﺩﻤﺔ ﻓﻲ ﺍﻟﻁﺭﻴﻘﺔ
ﺍﻟﺘﺩﺭﻴﺠﻴﺔ ﻭﺫﻟﻙ ﻋﻥ ﻁﺭﻴﻕ ﺨﻴﺎﺭ ﺍﻟﻁﺭﻴﻘﺔ Methodﻓﻲ ﻨﺎﻓﺫﺓ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ
ﺍﻟﺭﺌﻴﺴﻴﺔ Discriminant Analysisﺍﻟﺘﺎﺒﻌﺔ ﻟﻁﺭﻴﻘﺔ ﺍﻟﺘﺤﻠﻴل ﺍﻟﺘﺩﺭﻴﺠﻲ Stepwise
Methodﻟﺘﻔﺘﺢ ﻨﺎﻓﺫﺓ ﻁﺭﻴﻘﺔ ﺍﻟﺘﺤﻠﻴل ﺍﻟﺘﺩﺭﻴﺠﻲ Stepwise Methodﻜﻤﺎ ﻓﻲ ﺸﻜل
6-16ﺃﺩﻨﺎﻩ ،ﺴﻨﺠﺩ ﺃﻥ ﺍﻟﻘﻴﻡ ﺍﻟﺘﻠﻘﺎﺌﻴﺔ ﻓﻲ ﻫﺫﻩ ﺍﻟﻨﺎﻓﺫﺓ ﻭﻫﻲ ﺇﺤﺼﺎﺀ ﻻﻤﺩﺍ ﻟﻭﻴﻠﻜﺱ
Wilks' Lambdaﻭﻜﺫﻟﻙ ﻗﻴﻡ ﺩﺍﻟﺔ Fﻟﻼﺨﺘﺒﺎﺭ F valuesﺒﻘﻴﻡ 3.84ﻭ 2.71ﻭﺫﻟﻙ ﻹﺩﺨﺎل ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺇﻟﻰ ﺍﻟﺘﺤﻠﻴل ﻭﺤﺫﻓﻬﺎ ﻤﻨﻪ ،ﻫﺫﻩ ﺍﻟﺨﻴﺎﺭﺍﺕ ﺍﻟﺘﻠﻘﺎﺌﻴﺔ ﻭﺒﻬﺫﻩ ﺍﻟﻘﻴﻡ ﺘﻌﺘﺒﺭ ﻤﻨﺎﺴﺒﺔ ﻭﻻ ﺩﺍﻋﻲ ﻟﺘﻐﻴﻴﺭﻫﺎ ﺒل ﻴﺠﺏ ﺍﻟﺘﺄﻜﺩ ﻓﻘﻁ ﻤﻥ ﻭﺠﻭﺩﻫﺎ ،ﻓﻲ ﺍﻟﻨﻬﺎﻴﺔ ﺍﻀﻐﻁ
ﻤﻔﺘﺎﺡ ﺍﻻﺴﺘﻤﺭﺍﺭ Continueﻟﻠﻌﻭﺩﺓ ﻟﻠﻨﺎﻓﺫﺓ ﺍﻟﺴﺎﺒﻘﺔ ،ﻭﻫﻨﺎﻙ ﺒﻬﺫﻩ ﺍﻟﺨﻁﻭﺓ ﺘﻌﺘﺒﺭ ﻨﺎﻓﺫﺓ
ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ Discriminant Analysisﻗﺩ ﺍﻜﺘﻤﻠﺕ ﻭﺘﻅﻬﺭ ﻜﻤﺎ ﻓﻲ ﺸﻜل 5-16
ﺃﻋﻼﻩ ﻭﻴﺘﻡ ﺍﻟﻀﻐﻁ ﻋﻠﻰ ﻤﻔﺘﺎﺡ ﺍﻟﺘﻨﻔﻴﺫ OKﻟﺘﻨﻔﻴﺫ ﺍﻟﺘﺤﻠﻴل ﻭﺍﻟﺤﺼﻭل ﻋﻠﻰ ﺍﻟﻨﺘﺎﺌﺞ. ﺸﻜل : 6-16ﻨﺎﻓﺫﺓ ﺍﻟﻁﺭﻴﻘﺔ ﺍﻟﺘﺩﺭﻴﺠﻴﺔ ﻓﻲ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ Discriminant Analysis: Stepwise Methodﻭﺒﻬﺎ ﺠﻤﻴﻊ ﺍﻟﻤﻌﻠﻭﻤﺎﺕ ﺍﻟﻤﻁﻠﻭﺒﺔ ﻟﻬﺎ.
) (16ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ
571
ﻨﻌﺭﺽ ﻓﻲ ﺍﻟﺸﻜل 7-16ﺃﺩﻨﺎﻩ ﻗﺎﺌﻤﺔ ﻤﺤﺘﻭﻴﺎﺕ ﺠﺩﺍﻭل ﻨﺘﺎﺌﺞ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ Discriminant analysisﻜﻤﺎ ﺘﻅﻬﺭ ﻓﻲ ﺍﻟﺠﺯﺀ ﺍﻷﻴﺴﺭ ﻤﻥ ﺸﺎﺸﺔ ﻤﺤﺭﺭ ﺍﻟﻨﺘﺎﺌﺞ ،Output Viewerﻓﻴﻅﻬﺭ ﻫﻨﺎﻙ ﻋﺩﺩ ﻫﺎﺌل ﻤﻥ ﺍﻟﻨﺘﺎﺌﺞ ﻭﻟﻜﻨﻬﺎ ﻟﻴﺴﺕ ﺠﻤﻴﻌﻬﺎ ﻫﺎﻤﺔ
ﻭﻤﻁﻠﻭﺒﺔ ،ﻟﺫﻟﻙ ﻓﺈﻨﻨﺎ ﺴﻨﻘﺴﻡ ﻫﺫﻩ ﺍﻟﻨﺘﺎﺌﺞ ﺇﻟﻰ ﺃﺠﺯﺍﺀ ﻭﺴﻭﻑ ﻨﻨﺎﻗﺵ ﺠﻤﻴﻊ ﺍﻷﺠﺯﺍﺀ ﺍﻟﻬﺎﻤﺔ ﻤﻥ ﻫﺫﻩ ﺍﻟﻨﺘﺎﺌﺞ ﻓﻴﻤﺎ ﻴﻠﻲ.
ﺸﻜل : 7-16ﺍﻟﻘﺎﺌﻤﺔ ﺍﻟﻜﺎﻤﻠﺔ ﺒﺄﺴﻤﺎﺀ ﺍﻟﺠﺩﺍﻭل ﻓﻲ ﻨﺘﺎﺌﺞ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ Discriminant Analysisﻜﻤﺎ ﺘﻅﻬﺭ ﻓﻲ ﻤﺤﺭﺭ ﺍﻟﻨﺘﺎﺌﺞ .SPSS Viewer
ﻭﻴﺤﺘﻭﻱ ﺍﻟﺠﺯﺀ ﺍﻷﻭل ) ﺸﻜل (8-16ﻋﻠﻰ ﻤﻠﺨﺹ ﻟﻠﺒﻴﺎﻨﺎﺕ ﻭﻴﻅﻬﺭ ﺒﻪ
ﺒﻌﺽ ﺍﻟﻤﻌﻠﻭﻤﺎﺕ ﺍﻟﻌﺎﻤﺔ Analysis Case Processing Summaryﻤﺜل ﻋﺩﺩ ﺍﻟﻘﻴﻡ ﺍﻟﺘﻲ ﺩﺨﻠﺕ ﻓﻲ ﺍﻟﺘﺤﻠﻴل ﻭﻋﺩﺩ ﺍﻟﻘﻴﻡ ﺍﻟﻤﻔﻘﻭﺩﺓ ﻭﻨﺴﺒﺘﻬﺎ ﺇﻟﻰ ﺍﻟﻌﺩﺩ ﺍﻟﻜﻠﻲ ﻟﻠﻘﻴﻡ ،ﺒﻴﻨﻤﺎ
ﻴﻅﻬﺭ ﻓﻲ ﺍﻟﺠﺯﺀ ﺍﻟﺜﺎﻨﻲ )ﺸﻜل (9-16ﻋﺩﺩ ﺍﻟﻤﻔﺭﺩﺍﺕ ﻓﻲ ﻜل ﻓﺌﺔ ﻤﻥ ﻓﺌﺎﺕ ﺍﻟﻤﺘﻐﻴﺭ
) (16ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ
572
ﺍﻟﺘﺎﺒﻊ Group Statisticsﺒﺎﻹﻀﺎﻓﺔ ﺇﻟﻰ ﺒﻌﺽ ﺍﻟﻤﻌﻠﻭﻤﺎﺕ ﺍﻷﺨﺭﻯ ﺍﻟﻤﻜﺭﺭﺓ ﻨﻅﺭﹰﺍ ﻷﻥ ﺠﻤﻴﻊ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﺴﺘﻘﻠﺔ ﺘﺤﺘﻭﻱ ﻋﻠﻰ ﻨﻔﺱ ﺍﻟﻌﺩﺩ ﻤﻥ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﻓﻲ ﺍﻟﺘﺤﻠﻴل
ﻭﻟﺫﻟﻙ ﻓﻘﺩ ﻗﻤﻨﺎ ﺒﺤﺫﻑ ﻫﺫﻩ ﺍﻟﻤﻌﻠﻭﻤﺎﺕ ﻤﻥ ﻫﺫﺍ ﺍﻟﺠﺩﻭل .
ﺸﻜل : 8-16ﺍﻟﺠﺯﺀ ﺍﻷﻭل ﻤﻥ ﻨﺘﺎﺌﺞ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ Discriminant Analysis ﻭﻴﻅﻬﺭ ﺒﻪ ﻤﻠﺨﺹ ﻋﺎﻡ ﻋﻥ ﺍﻟﺒﻴﺎﻨﺎﺕ ﺍﻟﺘﻲ ﺩﺨﻠﺕ ﻓﻲ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ. Analysis Case Processing Summary Unweighted Cases Valid
Percent
N
91.5
108
.0
0
8.5
10
At least one missing discriminating va
.0
0
Both missing or out-of-range group co and at least one missing discriminating variable
8.5
10
Total
100.0
118
Excluded Missing or out-of-range group codes
Total
ﺸﻜل : 9-16ﺍﻟﺠﺯﺀ ﺍﻟﺜﺎﻨﻲ ﻤﻥ ﻨﺘﺎﺌﺞ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ Discriminant Analysis ﻭﻴﻅﻬﺭ ﺒﻪ ﺃﻋﺩﺍﺩ ﺍﻟﻤﻔﺭﺩﺍﺕ ﻓﻲ ﺍﻟﻔﺌﺎﺕ ﺍﻟﻤﺨﺘﻠﻔﺔ ﻟﻠﻤﺘﻐﻴﺭ ﺍﻟﺘﺎﺒﻊ ﻓﻲ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ. Group Statistics Valid N ) (li t i 30
Study Subject Architects
37
Psychologists
41
Engineers
108
Total
ﻭﺍﻟﺠﺯﺀ ﺍﻟﺭﺍﺒﻊ ﻤﻥ ﺍﻟﺘﺤﻠﻴل )ﺸﻜل (10-16ﻴﺒﻴﻥ ﺨﻼﺼﺔ ﺠﺩﺍﻭل ﺘﺤﻠﻴل
ﺍﻟﺘﺒﺎﻴﻥ ﻟﻜل ﻤﺘﻐﻴﺭ ﻋﻠﻰ ﺤﺩﻩ Univariate ANOVAsﻜﻤﺎ ﺘﻡ ﺘﺤﺩﻴﺩﻩ ﻤﻥ ﻀﻤﻥ ﺍﻻﺨﺘﻴﺎﺭﺍﺕ ﺍﻹﻀﺎﻓﻴﺔ .
) (16ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ
573
ﺸﻜل : 10-16ﺍﻟﺠﺯﺀ ﺍﻟﺭﺍﺒﻊ ﻤﻥ ﻨﺘﺎﺌﺞ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ Discriminant Analysis ﻭﻴﻅﻬﺭ ﺒﻪ ﺨﻼﺼﺔ ﺠﺩﺍﻭل ﺘﺤﻠﻴل ﺍﻟﺘﺒﺎﻴﻥ ﻟﻜل ﻤﺘﻐﻴﺭ ﻤﺴﺘﻘل .Univariate ANOVAs Tests of Equality of Group Means
Sig.
df2
df1
F
'Wilks Lambda
.000
105
2
15.991
.767
Sex of Student
.000
105
2
9.706
.844
Interest in Construction Kits
.126
105
2
2.110
.961
Interest in Modelling Kits
.003
105
2
6.086
.896
Interest in Drawing
.000
105
2
10.409
.835
Interest in Painting
.043
105
2
3.243
.942
Interest in Outdoor Pursuits Interest in Computing
.999
105
2
.001
1.000
.000
105
2
9.740
.844
Ability to Visualise Model
.001
105
2
7.384
.877
School Qualifications
ﻭﻫﺫﺍ ﺍﻟﺠﺩﻭل ﻴﺒﻴﻥ ﻤﺎ ﺇﺫﺍ ﻜﺎﻥ ﻫﻨﺎﻙ ﻓﺭﻭﻕ ﻤﻌﻨﻭﻴﺔ ﺇﺤﺼﺎﺌﻴﹰﺎ ﺒﻴﻥ ﻤﺘﻭﺴﻁﺎﺕ
ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﺴﺘﻘﻠﺔ ﻜل ﻋﻠﻰ ﺤﺩﻩ ﻓﻲ ﺍﻟﻔﺌﺎﺕ ﺍﻟﺜﻼﺜﺔ ﻟﻤﺘﻐﻴﺭ ﺍﻟﺘﺼﻨﻴﻑ ﺍﻟﺘﺎﺒﻊ
)ﺘﺨﺼﺹ ﺍﻟﻁﺎﻟﺏ( ،ﻭﻴﺘﻀﺢ ﻤﻥ ﺍﻟﺠﺩﻭل ﺃﻥ ﺍﻟﻔﺭﻭﻕ ﺒﻴﻥ ﻤﺘﻭﺴﻁﺎﺕ ﺍﻟﺘﺨﺼﺼﺎﺕ ﺍﻟﺜﻼﺜﺔ ﻟﺠﻤﻴﻊ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﺴﺘﻘﻠﺔ ﻤﻌﻨﻭﻴﺔ ﺇﺤﺼﺎﺌﻴﹰﺎ ﺒﺎﺴﺘﺜﻨﺎﺀ ﻤﺘﻐﻴﺭﺍ ﺍﻻﻫﺘﻤﺎﻡ ﻓﻲ ﺍﻟﺤﺎﺴﻭﺏ ﻭﺍﻻﻫﺘﻤﺎﻡ ﻓﻲ ﺃﺩﻭﺍﺕ ﺍﻟﺘﺨﻁﻴﻁ . ﻭﺍﻟﺠﺯﺀ ﺍﻟﺨﺎﻤﺱ ﻤﻥ ﺍﻟﻨﺘﺎﺌﺞ )ﺸﻜل (11-16ﻴﻌﻁﻲ ﻤﻠﺨﺼﹰﺎ ﻟﻠﺨﻁﻭﺍﺕ ﺍﻟﺘﻲ
ﻤﺭ ﺒﻬﺎ ﺍﻟﻨﻅﺎﻡ ﻤﻥ ﺇﺩﺨﺎل ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﺴﺘﻘﻠﺔ ﺇﻟﻰ ﺍﻟﺘﺤﻠﻴل ﻭﺍﺴﺘﺒﻌﺎﺩﻫﺎ ﻤﻨﻪ ﻓﻲ ﺍﻟﺘﺤﻠﻴل
ﺍﻟﻁﺒﻘﻲ ﺍﻟﺘﺩﺭﻴﺠﻲ ،Stepwise Discriminant Analysisﻭﻴﺒﻴﻥ ﺍﻟﺠﺩﻭل ﻓﻲ ﻫﺫﺍ ﺍﻟﺠﺯﺀ ﺘﺭﺘﻴﺏ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺤﺴﺏ ﺩﺨﻭﻟﻬﺎ ﺃﻭ ﺍﺴﺘﺒﻌﺎﺩﻫﺎ ﻤﻥ ﺍﻟﺘﺤﻠﻴل )ﻋﻠﻰ ﺍﻟﺭﻏﻡ ﻤﻥ
ﺃﻨﻪ ﻓﻲ ﻫﺫﺍ ﺍﻟﻤﺜﺎل ﻟﻡ ﻴﺴﺘﺒﻌﺩ ﺃﻱ ﻤﺘﻐﻴﺭ( ﺒﺎﻹﻀﺎﻓﺔ ﺇﻟﻰ ﻗﻴﻤﺔ ﻻﻤﺩﺓ ﻟﻭﻴﻠﻜﺱ 'Wilks
Lambda Λﻭﻗﻴﻤﺔ ﺍﻟﻤﻌﻨﻭﻴﺔ p-valueﺍﻟﻤﺼﺎﺤﺒﺔ ﻟﻜل ﺤﺎﻟﺔ ،ﻻﺤﻅ ﺃﻥ ﻗﻴﻡ Fﻓﻲ ﺍﻟﺤﻭﺍﺸﻲ ﺍﻟﺴﻔﻠﻴﺔ bﻭ cﻟﻠﺠﺩﻭل ﻗﺩ ﻅﻬﺭﺕ ﻓﻲ ﺍﻟﺠﺯﺀ ﺍﻟﺴﺎﺒﻕ )ﺸﻜل .(10-16
) (16ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ
574
ﺸﻜل : 11-16ﺍﻟﺠﺯﺀ ﺍﻟﺨﺎﻤﺱ ﻤﻥ ﻨﺘﺎﺌﺞ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ Discriminant Analysis ﻭﻴﺒﻴﻥ ﻤﻠﺨﺹ ﻟﺨﻁﻭﺍﺕ ﺇﺩﺨﺎل ﻭﺍﺴﺘﺒﻌﺎﺩ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﻤﻥ ﺍﻟﺘﺤﻠﻴل ﻓﻲ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﺍﻟﺘﺩﺭﻴﺠﻲ .Stepwise Discriminant Analysis Variables Entered/Removeda,b,c,d Wilks' Lambda Exact F Sig.
df2
df1
Stati stic
df3
df2
df1
Statistic
Entered
Step
105.0 .000
2
105.0 16.0
2
1
.767
Sex of Student
1
208.0 .000
4
105.0 13.0
2
2
.641
Interest in Painting
2
206.0 .000
6
105.0 12.4
2
3
.539
School Qualifications
3
204.0 .000
8
105.0 11.3
2
4
.481
Ability to Visualise Model
4
202.0 .000
10
105.0 10.3
2
5
.439
Interest in Outdoor Pursuits
5
200.0 .000
12
9.6
105.0
2
6
.403
Interest in Construction Kits
6
198.0 .000
14
9.0
105.0
2
7
.374
Interest in Computing
7
At each step, the variable that minimizes the overall Wilks' Lambda is entered. a. Maximum number of steps is 18. b. Minimum partial F to enter is 3.84. c. Maximum partial F to remove is 2.71. d. F level, tolerance, or VIN insufficient for further computation.
ﻭﺍﻟﺠﺯﺀ ﺍﻟﺘﺎﻟﻲ ﻤﻥ ﻗﺎﺌﻤﺔ ﺍﻟﻨﺘﺎﺌﺞ )ﺸﻜل (12-16ﻫﻭ ﺍﻟﺠﺯﺀ ﺍﻟﺴﺎﺩﺱ ﻭﻴﺒﺭﺯ
ﻻ ﺒﺎﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﺴﺘﻘﻠﺔ ﺍﻟﻤﺘﺒﻘﻴﺔ ﻓﻲ ﺍﻟﺘﺤﻠﻴل Variables in the Analysisﻭﺫﻟﻙ ﺠﺩﻭ ﹰ
ﻓﻲ ﺒﺩﺍﻴﺔ ﻜل ﺨﻁﻭﺓ ﻤﻥ ﺨﻁﻭﺍﺕ ﺍﻟﺘﺤﻠﻴل ﺍﻟﺘﺩﺭﻴﺠﻲ ،ﻭﻟﻜﻥ ﻨﻅﺭﹰﺍ ﻟﻁﻭل ﻫﺫﺍ ﺍﻟﺠﺩﻭل ﻓﻘﺩ ﺘﻡ ﺍﺨﺘﺼﺎﺭﻩ ﻓﻲ ﺸﻜل 12-16ﻟﻴﻭﻀﺢ ﻨﺘﺎﺌﺞ ﺍﻟﺨﻁﻭﺘﻴﻥ ﺍﻷﻭﻟﻰ ﻭﺍﻷﺨﻴﺭﺓ ﻓﻘﻁ،
ﻜﻤﺎ ﻴﺒﻴﻥ ﺍﻟﺠﺩﻭل ﺍﻟﺴﺎﺒﻕ ﻗﻴﻡ ﺩﻭﺍل ﺍﻻﺨﺘﺒﺎﺭ ، F to Removeﻭﻴﺘﻀﺢ ﻓﻲ ﻫﺫﺍ ﺍﻟﻤﺜﺎل
ﺃﻥ ﺠﻤﻴﻌﻬﺎ ﺃﻋﻠﻰ ﻤﻥ ﺍﻟﺤﺩ ﺍﻷﺩﻨﻰ ﻟﻘﻴﻤﺔ ﺩﺍﻟﺔ ﺍﻻﺨﺘﺒﺎﺭ 2.71) Fﻜﻤﺎ ﺘﻡ ﺘﺤﺩﻴﺩﻫﺎ( ﻟﻜﻲ ﻻ ﻴﺘﻡ ﺍﺴﺘﺒﻌﺎﺩﻫﺎ ﻤﻥ ﺍﻟﺘﺤﻠﻴل ،ﻭﺒﺎﻟﺘﺎﻟﻲ ﻓﻠﻡ ﻴﺴﺘﺒﻌﺩ ﺃﻱ ﻤﻥ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﺴﺘﻘﻠﺔ ﺍﻟﺘﻲ ﺴﺒﻕ ﻭﺃﻥ ﺩﺨﻠﺕ ﻓﻲ ﺍﻟﺘﺤﻠﻴل ﻓﻲ ﻫﺫﺍ ﺍﻟﻤﺜﺎل.
( ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ16)
575
Discriminant Analysis ﺍﻟﺠﺯﺀ ﺍﻟﺴﺎﺩﺱ ﻤﻥ ﻨﺘﺎﺌﺞ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ: 12-16 ﺸﻜل .ﻭﻴﺒﻴﻥ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﺘﺒﻘﻴﺔ ﻓﻲ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﺍﻟﺘﺩﺭﻴﺠﻲ ﻓﻲ ﺍﻟﺨﻁﻭﺘﻴﻥ ﺍﻷﻭﻟﻰ ﻭﺍﻷﺨﻴﺭﺓ Variables in the Analysis
Tolerance
F to Remove
1.000
15.991
Sex of Student
.592
7.471
.430
Interest in Painting
.734
10.917
.456
School Qualifications
.915
10.832
.456
Ability to Visualise Model
.900
7.959
.434
Interest in Outdoor Pursuits
.843
3.959
.404
Interest in Construction Kits
.801
4.332
.407
Interest in Computing
.700
3.854
.403
Step 1 Sex of Student 7
Wilks' Lambda
Discriminant Analysis ﺍﻟﺠﺯﺀ ﺍﻟﺴﺎﺒﻊ ﻤﻥ ﻨﺘﺎﺌﺞ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ: 13-16 ﺸﻜل
.ﻭﻴﺒﻴﻥ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﺤﺫﻭﻓﺔ ﻤﻥ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﺍﻟﺘﺩﺭﻴﺠﻲ ﻓﻲ ﺍﻟﺨﻁﻭﺘﻴﻥ ﺍﻷﻭﻟﻰ ﻭﺍﻷﺨﻴﺭﺓ Variables Not in the Analysis
Step 0
7
Min. Tolerance Tolerance
F to Enter
Wilks' Lambda
Sex of Student
1.000
1.000
15.991
.767
Interest in Construction Kits
1.000
1.000
9.706
.844
Interest in Modelling Kits
1.000
1.000
2.110
.961
Interest in Drawing
1.000
1.000
6.086
.896
Interest in Painting
1.000
1.000
10.409
.835
Interest in Outdoor Pursuits
1.000
1.000
3.243
.942
Interest in Computing
1.000
1.000
.001
1.000
Ability to Visualise Model
1.000
1.000
9.740
.844
School Qualifications
1.000
1.000
7.384
.877
Interest in Modelling Kits
.716
.572
.356
.371
Interest in Drawing
.628
.521
.911
.367
) (16ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ
576
ﻻ ﺒﺎﻟﻤﺘﻐﻴﺭﺍﺕ ﻜﻤﺎ ﻴﺒﻴﻥ ﺍﻟﺠﺯﺀ ﺍﻟﺴﺎﺒﻊ ﻤﻥ ﻗﺎﺌﻤﺔ ﺍﻟﻨﺘﺎﺌﺞ )ﺸﻜل (13-16ﺠﺩﻭ ﹰ ﺍﻟﻤﺴﺘﻘﻠﺔ ﺍﻟﻤﺴﺘﺒﻌﺩﺓ ﻤﻥ ﺍﻟﺘﺤﻠﻴل Variables not in the Analysisﻭﺫﻟﻙ ﻓﻲ ﺒﺩﺍﻴﺔ
ﻜل ﺨﻁﻭﺓ ﻤﻥ ﺨﻁﻭﺍﺕ ﺍﻟﺘﺤﻠﻴل ﺍﻟﺘﺩﺭﻴﺠﻲ ﻤﻥ ﺍﻟﺒﺩﺍﻴﺔ ﺤﺘﻰ ﺍﻟﺨﻁﻭﺓ ﺍﻟﻨﻬﺎﺌﻴﺔ ،ﻭﻴﻤﻜﻨﻨﺎ
ﺃﻥ ﻨﻼﺤﻅ ﺃﻥ ﻤﺘﻐﻴﺭ ﻨﻭﻉ ﺍﻟﻁﺎﻟﺏ Sex of the Studentﻴﺘﻤﺘﻊ ﺒﺄﻜﺒﺭ ﻗﻴﻤﺔ ﻟﺩﺍﻟﺔ F to
Enterﻤﻨﺫ ﺍﻟﺒﺩﺍﻴﺔ ﻭﺒﺎﻟﺘﺎﻟﻲ ﻓﻘﺩ ﻜﺎﻨﺕ ﺍﻟﻤﺘﻐﻴﺭ ﺍﻷﻭل ﺍﻟﺫﻱ ﺩﺨل ﺇﻟﻰ ﺍﻟﺘﺤﻠﻴل ﻓﻲ ﺍﻟﺨﻁﻭﺓ ﺍﻷﻭﻟﻰ ،ﻭﻴﺘﻀﺢ ﺫﻟﻙ ﻤﻥ ﺍﻟﺸﻜﻠﻴﻥ 12-16ﻭ. 13-16
ﻭﻓﻲ ﺍﻟﺨﻁﻭﺓ ﺍﻟﺜﺎﻨﻴﺔ )ﻭﺍﻟﺘﻲ ﺤﺫﻓﺕ ﻨﺘﺎﺌﺠﻬﺎ ﻤﻥ ﺍﻟﺸﻜﻠﻴﻥ ﺍﻟﺴﺎﺒﻘﻴﻥ( ﻜﺎﻥ ﺍﻟﻤﺘﻐﻴﺭ ﺍﻟﻤﺴﺘﻘل ﺍﻟﺜﺎﻨﻲ ﺍﻟﺫﻱ ﻴﺘﻤﺘﻊ ﺒﺄﻜﺒﺭ ﻗﻴﻤﺔ ﻟﺩﺍﻟﺔ F to Enterﺘﺎﻟﻴﺔ ﻟﻘﻴﻤﺔ ﺍﻟﻤﺘﻐﻴﺭ ﺍﻟﺴﺎﺒﻕ ﻭﻗﺩ ﺩﺨل ﻓﻲ ﺍﻟﺘﺤﻠﻴل ﻫﻭ ﻤﺘﻐﻴﺭ ﺍﻻﻫﺘﻤﺎﻡ ﻓﻲ ﺍﻟﺭﺴﻡ ﺍﻟﻔﻨﻲ )،(Interest in Painting
ﻭﻓﻲ ﺍﻟﻨﻬﺎﻴﺔ ﻓﻲ ﺍﻟﺨﻁﻭﺓ 7ﻗﺩ ﺘﺒﻘﻰ ﻤﺘﻐﻴﺭﻱ ﺍﻻﻫﺘﻤﺎﻡ ﻓﻲ ﺍﻟﺭﺴﻡ ﺍﻟﻬﻨﺩﺴﻲ (Interest
) in Drawingﻭﺍﻻﻫﺘﻤﺎﻡ ﻓﻲ ﺃﺩﻭﺍﺕ ﺍﻟﺘﺨﻁﻴﻁ ) (Interest in Modeling Kitsﻭﻟﻡ
ﻴﺘﻡ ﺇﺩﺨﺎﻟﻬﻤﺎ ﻟﻠﺘﺤﻠﻴل ﻋﻠﻰ ﺍﻹﻁﻼﻕ ﻨﻅﺭﹰﺍ ﻷﻥ ﻗﻴﻤﺘﻲ ﺩﺍﻟﺘﻲ ﺍﻻﺨﺘﺒﺎﺭ F to Enter
ﺍﻟﻤﺼﺎﺤﺒﺔ ﻟﻬﻤﺎ ﺃﺼﻐﺭ ﻤﻥ ﺍﻟﺤﺩ ﺍﻷﺩﻨﻰ ﺍﻟﻤﻭﻀﻭﻉ ﻤﺴﺒﻘﹰﺎ ﻭﻫﻭ . 3.84
ﻭﻴﻌﻁﻲ ﺍﻟﺠﺯﺀ ﺍﻟﺜﺎﻤﻥ ﻤﻥ ﻗﺎﺌﻤﺔ ﺍﻟﻨﺘﺎﺌﺞ )ﺸﻜل (14-16ﺍﻹﺤﺼﺎﺀﺍﺕ ﺍﻟﻤﺘﻌﻠﻘﺔ
ﺒﺩﻭﺍل ﺍﻟﺘﻤﻴﻴﺯ ،Statistics of the Discriminant Functionsﻭﻴﺒﻴﻥ ﺍﻟﻨﺴﺒﺔ ﺍﻟﻤﺌﻭﻴﺔ ل ﻤﻥ ﺩﻭﺍل ﺍﻟﺘﻤﻴﻴﺯ ،Discriminant Functionﻜﻤﺎ ﻤﻥ ﺍﻟﺘﺒﺎﻴﻥ ﺍﻟﺘﻲ ﺘﻌﺯﻯ ﺇﻟﻰ ﻜ ٍ
ﻴﺒﻴﻥ ﻋﺩﺩ ﺍﻟﺩﻭﺍل ﺍﻟﻤﻌﻨﻭﻴﺔ ﺇﺤﺼﺎﺌﻴﹰﺎ ﺇﻥ ﻭﺠﺩﺕ ،ﻭﻫﺫﺍ ﻴﺘﻀﺢ ﻤﻥ ﺍﻟﻌﻤﻭﺩ ﺍﻷﺨﻴﺭ
ﺍﻟﻤﺘﻌﻠﻕ ﺒﻤﻌﻨﻭﻴﺔ Sig.ﺇﺤﺼﺎﺀ ﻻﻤﺩﺍ ﻟﻭﻴﻠﻜﺱ Wilks' Lambdaﻓﻲ ﺍﻟﺠﺩﻭل ﺍﻟﺜﺎﻨﻲ.
ﻭﺍﻟﺠﺯﺀ ﺍﻟﺘﺎﺴﻊ ﻤﻥ ﻗﺎﺌﻤﺔ ﺍﻟﻨﺘﺎﺌﺞ )ﺸﻜل (15-16ﻴﻌﻁﻲ ﺠﺩﻭﻟﻴﻥ )ﺍﻷﻭل ﺘﻡ ﺤﺫﻓﻪ ﻭﻴﺘﻌﻠﻕ ﺒﻤﻌﺎﻤﻼﺕ ﺩﺍﻟﺔ ﺍﻟﺘﻤﻴﻴﺯ ﺍﻟﻤﻌﻴﺎﺭﻴﺔ ﺍﻟﺸﺭﻋﻴﺔ Standardized Canonical
(Discriminant Functionﺒﺎﻹﻀﺎﻓﺔ ﺇﻟﻰ ﻤﺼﻔﻭﻓﺔ ﺍﻟﺘﺭﻜﻴﺏ Structure Matrix
ﻭﻫﻲ ﻋﺒﺎﺭﺓ ﻋﻥ ﺠﺩﻭل ﺒﻘﻴﻡ ﻤﻌﺎﻤﻼﺕ ﺍﻻﺭﺘﺒﺎﻁ ﺍﻟﺘﺠﻤﻴﻌﻴﺔ ﻟﻠﻤﺠﻤﻭﻋﺎﺕ ﺒﻴﻥ ﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺘﻤﻴﻴﺯ Discriminating Variablesﻭﺩﻭﺍل ﺍﻟﺘﻤﻴﻴﺯ .Discriminant Functions
( ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ16)
577
Discriminant Analysis ﺍﻟﺠﺯﺀ ﺍﻟﺜﺎﻤﻥ ﻤﻥ ﻨﺘﺎﺌﺞ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ: 14-16 ﺸﻜل .Discriminant Functions ﻭﻴﺒﻴﻥ ﺍﻹﺤﺼﺎﺀﺍﺕ ﺍﻟﻤﺘﻌﻠﻘﺔ ﺒﻜل ﻤﻥ ﺩﻭﺍل ﺍﻟﺘﻤﻴﻴﺯ Eigenvalues
Function Eigenvalue % of Variance Cumulative %
Canonical Correlation
a
54.8
54.8
.641
a
45.2
100.0
.604
1
.698
2
.575
a. First 2 canonical discriminant functions were used in the analysis.
Wilks' Lambda Test of Function(s) Wilks' Lambda
Chi-square
df
Sig.
1 through 2
.374
100.341
14
.000
2
.635
46.334
6
.000
Discriminant Analysis ﺍﻟﺠﺯﺀ ﺍﻟﺘﺎﺴﻊ ﻤﻥ ﻨﺘﺎﺌﺞ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ: 15-16 ﺸﻜل .Structure Matrix )ﺒﻌﺩ ﺤﺫﻑ ﺍﻟﻘﺴﻡ ﺍﻷﻭل ﻤﻨﻪ( ﻭﻴﺒﻴﻥ ﻤﺼﻔﻭﻓﺔ ﺍﻟﺘﺭﻜﻴﺏ Structure Matrix Function 1
2
Ability to Visualise Model
-.508*
-.098
School Qualifications
.426*
-.156
Interest in Painting
-.419*
.363
Interest in Drawing
-.217*
.115
Interest in Modelling Kitsa
-.115*
.070
Interest in Computing
.006*
.000
Sex of Student
.194
.696*
Interest in Construction Kits
-.148
-.543*
Interest in Outdoor Pursuits
.193
.250*
a
Pooled within-groups correlations between discriminating variables and standardized canonical discriminant functions Variables ordered by absolute size of correlation within function. *. Largest absolute correlation between each variable and any discriminant function a. This variable not used in the analysis.
) (16ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ
578
ﻴﺘﻀﺢ ﻤﻥ ﺍﻟﻨﺘﺎﺌﺞ ﻓﻲ ﺍﻟﺸﻜل ﺍﻟﺴﺎﺒﻕ ﺃﻥ ﺩﺍﻟﺔ ﺍﻟﺘﻤﻴﻴﺯ ﺍﻷﻭﻟﻰ ﺘﻌﺘﻤﺩ ﻋﻠﻰ ﻗﺩﺭﺓ ﺍﻟﻁﺎﻟﺏ ﻋﻠﻰ ﺘﺨﻴل ﺍﻟﻨﻤﺎﺫﺝ Ability to Visualize Modelsﻭﺘﺤﺼﻴﻠﻪ ﺍﻟﻤﺩﺭﺴﻲ
School Qualificationsﻭﻜﺫﻟﻙ ﺍﻫﺘﻤﺎﻤﻪ ﻓﻲ ﺍﻟﺭﺴﻡ ﺍﻟﻔﻨﻲ Interest in Painting
ﻭﺍﻫﺘﻤﺎﻤﻪ ﻓﻲ ﺍﻟﺭﺴﻡ ﺍﻟﻬﻨﺩﺴﻲ ،Interest in Drawingﺒﻴﻨﻤﺎ ﺘﻌﺘﻤﺩ ﺩﺍﻟﺔ ﺍﻟﺘﻤﻴﻴﺯ ﺍﻟﺜﺎﻨﻴﺔ
ﻋﻠﻰ ﻨﻭﻉ ﺍﻟﻁﺎﻟﺏ Sexﻭﺍﻫﺘﻤﺎﻤﻪ ﻓﻲ ﺃﺩﻭﺍﺕ ﺍﻟﺒﻨﺎﺀ Interest in Construction Kit
ﻭﺍﻫﺘﻤﺎﻤﻪ ﻓﻲ ﺍﻟﻌﻤل ﺍﻟﺤﺭﻓﻲ ﺍﻟﺨﺎﺭﺠﻲ ،Interest in Outdoor Pursuitsﻭﻴﺘﻀﺢ
ﺫﻟﻙ ﻤﻥ ﺍﻟﺠﺩﻭل ﻓﻲ ﺸﻜل 15-16ﺍﻟﺴﺎﺒﻕ ﺤﻴﺙ ﺘﺸﻴﺭ ﺍﻟﻌﻼﻤﺔ * ﺇﻟﻰ ﻗﻴﻡ ﻤﻌﺎﻤﻼﺕ ﺍﻻﺭﺘﺒﺎﻁ ﺍﻟﻤﺭﺘﻔﻌﺔ ﻟﺘﻠﻙ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ.
ﻭﺍﻟﺠﺩﻭل ﻓﻲ ﺍﻟﺸﻜل 16-16ﻭﻫﻭ ﺍﻟﻌﺎﺸﺭ ﻭﺍﻟﻨﻬﺎﺌﻲ ﻓﻲ ﻗﺎﺌﻤﺔ ﺍﻟﻨﺘﺎﺌﺞ ،ﻜﻤﺎ ﺃﻨﻪ
ﻻ ﺍﺨﺘﻴﺎﺭﻴﹰﺎ ﺘﻡ ﻁﻠﺒﻪ ﻤﻥ ﻀﻤﻥ ﺨﻴﺎﺭ ﺍﻟﺘﺼﻨﻴﻑ Classifyﻓﻲ ﻨﺎﻓﺫﺓ ﺍﻟﺘﺤﻠﻴل ﺠﺩﻭ ﹰ
ﺍﻟﻁﺒﻘﻲ Discriminant Analysisﻭﻴﺘﻌﻠﻕ ﺒﺠﺩﻭل ﺘﻠﺨﻴﺼﻲ Summary Table
ﻟﻴﻭﻀﺢ ﻤﻌﺩل ﺍﻟﻨﺠﺎﺡ ﻓﻲ ﺍﻟﺘﻨﺒﺅ ﺒﺎﻟﺘﺼﻨﻴﻑ ﺍﻟﺼﺤﻴﺢ ﻟﻤﻔﺭﺩﺍﺕ ﺍﻟﻌﻴﻨﺔ ﻓﻲ ﻤﺠﻤﻭﻋﺎﺘﻬﺎ
ﺍﻋﺘﻤﺎﺩﺍ ﻋﻠﻰ ﺩﻭﺍل ﺍﻟﺘﻤﻴﻴﺯ Discriminant functionsﺍﻟﺘﻲ ﺘﻡ ﺍﺴﺘﻨﺒﺎﻁﻬﺎ ﻓﻲ ﻫﺫﺍ ﺍﻟﺘﺤﻠﻴل ،ﻭﻴﺒﻴﻥ ﺍﻟﺠﺩﻭل ﺃﻥ ﻤﻌﺩل ﺍﻟﺘﻨﺒﺅ ﺍﻟﻨﺎﺠﺢ ﻫﻭ . 72.2% ﻜﻤﺎ ﻴﺒﻴﻥ ﻫﺫﺍ ﺍﻟﺠﺩﻭل ﺃﻨﻪ ﻴﻤﻜﻥ ﺘﺼﻨﻴﻑ ﺍﻟﻤﻬﻨﺩﺴﻴﻥ ﺍﻟﻤﺩﻨﻴﻴﻥ Engineers
ﺒﺄﻋﻠﻰ ﺩﺭﺠﺔ ﺩﻗﺔ ﺤﻴﺙ ﻫﻨﺎﻙ ﻨﺴﺒﺔ 75.6%ﻤﻤﻥ ﺘﻡ ﺘﺼﻨﻴﻔﻬﻡ ﺒﻨﺠﺎﺡ ،ﻴﻠﻴﻬﺎ ﻓﺌﺔ
ﺍﻟﻤﻬﻨﺩﺴﻴﻥ ﺍﻟﻤﻌﻤﺎﺭﻴﻴﻥ Architectsﺤﻴﺙ ﻜﺎﻨﺕ ﻨﺴﺒﺔ ﺍﻟﻨﺠﺎﺡ ﻓﻲ ﺍﻟﺘﻨﺒﺅ ﺒﻴﻨﻬﻡ ﻤﺴﺎﻭﻴﺔ
، 67.6%ﻻﺤﻅ ﺃﻥ ﺍﻟﻤﻬﻨﺩﺴﻴﻥ ﺍﻟﻤﻌﻤﺎﺭﻴﻴﻥ ﺍﻟﺫﻴﻥ ﻓﺸل ﺍﻟﺘﺤﻠﻴل ﺒﺎﻟﺘﻨﺒﺅ ﺒﻬﻡ ﻏﺎﻟﺒﹰﺎ ﻤﺎ
ﻴﺘﻡ ﺘﺼﻨﻴﻔﻬﻡ ﻋﻠﻰ ﺃﻨﻬﻡ ﻤﻬﻨﺩﺴﻴﻥ ﻤﺩﻨﻴﻴﻥ ﻭﻟﻴﺴﻭﺍ ﺃﺨﺼﺎﺌﻴﻲ ﻋﻠﻡ ﻨﻔﺱ ،ﻜﻤﺎ ﺃﻥ ﺃﺨﺼﺎﺌﻴﻲ ﻋﻠﻡ ﺍﻟﻨﻔﺱ ﺍﻟﺫﻴﻥ ﻓﺸل ﺍﻟﺘﺤﻠﻴل ﺒﺎﻟﺘﻨﺒﺅ ﺒﻬﻡ ﻏﺎﻟﺒﹰﺎ ﻤﺎ ﻴﺼﻨﻔﻭﺍ ﻋﻠﻰ ﺃﻨﻬﻡ ﻤﻬﻨﺩﺴﻴﻥ ﻤﺩﻨﻴﻴﻥ ﻭﻟﻴﺱ ﻤﻬﻨﺩﺴﻴﻥ ﻤﻌﻤﺎﺭﻴﻴﻥ.
) (16ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ
579
ﺸﻜل : 16-16ﺍﻟﺠﺯﺀ ﺍﻟﻌﺎﺸﺭ ﻭﺍﻷﺨﻴﺭ ﻤﻥ ﻨﺘﺎﺌﺞ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ Discriminant Analysisﻭﻴﺒﻴﻥ ﺍﻟﻨﺘﺎﺌﺞ ﺍﻟﻨﻬﺎﺌﻴﺔ ﻟﺘﺼﻨﻴﻑ ﺒﻴﺎﻨﺎﺕ ﺍﻟﻌﻴﻨﺔ .Classification Results Classification Resultsa Predicted Group Membership Architects
Study Subject Original Architects Count Psychologists
Total
Engineers
Psychologists
30
6
2
22
37
8
25
4
41
31
5
5
Engineers
100.0
20.0
6.7
73.3
Architects
100.0
21.6
67.6
10.8
Psychologists
100.0
75.6
12.2
12.2
Engineers
%
a. 72.2% of original grouped cases correctly classified.
ﻭﺨﺘﺎﻤﺎﹰ ،ﻨﺠﺩ ﺃﻥ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻗﺩ ﺒﻴﻥ ﺃﻨﻪ ﻴﻤﻜﻥ ﺍﺸﺘﻘﺎﻕ ﺩﺍﻟﺘﻲ ﺍﻟﺘﻤﻴﻴﺯ
Discriminant functionsﺒﺎﺴﺘﺨﺩﺍﻡ ﺠﻤﻴﻊ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺒﺎﺴﺘﺜﻨﺎﺀ ﻤﺘﻐﻴﺭﻱ ﺍﻻﻫﺘﻤﺎﻡ ﻓﻲ
ﺍﻟﺭﺴﻡ ﺍﻟﻬﻨﺩﺴﻲ Interest in Drawingﻭﺍﻻﻫﺘﻤﺎﻡ ﻓﻲ ﺃﺩﻭﺍﺕ ﺍﻟﺘﺨﻁﻴﻁ Interest in
Modeling Kitsﺍﻟﺫﻴﻥ ﻟﻡ ﻴﺘﻡ ﺇﺩﺨﺎﻟﻬﻤﺎ ﻟﻠﺘﺤﻠﻴل ﻋﻠﻰ ﺍﻹﻁﻼﻕ ،ﻭﻫﺎﺘﻴﻥ ﺍﻟﺩﺍﻟﺘﻴﻥ ﻴﻤﻜﻨﻬﻤﺎ ﺍﻟﺘﻨﺒﺅ ﺒﹻ 72%ﻤﻥ ﺍﻟﻤﻔﺭﺩﺍﺕ ﺒﺩﻗﺔ ،ﻋﻼﻭﺓ ﻋﻠﻰ ﺫﻟﻙ ﻴﺘﻀﺢ ﻤﻥ ﻗﺎﺌﻤﺔ
ﺍﻟﻨﺘﺎﺌﺞ ﻓﻲ ﺸﻜل 15-16ﺃﻥ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﻨﻭﻉ ﺍﻟﻁﺎﻟﺏ Sexﻭﻗﺩﺭﺘﻪ ﻋﻠﻰ ﺘﺨﻴل
ﺍﻟﻨﻤﺎﺫﺝ Ability to Visualize Modelsﻭﺍﻫﺘﻤﺎﻤﻪ ﻓﻲ ﺃﺩﻭﺍﺕ ﺍﻟﺒﻨﺎﺀ Interest in
Construction Kitﻭﺘﺤﺼﻴﻠﻪ ﺍﻟﻤﺩﺭﺴﻲ School Qualificationsﻭﻜﺫﻟﻙ ﺍﻫﺘﻤﺎﻤﻪ
ﻓﻲ ﺍﻟﺭﺴﻡ ﺍﻟﻔﻨﻲ Interest in Paintingﻫﻡ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﺴﺎﻫﻤﺔ ﻓﻲ ﺘﻜﻭﻴﻥ ﺩﻭﺍل ﺍﻟﺘﻤﻴﻴﺯ ،Discriminant functionsﻭﻟﻜﻥ ﻤﺎﺫﺍ ﻋﻥ ﺍﻟﺘﻨﺒﺅ ﺒﺘﺨﺼﺹ ﻁﻠﺒﺔ ﻓﻲ
ﺍﻟﻤﺴﺘﻘﺒل ﻭﺍﻟﻤﻌﺭﻭﻑ ﻋﻨﻬﻡ ﻓﻘﻁ ﺒﻴﺎﻨﺎﺕ ﺘﺘﻌﻠﻕ ﺒﺘﻠﻙ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﺴﺘﻘﻠﺔ؟ ﻫل ﻴﻤﻜﻥ ﺒﺎﺴﺘﺨﺩﺍﻡ ﻫﺫﺍ ﺍﻷﺴﻠﻭﺏ ﻭﺒﺎﺴﺘﺨﺩﺍﻡ ﻨﻅﺎﻡ SPSSﺍﻟﺘﻨﺒﺅ ﺒﺎﻟﺘﺨﺼﺹ ﺍﻟﺫﻱ ﻴﺠﺏ ﺃﻥ ﻴﺩﺭﺴﻭﻥ ﺒﻪ؟ ﺍﻹﺠﺎﺒﺔ ﻫﻲ ﻨﻌﻡ.
) (16ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ ﻭﺍﻟﺘﻤﻴﻴﺯ ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ
580
ﻭﻟﻠﻭﺼﻭل ﺇﻟﻰ ﺫﻟﻙ ﺘﺘﺒﻊ ﺍﻟﺨﻁﻭﺍﺕ ﺍﻟﺘﺎﻟﻴﺔ : • ﺃﺩﺨل ﺒﻴﺎﻨﺎﺕ ﺍﻟﻁﻼﺏ ﺍﻟﺠﺩﺩ ﻓﻲ ﻨﻬﺎﻴﺔ ﺍﻟﺒﻴﺎﻨﺎﺕ ﺍﻟﺴﺎﺒﻘﺔ ﻓﻲ ﺸﺎﺸﺔ ﻤﺤﺭﺭ ﺍﻟﺒﻴﺎﻨﺎﺕ
، Data Editorﻭﺍﺘﺭﻙ ﺍﻟﻤﺘﻐﻴﺭ ﺍﻟﺘﺎﺒﻊ ﺍﻟﺫﻱ ﻴﺸﻴﺭ ﻟﺘﺨﺼﺹ ﺍﻟﻁﺎﻟﺏ ﻓﺎﺭﻏﹰﺎ ﺃﻭ ﺃﺩﺨل
ﻗﻴﻤﹰﺎ ﺨﺎﺭﺝ ﻤﺩﻯ ﻫﺫﺍ ﺍﻟﻤﺘﻐﻴﺭ ﺒﺤﻴﺙ ﻻ ﻴﻘﻭﻡ ﺍﻟﺒﺭﻨﺎﻤﺞ ﺒﺎﺴﺘﺨﺩﺍﻡ ﺘﻠﻙ ﺍﻟﻘﻴﻡ ﻓﻲ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ .
• ﺃﻜﻤل ﻨﺎﻓﺫﺓ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻁﺒﻘﻲ Discriminant Analysisﻜﻤﺎ ﺴﺒﻕ ﺘﻭﻀﻴﺤﻪ ﻭﻟﻜﻥ
ﻭﺒﺎﻹﻀﺎﻓﺔ ﺇﻟﻰ ﺫﻟﻙ ﺍﻀﻐﻁ ﻋﻠﻰ ﻤﺭﺒﻊ ﺤﻭﺍﺭ ﺍﻟﺤﻔﻅ Saveﺜﻡ ﺍﻀﻐﻁ ﻋﻠﻰ ﻤﻔﺘﺎﺡ
ﺍﻟﺘﻨﺒﺅ ﺒﺎﻟﻔﺌﺎﺕ ، Predicted group membershipﺍﻀﻐﻁ ﻤﺭﺒﻊ ﺍﻻﺴﺘﻤﺭﺍﺭ
Continueﺜﻡ ﻤﺭﺒﻊ ﺍﻟﺘﻨﻔﻴﺫ OKﻟﺘﻨﻔﻴﺫ ﺍﻟﺘﺤﻠﻴل. •
ﻓﻲ ﺍﻟﻨﻬﺎﻴﺔ ﺴﻭﻑ ﺘﻅﻬﺭ ﺍﻟﻔﺌﺎﺕ ﺍﻟﻤﺘﻨﺒﺄ ﺒﻬﺎ Predicted group membership
ﻓﻲ ﻋﻤﻭﺩ ﻟﻤﺘﻐﻴﺭ ﺠﺩﻴﺩ ﺒﺎﺴﻡ Dis_1ﻓﻲ ﺸﺎﺸﺔ ﻤﺤﺭﺭ ﺍﻟﺒﻴﺎﻨﺎﺕ Data editor
ﺒﺎﻹﻀﺎﻓﺔ ﺇﻟﻰ ﺍﻟﻔﺌﺎﺕ ﺍﻟﻤﺘﻨﺒﺄ ﺒﻬﺎ Predictionsﻟﺠﻤﻴﻊ ﺍﻟﻤﻔﺭﺩﺍﺕ ﺍﻷﺨﺭﻯ .