Spsschap4

‫]‪Äe]†Ö]<Ø’ËÖ‬‬ ‫‪l^Þ^éfÖ]<Ì‘æ<l^é×ÛÂ‬‬ ‫‪Exploratory Data Analysis‬‬

‫‪ .1 .4‬ﻣﻘﺪﻣﺔ‪:‬‬ ‫‪ .2 .4‬اﺳﺘﻜﺸﺎف اﻟﺒﻴﺎﻧﺎت‬ ‫‪ .3 .4‬إﻳﺠﺎد اﻟﻨﻮاﻓﺬ‬ ‫‪ .4 .4‬وﺻﻒ وﺗﺒﻮﻳﺐ اﻟﺒﻴﺎﻧﺎت‬ ‫‪ .1 .4 .4‬وﺻﻒ اﻟﺒﻴﺎﻧﺎت اﻟﻨﻮﻋﻴﺔ‬ ‫‪ .2 .4 .4‬وﺻﻒ اﻟﺒﻴﺎﻧﺎت اﻟﻜﻤﻴﺔ‬

‫‪ .5 .4‬ﺍﻟﻤﻠﺨﺼﺎﺕ ﻭﺍﻟﺘﻘﺎﺭﻴﺭ‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

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‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

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‫]‪Äe]†Ö]<Ø’ËÖ‬‬ ‫‪l^Þ^éfÖ]<Ì‘æ<l^é×ÛÂ‬‬ ‫‪Exploratory Data Analysis‬‬ ‫‪ .1 .4‬ﻣﻘﺪﻣﺔ‪:‬‬ ‫ﻟﻘﺩ ﺼﻤﻡ ﻨﻅﺎﻡ ‪ SPSS‬ﺒﻬﺩﻑ ﺘﻨﻔﻴﺫ ﻤﺠﻤﻭﻋﺔ ﻜﺒﻴﺭﺓ ﻤﻥ ﺍﻟﻤﻬﺎﻡ ﺍﻹﺤﺼﺎﺌﻴﺔ‬

‫ﻭﺍﻟﺘﻲ ﻤﻥ ﺒﻴﻨﻬﺎ ﺍﺨﺘﺒﺎﺭﺍﺕ ﺇﺤﺼﺎﺌﻴﺔ ﻤﺘﻘﺩﻤﺔ ﻭﺫﻟﻙ ﺒﺴﻬﻭﻟﺔ ﻭﻴﺴﺭ‪ ،‬ﻭﻗﺒل ﺃﻥ ﻴﺒﺩﺃ‬

‫ﺍﻟﻤﺴﺘﺨﺩﻡ ﻓﻲ ﻤﺤﺎﻭﻟﺔ ﺘﻨﻔﻴﺫ ﻫﺫﻩ ﺍﻟﻤﻬﺎﻡ ﻴﺠﺏ ﻋﻠﻴﻪ ﺃﻥ ﻴﺠﺭﻱ ﺒﻌﺽ ﺍﻟﺨﻁﻭﺍﺕ‬

‫ﺍﻟﺘﺤﻀﻴﺭﻴﺔ‪ ،‬ﻓﻔﻲ ﺍﻟﺒﺩﺍﻴﺔ‪ ،‬ﺇﻨﻪ ﻟﻤﻥ ﺍﻟﻀﺭﻭﺭﻱ ﺍﻻﻨﺘﺒﺎﻩ ﺩﺍﺌﻤﹰﺎ ﺇﻟﻰ ﺼﺤﺔ ﺇﺩﺨﺎل‬

‫ﺍﻟﺒﻴﺎﻨﺎﺕ‪ ،‬ﻟﺫﻟﻙ ﺘﻌﺘﺒﺭ ﺃﻭل ﻤﻬﻤﺔ ﻓﻲ ﺍﻟﺘﺤﻠﻴل ﺍﻹﺤﺼﺎﺌﻲ ﻫﻲ ﺍﻟﺘﺤﻘﻕ ﻤﻥ ﺍﻟﺩﻗﺔ ﻓﻲ‬

‫ﺇﺩﺨﺎل ﺍﻟﺒﻴﺎﻨﺎﺕ ﻭﺍﻟﺒﺤﺙ ﻋﻥ ﺃﺨﻁﺎﺀ ﻭﻗﻌﺕ ﺃﺜﻨﺎﺀ ﻤﺭﺤﻠﺔ ﺇﺩﺨﺎل ﺍﻟﺒﻴﺎﻨﺎﺕ‪ ،‬ﻭﻴﻤﻜﻥ ﺃﻥ‬

‫ﻴﺘﻡ ﺫﻟﻙ ﺒﻤﺠﻤﻭﻋﺔ ﻤﻥ ﺍﻹﺠﺭﺍﺀﺍﺕ ﺘﺘﺭﺍﻭﺡ ﺒﻴﻥ ﺍﻟﻨﻅﺭﺓ ﺍﻟﺴﺭﻴﻌﺔ ﻋﻠﻰ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ‬ ‫ﻭﺘﻔﺤﺹ ﺍﻟﻘﻴﻡ ﺍﻟﻤﺘﻁﺭﻓﺔ ﺇﻟﻰ ﺘﺼﻤﻴﻡ ﺍﻟﺠﺩﺍﻭل ﻭﺍﻟﺒﺤﺙ ﻋﻥ ﻭﺠﻭﺩ ﻗﻴﻡ ﻏﻴﺭ ﻤﻨﻁﻘﻴﺔ ﻓﻲ‬

‫ﻫﺫﻩ ﺍﻟﺠﺩﺍﻭل‪ ،‬ﻭﻟﻴﺱ ﻫﻨﺎﻙ ﻭﺴﻴﻠﺔ ﺃﻓﻀل ﻤﻥ ﺘﻔﺤﺹ ﻤﺠﻤﻭﻋﺔ ﻜﺒﻴﺭﺓ ﻤﻥ ﺍﻟﺠﺩﺍﻭل‬

‫ﺍﻟﺘﻜﺭﺍﺭﻴﺔ ﺍﻟﻤﺯﺩﻭﺠﺔ ﻭﺍﻟﺘﻲ ﺘﺭﺒﻁ ﺒﻴﻥ ﻤﺘﻐﻴﺭﻴﻥ ﺃﻭ ﺍﻜﺜﺭ ‪ ، Cross Tabulation‬ﻟﺫﻟﻙ‬

‫ﻓﺈﻨﻪ ﺒﻌﺩ ﺍﻻﻨﺘﻬﺎﺀ ﻤﻥ ﻋﻤﻠﻴﺔ ﺇﺩﺨﺎل ﺍﻟﺒﻴﺎﻨﺎﺕ ﻭﺘﺨﺯﻴﻨﻬﺎ ﻓﻲ ﻤﻠﻑ ﻭﻨﻅﺭﺓ ﺴﺭﻴﻌﺔ ﻋﻠﻴﻬﺎ‬

‫ﻟﻠﺘﺤﻘﻕ ﻤﻥ ﻭﺠﻭﺩ ﺃﺨﻁﺎﺀ ﻭﻗﺒل ﺍﻟﺒﺩﺀ ﻓﻲ ﻤﺭﺤﻠﺔ ﺍﻟﺘﺤﻠﻴل ﺍﻹﺤﺼﺎﺌﻲ ﺍﻟﻤﺘﻌﻤﻕ ﻓﻲ‬

‫ﺍﻟﺒﻴﺎﻨﺎﺕ ﻻﺒﺩ ﻤﻥ ﺍﻟﻤﺭﻭﺭ ﺒﻤﺭﺤﻠﺔ ﺃﺴﺎﺴﻴﺔ ﻭﻫﻲ ﻤﺭﺤﻠﺔ ﺍﺴﺘﻜﺸﺎﻑ ﻭﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‪.‬‬

‫ﻭﻫﺫﺍ ﺍﻟﻔﺼل ﻤﻥ ﻫﺫﺍ ﺍﻟﻜﺘﺎﺏ ﻴﻬﺘﻡ ﻓﻘﻁ ﺒﻤﺭﺤﻠﺔ ﺍﺴﺘﻜﺸﺎﻑ ﻭﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‪،‬‬

‫ﻭﻋﺎﺩﺓ ﻴﺭﺍﻓﻕ ﺘﻠﻙ ﺍﻟﻤﺭﺤﻠﺔ ﺒل ﻭﻴﺴﺘﻤﺭ ﻤﻌﻬﺎ ﺨﻼل ﻤﺭﺤﻠﺔ ﺍﻟﺘﺤﻠﻴل ﻭﻋﺭﺽ ﺍﻟﻨﺘﺎﺌﺞ‬

‫ﻭﺭﻓﻊ ﺍﻟﺘﻭﺼﻴﺎﺕ ﺍﺴﺘﺨﺩﺍﻡ ﺍﻟﺭﺴﻭﻡ ﺍﻟﺒﻴﺎﻨﻴﺔ )‪ ،(Graphical Data Analysis‬ﻭﻨﻅﺭﹰﺍ‬

‫ﻷﻫﻤﻴﺔ ﺘﻠﻙ ﺍﻟﻭﺴﻴﻠﺔ ﻭﺍﺴﺘﺨﺩﺍﻤﻬﺎ ﻓﻲ ﺠﻤﻴﻊ ﺍﻟﻤﺭﺍﺤل ﻓﻘﺩ ﺨﺼﺼﻨﻨﺎ ﻟﻬﺎ ﺍﻟﻔﺼل ﺍﻟﻼﺤﻕ‬

‫ﻟﻌﺭﻀﻬﺎ ﺒﺎﻟﺘﻔﺼﻴل ﺭﻏﻡ ﺃﻨﻨﺎ ﺴﻨﺘﻁﺭﻕ ﺇﻟﻴﻬﺎ ﻓﻲ ﻫﺫﺍ ﺍﻟﻔﺼل‪.‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

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‫‪ .2 .4‬اﺳﺘﻜﺸﺎف اﻟﺒﻴﺎﻧﺎت‪Exploratory Data Analysis :‬‬ ‫ﻟﻘﺩ ﺃﺘﺎﺡ ﻭﺠﻭﺩ ﻨﻅﺎﻡ ‪ SPSS‬ﺒﺈﻤﻜﺎﻨﺎﺘﻪ ﺍﻟﺤﺴﺎﺒﻴﺔ ﺍﻟﻘﻭﻴﺔ ﺍﻟﻘﺩﺭﺓ ﻋﻠﻰ ﺇﺠﺭﺍﺀ‬

‫ﻭﺘﻨﻔﻴﺫ ﺍﻷﺴﺎﻟﻴﺏ ﺍﻹﺤﺼﺎﺌﻴﺔ ﺍﻟﻤﺘﻘﺩﻤﺔ ﻋﻠﻰ ﺃﻱ ﻤﺠﻤﻭﻋﺔ ﻤﻥ ﺍﻟﺒﻴﺎﻨﺎﺕ ﻤﻬﻤﺎ ﺒﻠﻎ ﺤﺠﻤﻬﺎ‬ ‫ﻤﻥ ﺍﻟﻜﺒﺭ‪ ،‬ﻭﻴﻤﻜﻥ ﺍﻟﺒﺩﺀ ﻓﻲ ﺍﻟﺘﺤﻠﻴل ﺍﻹﺤﺼﺎﺌﻲ ﺍﻟﻤﺘﻘﺩﻡ ﻭﺍﺴﺘﺨﺩﺍﻡ ﺍﺨﺘﺒﺎﺭﺍﺕ‬ ‫ﺍﻟﻔﺭﻀﻴﺎﺕ ﺍﻟﻤﺘﺎﺤﺔ ﻓﻭﺭ ﺘﺨﺯﻴﻥ ﻭﺤﻔﻅ ﺍﻟﺒﻴﺎﻨﺎﺕ ﻓﻲ ﻤﻠﻑ‪ ،‬ﺇﻻ ﺃﻥ ﺍﻟﺘﺴﺭﻉ ﻓﻲ ﺍﻟﺒﺩﺀ‬

‫ﻓﻲ ﺍﻟﺘﺤﻠﻴل ﻤﺒﺎﺸﺭﺓ ﻴﻜﻤﻥ ﻭﺭﺍﺀﻩ ﻤﺨﺎﻁﺭ ﻜﺜﻴﺭﺓ‪ ،‬ﻓﻬﻨﺎﻙ ﺴﺒﺒﻴﻥ ﺠﻭﻫﺭﻴﻴﻥ ﻴﻔﺭﻀﺎﻥ‬

‫ﻋﻠﻰ ﺍﻟﻤﺴﺘﺨﺩﻡ ﻟﻬﺫﺍ ﺍﻟﻨﻅﺎﻡ ﺍﻟﺤﺫﺭ ﻤﻥ ﺍﻟﺘﺴﺭﻉ ﻓﻲ ﺍﻟﺒﺩﺀ ﻓﻲ ﺍﻟﺘﺤﻠﻴل ﻤﺒﺎﺸﺭﺓ‪ ،‬ﺍﻟﺴﺒﺏ‬ ‫ﺍﻷﻭل ﻫﻭ ﺃﻥ ﻤﻌﻅﻡ ﺍﻻﺨﺘﺒﺎﺭﺍﺕ ﻭﺍﻟﻁﺭﻕ ﺍﻹﺤﺼﺎﺌﻴﺔ ﺘﺘﻁﻠﺏ ﺘﻭﺍﻓﺭ ﺸﺭﻭﻁ ﻭﺘﺤﻘﻕ‬

‫ﻓﺭﻀﻴﺎﺕ ﻤﻌﻴﻨﺔ ﻓﻲ ﺍﻟﺒﻴﺎﻨﺎﺕ ﻟﻜﻲ ﺘﻜﻭﻥ ﻨﺘﺎﺌﺞ ﺍﻟﺘﺤﻠﻴل ﺴﻠﻴﻤﺔ‪ ،‬ﻭﻋﺩﻡ ﺘﻭﻓﺭ ﻫﺫﻩ‬ ‫ﺍﻟﺸﺭﻭﻁ ﻴﻌﻨﻲ ﺃﻥ ﺍﻟﻨﺘﺎﺌﺞ ﺍﻟﺘﻲ ﺤﺼﻠﺕ ﻋﻠﻴﻬﺎ ﻤﻀﻠﻠﺔ‪ ،‬ﻟﺫﺍ ﻴﺠﺏ ﺍﻟﺘﺤﻘﻕ ﻤﻥ ﺼﺤﺔ‬

‫ﻫﺫﻩ ﺍﻟﺸﺭﻭﻁ ﻗﺒل ﺍﻟﺒﺩﺀ ﻓﻲ ﺍﻟﺘﺤﻠﻴل‪ ،‬ﻭﺍﻟﺴﺒﺏ ﺍﻟﺜﺎﻨﻲ ﻫﻭ ﺃﻥ ﺍﻟﻤﺴﺘﺨﺩﻡ ﺍﻟﺫﻱ ﻴﺒﺩﺃ‬ ‫ﻤﺒﺎﺸﺭﺓ ﻓﻲ ﺍﻟﺘﺤﻠﻴل ﻭﺍﺴﺘﺨﺩﺍﻡ ﺍﺨﺘﺒﺎﺭ ﺍﻟﻔﺭﻀﻴﺎﺕ ﺍﻹﺤﺼﺎﺌﻴﺔ ﻴﻔﻘﺩ ﺍﻟﻔﺭﺼﺔ ﻋﻠﻰ‬

‫ﺍﻟﺘﻌﺭﻑ ﻋﻠﻰ ﺍﻟﺠﻭﺍﻨﺏ ﺍﻟﻤﻀﻴﺌﺔ ﻓﻲ ﺍﻟﺒﻴﺎﻨﺎﺕ ﻭﺍﻟﺘﻲ ﺘﻤﻜﻨﻪ ﻤﻥ ﺍﺨﺘﻴﺎﺭ ﺍﻷﺴﺎﻟﻴﺏ‬

‫ﺍﻹﺤﺼﺎﺌﻴﺔ ﺍﻟﻤﻨﺎﺴﺒﺔ ﻟﻬﺫﻩ ﺍﻟﺒﻴﺎﻨﺎﺕ ﺒﺩﺭﺠﺔ ﻋﺎﻟﻴﺔ ﻤﻥ ﺍﻟﺩﻗﺔ‪ ،‬ﻭﻴﺤﺘﻭﻱ ﻨﻅﺎﻡ ‪ SPSS‬ﻋﻠﻰ‬ ‫ﻤﺠﻤﻭﻋﺔ ﻤﻥ ﺍﻟﻁﺭﻕ ﺍﻹﺤﺼﺎﺌﻴﺔ ﺍﻟﻤﺼﻤﻤﺔ ﻤﻥ ﺃﺠل ﺘﺴﻬﻴل ﻋﻤﻠﻴﺔ ﺍﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫ﻗﺒل ﺍﻟﺒﺩﺀ ﻓﻲ ﺍﻟﺘﺤﻠﻴل‪ ،‬ﻭﺘﻌﺭﻑ ﻫﺫﻩ ﺍﻟﻤﺠﻤﻭﻋﺔ ﺒﺎﺴﻡ ﻤﺠﻤﻭﻋﺔ ﺒﺭﺍﻤﺞ ﺍﺴﺘﻜﺸﺎﻑ‬ ‫ﺍﻟﺒﻴﺎﻨﺎﺕ )‪ (EDA‬ﺍﺨﺘﺼﺎﺭﺍ ﻟﻼﺴﻡ ‪.Exploratory Data Analysis‬‬

‫ﻓﻴﻤﻜﻥ ﻤﻥ ﻫﺫﻩ ﺍﻟﻁﺭﻕ ﺃﻥ ﻴﻜﺘﺸﻑ ﺍﻟﻤﺴﺘﺨﺩﻡ ﺃﻥ ﺍﻟﺒﻴﺎﻨﺎﺕ ﺍﻟﺘﻲ ﺴﻴﻘﻭﻡ ﺒﺘﺤﻠﻴﻠﻬﺎ‬

‫ﺘﺤﺘﻭﻱ ﻋﻠﻰ ﻨﻤﻁ ﻤﻌﻴﻥ‪ ،‬ﻭﻫﺫﺍ ﻴﺘﺭﺘﺏ ﻋﻠﻴﻪ ﺭﻏﺒﺔ ﻓﻲ ﺍﺨﺘﺒﺎﺭ ﻤﺎ ﺇﺫﺍ ﻜﺎﻥ ﻫﺫﺍ ﺍﻟﻨﻤﻁ‬

‫ﻫﻭ ﻤﺤﺽ ﺍﻟﺼﺩﻓﺔ ﺃﻭ ﺃﻨﻪ ﻨﻤﻁ ﺤﻘﻴﻘﻲ ﻓﻲ ﺍﻟﻅﺎﻫﺭﺓ ﻗﻴﺩ ﺍﻟﺩﺭﺍﺴﺔ‪ ،‬ﻭﻫﺫﺍ ﺒﺩﻭﺭﻩ‬ ‫ﺴﻴﺘﺭﺘﺏ ﻋﻠﻴﻪ ﺍﺴﺘﺨﺩﺍﻡ ﺒﻌﺽ ﺍﻻﺨﺘﺒﺎﺭﺍﺕ ﺍﻹﺤﺼﺎﺌﻴﺔ ﺍﻟﺘﻲ ﻗﺩ ﺘﻜﻭﻥ ﻤﻨﺎﺴﺒﺔ ﺃﻜﺜﺭ ﻤﻥ‬

‫ﺘﻠﻙ ﺍﻟﻤﺨﻁﻁ ﻻﺴﺘﺨﺩﺍﻤﻬﺎ‪ ،‬ﻓﻴﻜﻭﻥ ﺍﻟﻬﺩﻑ ﻤﻥ ﺍﺨﺘﺒﺎﺭ ﺍﻟﻔﺭﻀﻴﺎﺕ ﻓﻲ ﻫﺫﻩ ﺍﻟﺤﺎﻟﺔ ﻫﻭ‬ ‫ﺘﺄﻜﻴﺩ ﺃﻭ ﺘﻔﻨﻴﺩ ﺨﺎﺼﻴﺔ ﺃﻭ ﻤﻼﺤﻅﺔ ﻤﻌﻴﻨﺔ ﻓﻲ ﺍﻟﺒﻴﺎﻨﺎﺕ ﻭﺒﺩﺭﺠﺔ ﺩﻗﺔ ﻋﺎﻟﻴﺔ ﻭﻤﺤﺩﺩﺓ ‪.‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

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‫ﺇﻥ ﺃﻱ ﻤﺴﺘﺨﺩﻡ ﻟﻨﻅﺎﻡ ‪ SPSS‬ﻴﻜﻭﻥ ﻟﺩﻴﻪ ﻓﻲ ﺍﻟﻌﺎﺩﺓ ﺜﻼﺙ ﺃﻨﻭﺍﻉ ﻤﻥ ﺍﻟﻅﻭﺍﻫﺭ‬ ‫ﺃﻭ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ‪:‬‬ ‫‪ .1‬ﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻔﺘﺭﺓ ﻭﻫﻲ ﻤﺘﻐﻴﺭﺍﺕ ﻜﻤﻴﺔ ﺒﺤﻴﺙ ﺘﻌﻁﻲ ﻗﻴﻡ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﻤﻌﻨﻰ ﻟﻠﻔﺭﻭﻕ‬ ‫ﺒﻴﻥ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ‪ ،‬ﻭﺘﻌﺭﻑ ﻫﺫﻩ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ‪ ، Interval Data‬ﻭﻤﻥ ﺍﻷﻤﺜﻠﺔ‬

‫ﺍﻟﻭﺍﻀﺤﺔ ﻋﻠﻰ ﺫﻟﻙ ﺩﺭﺠﺔ ﺍﻟﻁﺎﻟﺏ ﻓﻲ ﻤﺎﺩﺓ ﻤﻌﻴﻨﺔ‪ ،‬ﻓﻤﺜل ﻫﺫﺍ ﺍﻟﻤﺘﻐﻴﺭ ﻴﺴﻤﺢ‬ ‫ﺒﺈﺠﺭﺍﺀ ﺍﻟﻌﻤﻠﻴﺎﺕ ﺍﻟﺤﺴﺎﺒﻴﺔ ﻭﺤﺴﺎﺏ ﺒﻌﺽ ﺍﻟﻤﻘﺎﻴﻴﺱ ﻤﺜل ﺍﻟﻨﺴﺏ ﻭﺍﻟﻤﺘﻭﺴﻁﺎﺕ‪.‬‬

‫‪ .2‬ﻤﺘﻐﻴﺭﺍﺕ ﺘﺭﺘﻴﺒﻴﺔ ﻭﻫﻲ ﺃﻴﻀﹰﺎ ﻤﺘﻐﻴﺭﺍﺕ ﻜﻤﻴﺔ ﻭﻟﻜﻨﻬﺎ ﻋﺒﺎﺭﺓ ﻋﻥ ﺩﻟﻴل ﻟﻔﺌﺎﺕ‬ ‫ﻅﺎﻫﺭﺓ ﻭﻟﻜﻥ ﻟﻬﺎ ﻤﺩﻟﻭل ﻜﻤﻲ ﻤﻌﻴﻥ‪ ،‬ﺒﻤﻌﻨﻰ ﺃﻨﻪ ﻋﻨﺩﻤﺎ ﺘﻜﻭﻥ ﻗﻴﻡ ﻫﺫﺍ ﺍﻟﻤﺘﻐﻴﺭ‬

‫ﺘﻤﺜل ﺩﻟﻴل ﻟﻘﻴﻡ ﺃﻭ ﻓﺌﺎﺕ ﻅﺎﻫﺭﺓ ﻜﻤﻴﺔ ﺃﻭ ﻭﺼﻔﻴﺔ ﻓﺈﻥ ﻫﺫﻩ ﺍﻟﻘﻴﻡ ﺘﺴﻤﺢ ﺒﺎﻟﻤﻔﺎﻀﻠﺔ‪،‬‬ ‫ﻭﺘﻌﺭﻑ ﻫﺫﻩ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ‪.Ordinal Data‬‬

‫‪ .3‬ﻤﺘﻐﻴﺭﺍﺕ ﻭﺼﻔﻴﺔ ﻭﺘﺄﺨﺫ ﻗﻴﻤﹰﺎ ﻭﺼﻔﻴﺔ ﺃﻭ ﺩﻟﻴل ﻜﻤﻲ ﻟﻬﺫﻩ ﺍﻟﻘﻴﻡ ﻓﻬﻲ ﺒﺨﻼﻑ‬ ‫ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺘﺭﺘﻴﺒﻴﺔ ﻻ ﻴﻭﺠﺩ ﺃﻱ ﻤﺩﻟﻭل ﻜﻤﻲ ﻟﻠﻘﻴﻡ ﺍﻟﺘﻲ ﺘﺄﺨﺫﻫﺎ ﻫﺫﻩ ﺍﻟﻅﺎﻫﺭﺓ‪،‬‬

‫ﻼ ﻗﺩ ﻴﺄﺨﺫ ﻤﺘﻐﻴﺭ ﺍﻟﻨﻭﻉ ﺍﻟﻘﻴﻤﺘﻴﻥ "ﺫﻜﺭ" ﻭ"ﺃﻨﺜﻰ" ﻭﻴﻘﺩ ﻴﺸﺎﺭ ﻟﻬﻤﺎ ﻋﻠﻰ ﺍﻟﺘﺭﺘﻴﺏ‬ ‫ﻓﻤﺜ ﹰ‬ ‫ﺒﺎﻟﻘﻴﻤﺘﻴﻥ ‪ 1‬ﻭ ‪ 0‬ﺇﻻ ﺃﻥ ﺍﻟﻘﻴﻤﺔ ‪ 0‬ﻋﻠﻰ ﺴﺒﻴل ﺍﻟﻤﺜﺎل ﻻ ﺘﻌﻨﻲ ﺍﻟﺼﻔﺭ ﺃﻭ "ﻻﺸﻲﺀ"‬

‫ﻜﻤﺎ ﻫﻭ ﺍﻟﺤﺎل ﻓﻲ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻷﺨﺭﻯ‪ ،‬ﻭﺘﻌﺭﻑ ﻫﺫﻩ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ‪Nominal‬‬

‫‪ Data‬ﺃﻭ ‪. Categorical Data‬‬

‫ﻓﺈﺫﺍ ﻜﺎﻥ ﻟﺩﻴﻨﺎ ﻋﻠﻰ ﺴﺒﻴل ﺍﻟﻤﺜﺎل ﺒﻴﺎﻨﺎﺕ ﺘﻤﺜل ﺃﻁﻭﺍل ﻤﺠﻤﻭﻋﺔ ﻤﻥ ﺍﻷﻁﻔﺎل‬

‫ﻤﻘﺎﺴﻪ ﺒﺎﻟﺴﻨﺘﻴﻤﺘﺭ‪ ،‬ﻓﺈﻨﻪ ﻋﺎﺩﺓ ﻗﺒل ﺘﺤﻠﻴل ﻫﺫﻩ ﺍﻟﺒﻴﺎﻨﺎﺕ ﺴﻴﻜﻭﻥ ﻫﻨﺎﻙ ﺜﻼﺙ ﺃﻤﻭﺭ ﻴﻨﺒﻐﻲ‬ ‫ﻤﻌﺭﻓﺘﻬﺎ ﻤﻥ ﻤﺜل ﻫﺫﻩ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪:‬‬

‫‪ .1‬ﻤﺭﻜﺯ ﻫﺫﻩ ﺍﻟﺒﻴﺎﻨﺎﺕ ﺃﻭ ﻤﺘﻭﺴﻁ ﺃﻁﻭﺍل ﺍﻷﻁﻔﺎل‪.‬‬

‫‪ .2‬ﻤﺩﻯ ﺍﻟﺘﺸﺘﺕ ﺒﻴﻥ ﻫﺫﻩ ﺍﻟﻘﻴﻡ ﻤﻘﺎﺴﹰﺎ ﺒﺄﺤﺩ ﻤﻘﺎﻴﻴﺱ ﺍﻟﺘﺸﺘﺕ ﺍﻟﻤﻌﺭﻭﻓﺔ‪.‬‬

‫‪ .3‬ﺸﻜل ﺘﻭﺯﻴﻊ ﺍﻟﺒﻴﺎﻨﺎﺕ ﻤﻘﺎﺴﹰﺎ ﺒﻤﻘﺎﻴﻴﺱ ﺍﻟﺘﺸﺘﺕ ﻭﺍﻟﺘﻔﻠﻁﺢ‪.‬‬

‫ﻭﺴﻨﻔﺘﺭﺽ ﻓﻲ ﻫﺫﺍ ﺍﻟﻜﺘﺎﺏ ﺃﻥ ﺍﻟﻤﺴﺘﺨﺩﻡ ﻟﺩﻴﻪ ﻓﻜﺭﺓ ﻋﻥ ﺘﻠﻙ ﺍﻟﻤﻘﺎﻴﻴﺱ‪ ،‬ﻓﻤﻥ‬

‫ﺍﻟﻤﻌﺭﻭﻑ ﺃﻥ ﺃﻫﻡ ﻤﻘﺎﻴﻴﺱ ﺍﻟﻤﺭﻜﺯ ﻫﻲ ﺍﻟﻭﺴﻁ ﺍﻟﺤﺴﺎﺒﻲ ‪ mean‬ﻭﺍﻟﻭﺴﻴﻁ ‪median‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

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‫ﻭﺍﻟﻤﻨﻭﺍل ‪ ، mode‬ﺒﻴﻨﻤﺎ ﺃﻫﻡ ﻤﻘﺎﻴﻴﺱ ﺍﻟﺘﺸﺘﺕ ﺍﻟﻤﻌﺭﻭﻓﺔ ﻫﻲ ﺍﻻﻨﺤﺭﺍﻑ ﺍﻟﻤﻌﻴﺎﺭﻱ‬ ‫‪ Standard deviation‬ﻭﺍﻟﺘﺒﺎﻴﻥ ‪ Variance‬ﻭﺍﻟﻤﺩﻯ ﺍﻟﺭﺒﻴﻌﻲ ‪، Quartile range‬‬ ‫ﻭﺴﻨﻔﺘﺭﺽ ﺃﻴﻀﹰﺎ ﺃﻥ ﻟﺩﻯ ﺍﻟﻤﺴﺘﺨﺩﻡ ﻓﻜﺭﺓ ﻋﻥ ﺍﻟﺘﻌﺒﻴﺭﺍﺕ ﺍﻟﻤﺘﻌﻠﻘﺔ ﺒﺘﻭﺯﻴﻊ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫ﻤﺜل ﺍﻻﻟﺘﻭﺍﺀ ‪ Skewness‬ﻭﺜﻨﺎﺌﻴﺔ ﺍﻟﻤﻨﻭﺍل ‪ Bimodality‬ﻭﻤﺎ ﺇﻟﻰ ﺫﻟﻙ‪ ،‬ﻓﺎﻟﻤﻘﺎﻴﻴﺱ‬ ‫ﻭﺍﻟﻁﺭﻕ ﺍﻟﻤﺨﺘﻠﻔﺔ ﻗﺩ ﺘﻜﻭﻥ ﻤﻨﺎﺴﺒﺔ ﻟﺒﻴﺎﻨﺎﺕ ﻭﻤﺘﻐﻴﺭﺍﺕ ﻤﺨﺘﻠﻔﺔ‪ ،‬ﻓﻌﻠﻰ ﺴﺒﻴل ﺍﻟﻤﺜﺎل ﻟﻥ‬

‫ﻴﻜﻭﻥ ﻫﻨﺎﻙ ﻤﻌﻨﻰ ﻤﻥ ﺤﺴﺎﺏ ﺍﻟﻤﺘﻭﺴﻁﺎﺕ ﻟﺒﻴﺎﻨﺎﺕ ﻻ ﺘﻤﺜل ﺇﻻ ﺭﺘﺏ ﻟﻘﻴﻡ ﻅﺎﻫﺭﺓ‬ ‫ﻤﻌﻴﻨﺔ ‪.‬‬

‫ﺇﻥ ﺤﺴﺎﺏ ﺒﻌﺽ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ﻋﺎﺩﺓ ﻤﺎ ﻴﻜﻭﻥ ﺒﻬﺩﻑ ﺇﻅﻬﺎﺭ ﺒﻌﺽ‬

‫ﺍﻟﺨﺼﺎﺌﺹ ﻭﺍﻟﻤﺅﺸﺭﺍﺕ ﻟﻠﻅﻭﺍﻫﺭ ﻗﻴﺩ ﺍﻟﺩﺭﺍﺴﺔ ﻓﻲ ﻤﻘﺎﻴﻴﺱ ﻜﻤﻴﺔ ﻤﺤﺩﻭﺩﺓ‪ ،‬ﻭﻟﻜﻥ ﻫﻨﺎﻙ‬

‫ﺒﻌﺽ ﺍﻟﺤﺎﻻﺕ ﺍﻟﺘﻲ ﺒﻬﺎ ﺘﻜﻭﻥ ﻫﺫﻩ ﺍﻟﻤﻘﺎﻴﻴﺱ ﻻ ﻤﻌﻨﻰ ﻟﻬﺎ ﺃﻭ ﻟﻬﺎ ﻤﻌﻨﻰ ﻤﻀﻠل‪ ،‬ﻭﺫﻟﻙ‬

‫ﻴﻌﻭﺩ ﺇﻟﻰ ﺃﻥ ﺍﻟﺒﻴﺎﻨﺎﺕ ﻤﻠﺘﻭﻴﺔ ﺒﺸﻜل ﺤﺎﺩ ﺃﻭ ﻴﻭﺠﺩ ﺒﻬﺎ ﺒﻌﺽ ﺍﻟﻘﻴﻡ ﺍﻟﻤﺘﻁﺭﻓﺔ ﺍﻟﺘﻲ‬ ‫ﺘﻌﺭﻑ ﺒﺎﻟﻘﻴﻡ ﺍﻟﺸﺎﺫﺓ ‪ Outliers‬ﻭﻫﺫﻩ ﻟﻬﺎ ﺘﺄﺜﻴﺭ ﻜﺒﻴﺭ ﻋﻠﻰ ﻗﻴﻡ ﺘﻠﻙ ﺍﻟﻤﻘﺎﻴﻴﺱ‪.‬‬

‫ﻋﺎﺩﺓ ﻤﺎ ﻴﻜﻭﻥ ﺍﻟﻬﺩﻑ ﺍﻟﻨﻬﺎﺌﻲ ﻤﻥ ﺍﻟﺘﺤﻠﻴل ﻫﻭ ﺇﺠﺭﺍﺀ ﺒﻌﺽ ﺍﻻﺨﺘﺒﺎﺭﺍﺕ‬ ‫ﺍﻹﺤﺼﺎﺌﻴﺔ ﻭﺍﻟﺘﻲ ﺘﺤﺘﺎﺝ ﺇﻟﻰ ﺘﻭﻓﺭ ﺒﻌﺽ ﺍﻟﻔﺭﻭﺽ ﻭﺍﻟﺨﺼﺎﺌﺹ ﻓﻲ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫ﻜﻤﺘﻁﻠﺏ ﻤﺴﺒﻕ ﻹﺠﺭﺍﺀ ﺘﻠﻙ ﺍﻻﺨﺘﺒﺎﺭﺍﺕ ﺃﻭ ﻟﺘﻁﺒﻴﻕ ﻨﻤﻭﺫﺝ ﺇﺤﺼﺎﺌﻲ ﻤﺤﺩﺩ‪ ،‬ﻓﻌﻠﻰ‬

‫ﺴﺒﻴل ﺍﻟﻤﺜﺎل ﻗﺒل ﺘﻁﺒﻴﻕ ﺍﺨﺘﺒﺎﺭ ‪ t‬ﻴﻨﺒﻐﻲ ﺃﻥ ﺘﻜﻭﻥ ﺍﻟﺒﻴﺎﻨﺎﺕ ﻤﺄﺨﻭﺫﺓ ﻤﻥ ﻤﺠﺘﻤﻊ ﻴﺘﺒﻊ‬ ‫ﺍﻟﺘﻭﺯﻴﻊ ﺍﻻﺤﺘﻤﺎﻟﻲ ﺍﻟﻁﺒﻴﻌﻲ‪ ،‬ﻭﺭﻏﻡ ﺃﻥ ﻫﺫﺍ ﺍﻻﺨﺘﺒﺎﺭ ﻴﺘﻤﺘﻊ ﺒﺒﻌﺽ ﺨﻭﺍﺹ ﺍﻟﻘﻭﺓ‬

‫‪ robustness‬ﻀﺩ ﺍﻨﺘﻬﺎﻙ ﺒﺴﻴﻁ ﻟﻬﺫﻩ ﺍﻟﺨﻭﺍﺹ ﺇﻻ ﺃﻨﻪ ﻴﺠﺏ ﺃﻥ ﺘﻜﻭﻥ ﺘﻠﻙ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫ﻤﻭﺯﻋﺔ ﺒﺸﻜل ﻻ ﻴﺒﺘﻌﺩ ﻜﺜﻴﺭﹰﺍ ﻋﻥ ﺸﻜل ﺍﻟﺘﻭﺯﻴﻊ ﺍﻟﻁﺒﻴﻌﻲ ﺍﻟﺫﻱ ﻴﻌﺭﻑ ﺃﻥ ﻤﻥ‬

‫ﺨﺼﺎﺌﺼﻪ ﺃﻨﻪ ﺘﻭﺯﻴﻊ ﻤﺘﻤﺎﺜل ﺃﻱ ﻏﻴﺭ ﻤﻠﺘﻭ ﺒﺸﻜل ﺤﺎﺩ‪ ،‬ﻭﻟﺫﻟﻙ ﻓﺈﻨﻪ ﻟﻠﺘﺄﻜﺩ ﻤﻥ ﺼﺤﺔ‬ ‫ﺍﻷﺴﺎﻟﻴﺏ ﺍﻹﺤﺼﺎﺌﻴﺔ ﺍﻟﻤﺴﺘﺨﺩﻤﺔ ﻓﻲ ﺍﻟﺘﺤﻠﻴل ﻴﻨﺒﻐﻲ ﻓﻲ ﺍﻟﺒﺩﺍﻴﺔ ﺍﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫ﻟﻤﻌﺭﻓﺔ ﻤﺩﻯ ﺍﻨﺘﻬﺎﻙ ﺘﻠﻙ ﺍﻟﻔﺭﻭﺽ ﺍﻟﻤﺘﻌﻠﻘﺔ ﺒﺎﻷﺴﻠﻭﺏ ﺍﻟﻤﺴﺘﺨﺩﻡ ﻭﺒﺎﻟﺘﺎﻟﻲ ﻤﺩﻯ ﺍﻟﺩﻗﺔ‬ ‫ﻓﻲ ﺍﺴﺘﺨﺩﺍﻡ ﻫﺫﺍ ﺍﻷﺴﻠﻭﺏ‪ ،‬ﻭﻗﺩ ﻴﺅﺩﻱ ﺍﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ ﺇﻟﻰ ﺍﺨﺘﻴﺎﺭ ﺃﺴﻠﻭﺏ‬ ‫ﺇﺤﺼﺎﺌﻲ ﺁﺨﺭ ﺃﻜﺜﺭ ﻤﻼﺌﻤﺔ ﺃﺤﻴﺎﻨﹰﺎ‪.‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪ .3 .4‬إﻳﺠﺎد اﻟﻨﻮاﻓﺬ‪:‬‬

‫‪137‬‬

‫‪Finding Menus‬‬

‫ﻗﺒل ﺍﻟﺨﻭﺽ ﻓﻲ ﺃﺴﺎﻟﻴﺏ ﺍﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ ﺍﻟﻤﺨﺘﻠﻔﺔ ﺍﻟﻤﺘﺎﺤﺔ ﻓﻲ ﻨﻅﺎﻡ ‪SPSS‬‬

‫ﻗﺩ ﻴﻜﻭﻥ ﻤﻥ ﺍﻟﻀﺭﻭﺭﻱ ﺘﺫﻜﻴﺭ ﺍﻟﻤﺴﺘﺨﺩﻡ ﺒﻜﻴﻔﻴﺔ ﺇﻴﺠﺎﺩ ﺍﻷﻭﺍﻤﺭ ﺍﻟﻤﺨﺘﻠﻔﺔ ﻭﺍﺴﺘﻜﻤﺎل‬ ‫ﺍﻟﻨﻭﺍﻓﺫ ﺍﻟﻤﺘﻌﻠﻘﺔ ﺒﻬﺎ ﻭﺘﻐﻴﻴﺭ ﺍﻟﻤﻌﻠﻭﻤﺎﺕ ﻭﺍﻟﻘﻴﻡ ﺍﻟﺘﻠﻘﺎﺌﻴﺔ ﺍﻟﻤﻭﺠﻭﺩﺓ ﺒﻬﺎ‪ ،‬ﻭﻴﺠﺩﺭ ﺒﺎﻟﺫﻜﺭ‬

‫ﺃﻥ ﻫﻨﺎﻙ ﻓﺭﻭﻗﹰﺎ ﻁﻔﻴﻔﺔ ﻓﻲ ﺒﻌﺽ ﺍﻷﻭﺍﻤﺭ ﺍﻟﻤﺘﻌﻠﻘﺔ ﺒﺎﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ ﻓﻲ ﺇﺼﺩﺍﺭﻱ‬ ‫ﺍﻟﻨﻅﺎﻡ ‪ SPSS‬ﺍﻟﻤﺴﺘﺨﺩﻤﻴﻥ ﺤﺎﻟﻴﹰﺎ ‪ 8.0‬ﻭ ‪ ، 11.0‬ﻭﻟﺫﻟﻙ ﻓﺈﻨﻨﺎ ﺴﻨﻭﻀﺢ ﺍﻟﻔﺭﻕ ﺒﻴﻥ‬ ‫ﺍﻹﺼﺩﺍﺭﻴﻥ ﻫﻨﺎ ﻭﻜﻴﻔﻴﺔ ﺍﺴﺘﺨﺩﺍﻡ ﻋﻤﻠﻴﺎﺕ ﺍﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ ﻓﻲ ﻜل ﻤﻨﻬﻤﺎ ‪.‬‬

‫ﻓﻔﻲ ﻗﺎﺌﻤﺔ ﻨﻅﺎﻡ ‪ SPSS‬ﺍﻟﺭﺌﻴﺴﻴﺔ ﻓﻲ ﻜل ﻤﻥ ﺍﻹﺼﺩﺍﺭﻴﻥ ‪ 8.0‬ﻭ ‪ 11.0‬ﺘﻭﺠﺩ‬

‫ﻋﺸﺭ ﺃﻭﺍﻤﺭ ﻤﺨﺘﻠﻔﺔ ﻴﺘﺒﻊ ﻜل ﻤﻨﻬﺎ ﻗﺎﺌﻤﺔ ﺨﺎﺼﺔ ﺒﻬﺎ‪ ،‬ﻭﻫﻨﺎﻙ ﻤﻥ ﺒﻴﻥ ﺘﻠﻙ ﺍﻟﻘﻭﺍﺌﻡ‬

‫ﻭﺍﻷﻭﺍﻤﺭ ﻤﺎ ﺘﻡ ﺍﻟﺘﻁﺭﻕ ﺇﻟﻴﻬﺎ ﻓﻲ ﺍﻟﻔﺼﻠﻴﻥ ﺍﻟﺴﺎﺒﻘﻴﻥ‪ ،‬ﻭﺠﻤﻴﻊ ﺍﻷﻭﺍﻤﺭ ﺍﻟﻤﺘﻌﻠﻘﺔ ﺒﻬﺫﺍ‬

‫ﺍﻟﻔﺼل ﺘﺩﺨل ﻀﻤﻥ ﻗﺎﺌﻤﺔ ﺍﻟﺘﺤﻠﻴﻠﻲ ﺍﻹﺤﺼﺎﺌﻲ ‪ Statistics‬ﻓﻲ ﺇﺼﺩﺍﺭ ‪ 8.0‬ﻭ‬

‫‪ Analyze‬ﻓﻲ ﺇﺼﺩﺍﺭ ‪ ، 11.0‬ﻭﺒﺩﺍﺨل ﻗﺎﺌﻤﺔ ﺍﻟﺘﺤﻠﻴل ﺍﻹﺤﺼﺎﺌﻲ ‪ Statistics‬ﺃﻭ‬ ‫‪Analyze‬‬

‫ﺴﻨﺩﺨل ﺒﺎﻟﺘﺤﺩﻴﺩ ﻓﻲ ﻨﻭﺍﻓﺫ ﺍﻷﻤﺭ ﻤﻘﺎﻴﻴﺱ ﻭﺼﻔﻴﺔ‬

‫‪Descriptive‬‬

‫‪ Statistics‬ﻓﻲ ﺇﺼﺩﺍﺭ ‪ 11.0‬ﺃﻭ ﺍﻷﻤﺭ ﺘﻠﺨﻴﺹ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪ Summarize‬ﻓﻲ ﺇﺼﺩﺍﺭ‬

‫‪ ،8.0‬ﻓﻌﻨﺩ ﺍﻹﺸﺎﺭﺓ ﺇﻟﻰ ﺘﻠﻙ ﺍﻟﻘﺎﺌﻤﺔ ﻭﺍﻹﺸﺎﺭﺓ ﺇﻟﻰ ﺃﻤﺭ ﻤﻘﺎﻴﻴﺱ ﻭﺼﻔﻴﺔ ‪Descriptive‬‬

‫‪ Statistics‬ﺃﻭ ﺃﻤﺭ ﺘﻠﺨﻴﺹ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪ Summarize‬ﺘﻔﺘﺢ ﻗﺎﺌﻤﺔ ﻓﺭﻋﻴﺔ ﻜﻤﺎ ﻓﻲ ﺍﻟﺸﻜل‬

‫‪ 1-4‬ﺍﻟﺨﺎﺹ ﺒﺸﺎﺸﺔ ﺍﻹﺼﺩﺍﺭ ‪ 11.0‬ﻭﺍﻟﺸﻜل ‪ 2-4‬ﻓﻲ ﺍﻹﺼﺩﺍﺭ ‪ ، 8.0‬ﻭﺘﺤﺘﻭﻱ‬

‫ﻫﺫﻩ ﺍﻟﻘﺎﺌﻤﺔ ﺍﻟﻔﺭﻋﻴﺔ ﻋﻠﻰ ﻤﺠﻤﻭﻋﺔ ﻤﻥ ﺍﻷﻭﺍﻤﺭ ‪.‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪138‬‬

‫ﺸﻜل ‪ : 1-4‬ﺃﻭﺍﻤﺭ ﺍﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ ﻓﻲ ﻗﺎﺌﻤﺔ ﺍﻟﺘﺤﻠﻴل ﺍﻹﺤﺼﺎﺌﻲ ‪ Analyze‬ﻓﻲ‬ ‫ﺍﻟﻘﺎﺌﻤﺔ ﺍﻟﺭﺌﻴﺴﻴﺔ ﻓﻲ ﻨﻅﺎﻡ ‪ SPSS‬ﺇﺼﺩﺍﺭ ‪. 11.0‬‬

‫ﺸﻜل ‪ : 2-4‬ﺃﻭﺍﻤﺭ ﺍﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ ﻓﻲ ﻗﺎﺌﻤﺔ ﺍﻟﺘﺤﻠﻴل ﺍﻹﺤﺼﺎﺌﻲ ‪ Statistics‬ﻓﻲ‬ ‫ﺍﻟﻘﺎﺌﻤﺔ ﺍﻟﺭﺌﻴﺴﻴﺔ ﻓﻲ ﻨﻅﺎﻡ ‪ SPSS‬ﺇﺼﺩﺍﺭ ‪. 8.0‬‬

‫ﻻﺤﻅ ﺃﻥ ﻗﺎﺌﻤﺔ ‪ S-Plus‬ﺍﻟﺘﻲ ﺘﻅﻬﺭ ﻓﻲ ﺍﻟﻘﺎﺌﻤﺔ ﺍﻟﺭﺌﻴﺴﻴﺔ ﻟﻠﻨﻅﺎﻡ ﻜﻤﺎ ﻓﻲ ﺍﻟﺸﻜل‬

‫‪1-4‬ﻫﻲ ﺇﻀﺎﻓﻴﺔ ﻭﺘﺸﻴﺭ ﺇﻟﻰ ﻨﻅﺎﻡ ﺇﺤﺼﺎﺌﻲ ﺁﺨﺭ ﻭﻟﻥ ﺘﻅﻬﺭ ﻁﺎﻟﻤﺎ ﻟﻡ ﻴﺘﻡ ﺘﺭﻜﻴﺏ‬

‫ﺫﻟﻙ ﺍﻟﻨﻅﺎﻡ‪ ،‬ﻜﻤﺎ ﺃﻨﻪ ﻓﻲ ﺍﻹﺼﺩﺍﺭ ‪ 11.0‬ﺘﻡ ﺘﻘﺴﻴﻡ ﺃﻤﺭ ﺘﻠﺨﻴﺹ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪ Summarize‬ﻓﻲ ﺇﺼﺩﺍﺭ ‪ 8.0‬ﺇﻟﻰ ﺃﻤﺭﻴﻥ ﻫﻤﺎ ﺍﻟﺘﻘﺎﺭﻴﺭ ‪ Reports‬ﻭﺍﻟﻤﻘﺎﻴﻴﺱ‬ ‫ﺍﻟﻭﺼﻔﻴﺔ ‪ Descriptive Statistics‬ﻜﻤﺎ ﻴﻅﻬﺭ ﻓﻲ ﺍﻟﺸﻜﻠﻴﻥ ﺃﻋﻼﻩ‪ ،‬ﻭﻓﻲ ﺍﻟﺤﺎﻟﺘﻴﻥ‬

‫ﻴﻭﺠﺩ ‪ 8‬ﺃﻭﺍﻤﺭ ﺘﻅﻬﺭ ﻤﻌﹰﺎ ﺘﺤﺕ ﺃﻤﺭ ﺘﻠﺨﻴﺹ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪ Summarize‬ﻓﻲ ﺇﺼﺩﺍﺭ ‪8.0‬‬ ‫ﺒﻴﻨﻤﺎ ﺘﻨﻘﺴﻡ ﻫﺫﻩ ﺍﻷﻭﺍﻤﺭ ﺇﻟﻰ ﻤﺠﻤﻭﻋﺘﻴﻥ ﻜل ﻤﻨﻬﻤﺎ ﻤﻜﻭﻥ ﻤﻥ ‪ 4‬ﺃﻭﺍﻤﺭ ﻭﺘﻅﻬﺭﺍﻥ‬

‫ﺘﺤﺕ ﺍﻻﺴﻤﻴﻥ ﺍﻟﺘﻘﺎﺭﻴﺭ ‪ Reports‬ﻭﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻟﻭﺼﻔﻴﺔ ‪ Descriptive Statistics‬ﻓﻲ‬ ‫ﺍﻹﺼﺩﺍﺭ ‪ 11.0‬ﻭﻫﺫﻩ ﺍﻷﻭﺍﻤﺭ ﻫﻲ ‪:‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪139‬‬

‫• ﺠﺩﺍﻭل ﺘﻜﺭﺍﺭﻴﺔ‬

‫‪Frequencies‬‬

‫• ﻤﻘﺎﻴﻴﺱ ﻭﺼﻔﻴﺔ‬

‫‪Descriptive‬‬

‫• ﺍﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪Explore‬‬

‫• ﺘﻜﻭﻴﻥ ﺠﺩﺍﻭل ﻤﺭﻜﺒﺔ‬

‫•‬

‫ﺘﻘﺎﺭﻴﺭ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻔﻭﺭﻱ‬

‫‪Cross-table‬‬ ‫)‪Layered Reports (OLAP Cubes‬‬

‫• ﻤﻠﺨﺹ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ‬

‫• ﺘﻘﺭﻴﺭ ﻤﻠﺨﺹ ﻓﻲ ﺍﻟﺼﻔﻭﻑ‬ ‫•‬

‫‪Case Summaries‬‬ ‫‪Report Summaries In Rows‬‬

‫ﺘﻘﺭﻴﺭ ﻤﻠﺨﺹ ﻓﻲ ﺍﻷﻋﻤﺩﺓ ‪Report Summaries In Columns‬‬

‫ﺜﻤﺔ ﺃﻤﺭ ﺁﺨﺭ ﻴﻤﻜﻥ ﺍﺴﺘﺨﺩﺍﻤﻪ ﺒﻬﺩﻑ ﺍﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ ﺃﻴﻀﹰﺎ ﻫﻭ ﺍﻷﻤﺭ‬

‫ﺤﺴﺎﺏ ﺍﻟﻤﺘﻭﺴﻁﺎﺕ ‪ Means‬ﺒﺩﺍﺨل ﻗﺎﺌﻤﺔ ﻤﻘﺎﺭﻨﺔ ﺍﻟﻤﺘﻭﺴﻁﺎﺕ ‪، Compare Means‬‬ ‫ﻭﺍﻟﻘﺎﺌﻤﺔ ﺍﻷﺨﻴﺭﺓ ﻫﻲ ﻤﻥ ﻀﻤﻥ ﺃﻭﺍﻤﺭ ﻗﺎﺌﻤﺔ ﺍﻟﺘﺤﻠﻴل ﺍﻹﺤﺼﺎﺌﻲ ‪ Statistics‬ﺃﻭ‬

‫‪ Analyze‬ﻓﻲ ﺍﻟﻘﺎﺌﻤﺔ ﺍﻟﺭﺌﻴﺴﻴﺔ ﻟﻠﻨﻅﺎﻡ‪.‬‬

‫ﻭﻴﻘﺩﻡ ﺃﻤﺭ ﺍﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﺒﻴﺎﻨﻴﺔ ‪ Graphs‬ﻓﻲ ﺍﻟﻘﺎﺌﻤﺔ ﺍﻟﺭﺌﻴﺴﻴﺔ ﻟﻨﻅﺎﻡ ‪ SPSS‬ﻋﺩﺩ‬ ‫ﻜﺒﻴﺭ ﻤﻥ ﺃﺸﻜﺎل ﺍﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﺒﻴﺎﻨﻴﺔ ﺍﻟﻤﺨﺘﻠﻔﺔ ﻓﻲ ﻗﺎﺌﻤﺔ ﻜﺎﻤﻠﺔ ) ﺃﻨﻅﺭ ﻫﺫﻩ ﺍﻟﻘﺎﺌﻤﺔ ﻓﻲ‬

‫ﺸﻜل ‪ ، (3-4‬ﻭﻫﺫﻩ ﺍﻟﻘﺎﺌﻤﺔ ﺴﺘﻜﻭﻥ ﻤﺤﻭﺭ ﺤﺩﻴﺜﻨﺎ ﻓﻲ ﺍﻟﻔﺼل ﺍﻟﺨﺎﻤﺱ ﻤﻥ ﻫﺫﺍ ﺍﻟﻜﺘﺎﺏ‬

‫‪ ،‬ﻭﺭﻏﻡ ﺫﻟﻙ ﻓﺈﻥ ﻫﻨﺎﻙ ﻋﺩﺩ ﻜﺒﻴﺭ ﻤﻥ ﻫﺫﻩ ﺍﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﺒﻴﺎﻨﻴﺔ ﻴﻤﻜﻥ ﺍﻟﺤﺼﻭل ﻋﻠﻴﻬﺎ‬

‫ﺃﺜﻨﺎﺀ ﺘﻨﻔﻴﺫ ﻭﻤﻥ ﺨﻼل ﺃﻭﺍﻤﺭ ﺃﺨﺭﻯ ﻓﻲ ﺍﻟﻨﻅﺎﻡ ﺨﺎﺼﺔ ﺃﺜﻨﺎﺀ ﻋﻤﻠﻴﺔ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬ ‫ﺒﺎﻹﻀﺎﻓﺔ ﺇﻟﻰ ﺒﻌﺽ ﺘﻠﻙ ﺍﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﺘﻲ ﺘﻅﻬﺭ ﺃﺜﻨﺎﺀ ﻤﺭﺤﻠﺔ ﺘﺤﻠﻴل ﺍﻟﺒﻴﺎﻨﺎﺕ‪ ،‬ﻭﻟﺫﻟﻙ‬

‫ﻓﺈﻨﻨﺎ ﺴﻨﺘﻁﺭﻕ ﻓﻲ ﻫﺫﺍ ﺍﻟﻔﺼل ﻓﻘﻁ ﺇﻟﻰ ﺒﻌﺽ ﺍﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﺒﻴﺎﻨﻴﺔ ﺍﻟﺘﻲ ﻴﻤﻜﻥ ﺍﻟﺤﺼﻭل‬ ‫ﻋﻠﻴﻬﺎ ﻭﺴﻭﻑ ﺘﻌﺘﺭﻀﻨﺎ ﺃﺜﻨﺎﺀ ﺍﻟﺤﺩﻴﺙ ﻋﻥ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ ﺒﻴﻨﻤﺎ ﺴﻨﻌﺭﺽ ﺍﻵﻥ ﻗﺎﺌﻤﺔ‬ ‫ﺍﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﺒﻴﺎﻨﻴﺔ ﻟﻜﻲ ﻴﺘﻌﺭﻑ ﻋﻠﻴﻬﺎ ﺍﻟﻤﺴﺘﺨﺩﻡ ﻤﻊ ﻤﺭﺍﻋﺎﺓ ﺃﻨﻨﺎ ﺴﻨﻘﻭﻡ ﺒﺎﻟﺘﻌﺎﻤل ﻤﻌﻬﺎ‬

‫ﺒﺎﻟﺘﻔﺼﻴل ﻓﻲ ﺍﻟﻔﺼل ﺍﻟﻼﺤﻕ ‪.‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

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‫ﺸﻜل ‪ : -4‬ﻗﺎﺌﻤﺔ ﺍﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﺒﻴﺎﻨﻴﺔ ﻓﻲ ﺍﻟﻘﺎﺌﻤﺔ ﺍﻟﺭﺌﻴﺴﻴﺔ ﻓﻲ ﻨﻅﺎﻡ ‪ SPSS‬ﺇﺼﺩﺍﺭ ‪.11.0‬‬

‫‪ .4 .4‬وﺻﻒ وﺗﺒﻮﻳﺐ اﻟﺒﻴﺎﻧﺎت ‪:‬‬

‫‪Describing Data‬‬

‫ﻗﺒل ﺍﻟﺒﺩﺀ ﻓﻲ ﺘﻭﻀﻴﺢ ﺍﺴﺘﺨﺩﺍﻤﺎﺕ ﺍﻷﻭﺍﻤﺭ ﺍﻟﻤﺨﺘﻠﻔﺔ ﺍﻟﻤﺘﻌﻠﻘﺔ ﺒﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫ﻴﺤﺴﻥ ﺃﻥ ﻨﺄﺨﺫ ﻤﺠﻤﻭﻋﺔ ﻤﻨﺎﺴﺒﺔ ﻤﻥ ﺍﻟﺒﻴﺎﻨﺎﺕ ﻟﻠﺘﻁﺒﻴﻕ ﻋﻠﻴﻬﺎ‪ ،‬ﻭﻟﻠﺘﺴﻬﻴل ﻋﻠﻰ‬ ‫ﺍﻟﻤﺴﺘﺨﺩﻡ ﻓﻘﺩ ﺍﺴﺘﺨﺩﻤﻨﺎ ﻤﺠﻤﻭﻋﺔ ﺍﻟﺒﻴﺎﻨﺎﺕ ﺍﻟﻤﺘﺎﺤﺔ ﻤﻊ ﺍﻟﻨﻅﺎﻡ ﻭﺍﻟﻤﺨﺯﻨﺔ ﻓﻲ ﻤﻠﻑ ﺒﺎﺴﻡ‬

‫‪ Employee data‬ﺤﻴﺙ ﺃﻨﻬﺎ ﺘﺤﺘﻭﻱ ﻋﻠﻰ ﻋﺩﺩ ﻤﻥ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﺨﺘﻠﻔﺔ ﺍﻟﻨﻭﻉ‬

‫ﻭﺍﻟﻤﻨﺎﺴﺒﺔ ﻟﻐﺭﺽ ﺘﻭﻀﻴﺢ ﺘﻠﻙ ﺍﻷﻭﺍﻤﺭ‪ ،‬ﻓﻠﻨﻔﺘﺭﺽ ﺍﻵﻥ ﺃﻨﻪ ﻗﺩ ﺘﻡ ﻓﺘﺢ ﻤﻠﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫ﻓﻲ ﻤﺤﺭﺭ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪ ، Data Editor‬ﻭﻴﺒﻴﻥ ﺍﻟﺸﻜل ‪ 4-4‬ﺠﺯﺀ ﻤﻥ ﻫﺫﻩ ﺍﻟﺒﻴﺎﻨﺎﺕ‪.‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

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‫ﺸﻜل ‪ :4-4‬ﺠﺎﻨﺏ ﻤﻥ ﺍﻟﺒﻴﺎﻨﺎﺕ ﻓﻲ ﺍﻟﻤﻠﻑ ‪ Employee data‬ﺍﻟﻤﺘﺎﺡ ﻤﻊ ﺍﻟﻨﻅﺎﻡ‪.‬‬

‫‪ .1 .4 .4‬وﺻﻒ اﻟﺒﻴﺎﻧﺎت اﻟﻨﻮﻋﻴﺔ ‪Describing Categorical Data :‬‬ ‫ﺘﺤﺘﻭﻱ ﻗﺎﺌﻤﺔ ﺘﻠﺨﻴﺹ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪ Summarize‬ﻓﻲ ﻗﺎﺌﻤﺔ ﺍﻟﺘﺤﻠﻴل ﺍﻹﺤﺼﺎﺌﻲ‬ ‫‪ Statistics‬ﺃﻭ ‪ Analyze‬ﻋﻠﻰ ﺃﻭﺍﻤﺭ ﻭﺇﺠﺭﺍﺀﺍﺕ ﻟﻭﺼﻑ ﻭﺘﻠﺨﻴﺹ ﺍﻟﺒﻴﺎﻨﺎﺕ ﺍﻟﻨﻭﻋﻴﺔ‬

‫ﺃﻭ ﺍﻟﻭﺼﻔﻴﺔ ‪ ،qualitative and categorical data‬ﻭﺍﻟﻤﻘﺼﻭﺩ ﺒﺎﻟﺘﺤﺩﻴﺩ ﻫﻭ ﺃﻭﺍﻤﺭ‬ ‫ﺘﻜﻭﻴﻥ ﺠﺩﺍﻭل ﺘﻜﺭﺍﺭﻴﺔ ﺒﺴﻴﻁﺔ ‪ Frequencies‬ﻭﺘﻜﻭﻴﻥ ﺠﺩﺍﻭل ﺘﻜﺭﺍﺭﻴﺔ ﻤﺭﻜﺒﺔ‬

‫‪ ، Crosstabs‬ﺒﺎﻹﻀﺎﻓﺔ ﺇﻟﻰ ﺫﻟﻙ ﻴﻭﺠﺩ ﺒﻌﺽ ﺍﻷﻭﺍﻤﺭ ﻭﺍﻹﺠﺭﺍﺀﺍﺕ ﻓﻲ ﻗﺎﺌﻤﺔ‬ ‫ﺍﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﺒﻴﺎﻨﻴﺔ ‪ Graphs‬ﻓﻲ ﺍﻟﻘﺎﺌﻤﺔ ﺍﻟﺭﺌﻴﺴﻴﺔ ﻟﻨﻅﺎﻡ ‪ SPSS‬ﻭﺍﻟﺘﻲ ﻴﻤﻜﻥ‬

‫ﺍﺴﺘﺨﺩﺍﻤﻬﺎ ﻟﻭﺼﻑ ﻭﺘﻠﺨﻴﺹ ﺍﻟﺒﻴﺎﻨﺎﺕ‪ ،‬ﻭﻟﻜﻨﻨﺎ ﺴﻨﻘﻭﻡ ﺒﺸﺭﺤﻬﺎ ﻓﻲ ﺍﻟﻔﺼل ﺍﻟﻼﺤﻕ‪.‬‬

‫ﻭﻴﻌﻁﻲ ﺍﻷﻤﺭ ﺘﻜﻭﻴﻥ ﺠﺩﺍﻭل ﺘﻜﺭﺍﺭﻴﺔ ‪ Frequencies‬ﺠﺩﺍﻭل ﺘﻜﺭﺍﺭﻴﺔ ﺒﺴﻴﻁﺔ‬

‫ﻟﻜل ﻤﺘﻐﻴﺭ ﻋﻠﻰ ﺤﺩﻩ ﻭﺫﻟﻙ ﻤﻬﻤﺎ ﻜﺎﻥ ﻨﻭﻉ ﻫﺫﻩ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ‪ ،‬ﻭﻫﻨﺎﻙ ﺨﻴﺎﺭﺍﺕ ﺃﺨﺭﻯ‬

‫ﻟﻬﺫﺍ ﺍﻷﻤﺭ ﺘﺘﻴﺢ ﺤﺴﺎﺏ ﻤﺠﻤﻭﻋﺔ ﻜﺒﻴﺭﺓ ﻤﻥ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ﺒﺎﻹﻀﺎﻓﺔ ﺇﻟﻰ ﺒﻌﺽ‬

‫ﺍﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﺒﻴﺎﻨﻴﺔ‪ ،‬ﻭﺒﺎﻟﻤﺜل ﻴﻌﻁﻲ ﺍﻷﻤﺭ ﺍﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪ Explore‬ﺠﻤﻴﻊ ﻫﺫﻩ‬

‫ﺍﻟﻤﻘﺎﻴﻴﺱ ﺒﺎﻹﻀﺎﻓﺔ ﺇﻟﻰ ﻤﺨﺘﻠﻑ ﺍﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﺒﻴﺎﻨﻴﺔ‪ ،‬ﺃﻤﺎ ﺃﻤﺭ ﺘﻜﻭﻴﻥ ﺍﻟﺠﺩﺍﻭل ﺍﻟﻤﺭﻜﺒﺔ‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

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‫ﻓﺈﻨﻪ ﻴﻤﻜﻥ ﻤﻥ ﺘﻜﻭﻴﻥ ﺠﺩﺍﻭل ﻤﺯﺩﻭﺠﺔ ﺃﻭ ﺠﺩﺍﻭل ﺘﻭﺍﻓﻕ ﺃﻭ ﺍﻗﺘﺭﺍﻥ ‪Contingency‬‬

‫‪ Tables‬ﻟﻤﺘﻐﻴﺭﻴﻥ ﻤﻌﹰﺎ ﺃﻭ ﺃﻜﺜﺭ ﻤﻥ ﻤﺘﻐﻴﺭﻴﻥ ﻭﺘﻅﻬﺭ ﺍﻟﺘﺸﺎﺒﻙ ﺒﻴﻥ ﻫﺫﻩ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ‬ ‫ﻭﺍﻟﻌﻼﻗﺔ ﺒﻴﻨﻬﻤﺎ‪ ،‬ﺒﺎﻹﻀﺎﻓﺔ ﺇﻟﻰ ﺫﻟﻙ ﻴﻤﻜﻥ ﺃﻥ ﻴﻅﻬﺭ ﻓﻲ ﺍﻟﺠﺩﺍﻭل ﺍﻟﻨﺴﺏ ﺍﻟﻤﺌﻭﻴﺔ‬ ‫ﻟﺨﻼﻴﺎ ﺍﻟﺠﺩﻭل ﺒﺎﻟﻨﺴﺒﺔ ﺇﻟﻰ ﺍﻟﺼﻔﻭﻑ ﺃﻭ ﺍﻷﻋﻤﺩﺓ ﺃﻭ ﺍﻟﻤﺠﻤﻭﻉ ﺍﻟﻜﻠﻲ ﺤﺴﺏ ﻤﺎ ﻴﺘﻡ‬

‫ﺘﺤﺩﻴﺩﻩ ﻓﻲ ﺨﻴﺎﺭﺍﺕ ﻫﺫﺍ ﺍﻷﻤﺭ‪ ،‬ﻭﻴﻌﻁﻲ ﻫﺫﺍ ﺍﻷﻤﺭ ﻜﺫﻟﻙ ﻤﺠﻤﻭﻋﺔ ﻤﻥ ﺍﻹﺤﺼﺎﺀﺍﺕ‬

‫‪ Statistics‬ﺍﻟﺘﻲ ﻴﻤﻜﻥ ﺍﺴﺘﺨﺩﺍﻤﻬﺎ ﻓﻲ ﺍﺨﺘﺒﺎﺭ ﻓﺭﻀﻴﺔ ﺍﺴﺘﻘﻼل ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﻋﻥ ﺒﻌﻀﻬﺎ‬ ‫ﺍﻟﺒﻌﺽ‪ ،‬ﻭﺴﻨﺘﺤﺩﺙ ﺍﻵﻥ ﻋﻥ ﻜﻴﻔﻴﺔ ﺍﺴﺘﺨﺩﺍﻡ ﻜل ﺃﻤﺭ ﻤﻥ ﻫﺫﻩ ﺍﻷﻭﺍﻤﺭ ﻜل ﻋﻠﻰ ﺤﺩﻩ‪.‬‬ ‫ﺃﻤﺭ ﺘﻜﻭﻴﻥ ﺍﻟﺠﺩﺍﻭل ﺍﻟﺘﻜﺭﺍﺭﻴﺔ ‪: Frequencies‬‬

‫ﻭﻴﻤﻜﻥ ﺘﻭﻀﻴﺢ ﻫﺫﺍ ﺍﻷﻤﺭ ﺒﺎﻟﻤﺜﺎل ﺍﻟﺘﺎﻟﻲ‪ ،‬ﺇﺫﺍ ﺃﺨﺫﻨﺎ ﻤﺘﻐﻴﺭ ﻤﺘﻘﻁﻊ ‪Discrete‬‬

‫ﻤﺜل ﻋﺩﺩ ﺃﻓﺭﺍﺩ ﺍﻷﺴﺭﺓ ﺃﻭ ﻋﺩﺩ ﺍﻟﻐﺭﻑ ﻓﻲ ﺍﻟﻤﻨﺯل‪ ..... ،‬ﺃﻭ ﺃﺨﺫﻨﺎ ﻤﺘﻐﻴﺭﹰﺍ ﻭﺼﻔﻴﹰﺎ‬

‫ﻤﺜل ﺍﻟﺤﺎﻟﺔ ﺍﻻﺠﺘﻤﺎﻋﻴﺔ ﺃﻭ ﺍﻟﻤﻨﻁﻘﺔ ﺃﻭ ﺍﻟﺠﻨﺱ‪ .... ،‬ﻓﺈﻥ ﺍﻟﺘﺤﻠﻴل ﻴﺘﻡ ﺒﻨﻔﺱ ﺍﻟﻁﺭﻴﻘﺔ‪،‬‬

‫ﻭﻓﻲ ﻫﺫﺍ ﺍﻟﻤﺜﺎل ﺒﺎﻟﺘﺤﺩﻴﺩ ﺇﺫﺍ ﺃﺨﺫﻨﺎ ﺍﻟﻤﺘﻐﻴﺭ ﺍﻟﻭﺼﻔﻲ ‪ jobcat‬ﺍﻟﺫﻱ ﻴﻤﺜل ﻓﺌﺔ ﺍﻟﻭﻅﻴﻔﺔ‬

‫‪ Employment Category‬ﻓﻲ ﻤﻠﻑ ‪ Employee data‬ﺍﻟﻤﺸﺎﺭ ﺇﻟﻴﻪ ﻓﺈﻨﻨﺎ ﻨﺘﺒﻊ ﻤﺎ‬ ‫ﻴﻠﻲ‪:‬‬

‫• ﺍﻀﻐﻁ ﺒﺎﻟﻔﺎﺭﺓ ﻋﻠﻰ ‪ Frequencies‬ﻓﻲ ﻗﺎﺌﻤﺔ ﺍﻹﺤﺼﺎﺀﺍﺕ ﺍﻟﻭﺼﻔﻴﺔ‬

‫‪ ) Descriptive Statistics‬ﺃﻭ ﻗﺎﺌﻤﺔ ﺘﻠﺨﻴﺹ ‪ Summarize‬ﻓﻲ ﺇﺼﺩﺍﺭ ‪ (8.0‬ﻓﺘﻔﺘﺢ‬ ‫ﻨﺎﻓﺫﺓ ﺍﻟﺠﺩﺍﻭل ﺍﻟﺘﻜﺭﺍﺭﻴﺔ ‪ Frequencies‬ﻜﻤﺎ ﻓﻲ ﺍﻟﺸﻜل ‪. 5-4‬‬

‫• ﻨﺨﺘﺎﺭ ﺍﻟﻤﺘﻐﻴﺭ ﺍﻟﺫﻱ ﻨﺭﻴﺩ ﺘﻜﻭﻴﻥ ﺠﺩﻭل ﺘﻜﺭﺍﺭﻱ ﻟﻪ ﺒﺎﻟﻀﻐﻁ ﺒﺎﻟﻔﺄﺭﺓ ﻤﺭ ﹰﺓ ﻭﺍﺤﺩﺓ‬ ‫ﺜﻡ ﻨﻀﻐﻁ ﻋﻠﻰ ﺍﻟﺴﻬﻡ ﺍﻟﻤﻭﺠﻭﺩ ﺒﻴﻥ ﺍﻟﻘﺎﺌﻤﺘﻴﻥ ﻓﻲ ﺍﻟﻨﺎﻓﺫﺓ ﻟﻴﻨﺘﻘل ﺍﺴﻡ ﻫﺫﺍ ﺍﻟﻤﺘﻐﻴﺭ ﻤﻥ‬

‫ﺒﻴﻥ ﺃﺴﻤﺎﺀ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﻓﻲ ﺍﻟﻘﺎﺌﻤﺔ ﺍﻟﻴﺴﺭﻯ ﺇﻟﻰ ﺍﻟﻘﺎﺌﻤﺔ ﺍﻟﻴﻤﻨﻰ‪ ،‬ﻭﻴﻤﻜﻥ ﺒﺘﻜﺭﺍﺭ ﺍﻟﻌﻤﻠﻴﺔ‬ ‫ﻨﻘل ﺃﺴﻤﺎﺀ ﺠﻤﻴﻊ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺘﻲ ﻨﺭﻴﺩ ﺘﺤﻠﻴﻠﻬﺎ ﻟﺘﻅﻬﺭ ﺘﺤﺕ ﻜﻠﻤﺔ ‪. Variables‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

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‫ﺸﻜل ‪ : 5-4‬ﻨﺎﻓﺫﺓ ﺘﻜﻭﻴﻥ ﺍﻟﺠﺩﺍﻭل ﺍﻟﺘﻜﺭﺍﺭﻴﺔ ‪. frequencies‬‬

‫• ﻓﻲ ﻨﻔﺱ ﺍﻟﻘﺎﺌﻤﺔ ﻓﻲ ﺍﻷﺴﻔل ‪ ،‬ﻴﻭﺠﺩ ﺜﻼﺜﺔ ﺨﻴﺎﺭﺍﺕ ﻫﻡ ﺍﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﺒﻴﺎﻨﻴﺔ‬ ‫‪ Charts‬ﻭﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ‪ Statistics‬ﻭﺍﻟﺸﻜل ‪ Format‬ﻓﺒﺎﺨﺘﻴﺎﺭ ﺍﻟﺭﺴﻭﻤﺎﺕ‬

‫ﺍﻟﺒﻴﺎﻨﻴﺔ ‪ Charts‬ﺘﻔﺘﺢ ﻨﺎﻓﺫﺓ ﺨﻴﺎﺭﺍﺕ ﺍﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﺒﻴﺎﻨﻴﺔ ﻜﻤﺎ ﻓﻲ ﺍﻟﺸﻜل ‪ 6-4‬ﻟﻨﺨﺘﺎﺭ‬ ‫ﺃﺤﺩ ﺍﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﺒﻴﺎﻨﻴﺔ ﺍﻟﻤﻨﺎﺴﺒﺔ ﻟﻬﺫﺍ ﺍﻟﻨﻭﻉ ﻤﻥ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﻭﻫﻭ ﺍﻷﻋﻤﺩﺓ ﺍﻟﺒﺴﻴﻁﺔ‬

‫‪ ،Bar Charts‬ﻭﻴﻤﻜﻨﻙ ﺃﻴﻀﹰﺎ ﺍﺨﺘﻴﺎﺭ ﺍﻟﺘﻜﺭﺍﺭﺍﺕ ﺃﻭ ﻨﺴﺒﻬﺎ ﺍﻟﻤﺌﻭﻴﺔ ﻟﺘﻤﺜﻴﻠﻬﺎ ﻋﻠﻰ‬ ‫ﺍﻟﻤﺤﻭﺭ ﺍﻟﺭﺃﺴﻲ ﻓﻲ ﺍﻟﺸﻜل ‪.‬‬

‫• ﻭﺍﻵﻥ ‪ ،‬ﺒﺎﺨﺘﻴﺎﺭ ﻜﻠﻤﺔ ‪ Statistics‬ﻭﺫﻟﻙ ﺒﺎﻟﻀﻐﻁ ﻋﻠﻴﻬﺎ ﺒﺎﻟﻔﺎﺭﺓ ﻤﺭ ﹰﺓ ﻭﺍﺤﺩﺓ ﺘﻔﺘﺢ‬

‫ﻨﺎﻓﺫﺓ ﺍﻹﺤﺼﺎﺀﺍﺕ ‪ Statistics‬ﻜﻤﺎ ﻓﻲ ﺸﻜل ‪ 7-4‬ﻟﻴﺘﻡ ﺍﺨﺘﻴﺎﺭ ﺘﻠﻙ ﺍﻹﺤﺼﺎﺀﺍﺕ‬

‫ﺍﻟﻤﺭﺍﺩ ﺤﺴﺎﺒﻬﺎ‪ ،‬ﻭﻓﻲ ﻫﺫﺍ ﺍﻟﻤﺜﺎل ﻓﻘﺩ ﺍﺨﺘﺭﻨﺎ ﺍﻹﺤﺼﺎﺀﺍﺕ ﺍﻟﻤﺘﻭﺴﻁ ‪ Mean‬ﻭﺍﻟﻭﺴﻴﻁ‬

‫‪ Median‬ﻭﺍﻟﻤﻨﻭﺍل ‪ Mode‬ﻟﻴﺘﻡ ﺤﺴﺎﺒﻬﺎ‪ ،‬ﻭﻗﺩ ﺍﺨﺘﺭﻨﺎ ﻫﺫﻩ ﺍﻹﺤﺼﺎﺀﺍﺕ ﻟﻠﺘﻭﻀﻴﺢ ﺭﻏﻡ‬ ‫ﺃﻥ ﻫﺫﻩ ﺍﻹﺤﺼﺎﺀﺍﺕ ﻏﻴﺭ ﻤﻨﺎﺴﺒﺔ ﻟﻌﺩﻡ ﻭﺠﻭﺩ ﻤﻌﻨﻰ ﻟﻬﺎ ﻓﻲ ﺤﺎﻟﺔ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻭﺼﻔﻴﺔ‬ ‫ﻤﺜل ﻫﺫﺍ ﺍﻟﻤﺘﻐﻴﺭ‪ ،‬ﻭﺘﻘﺩﻡ ﻫﺫﻩ ﺍﻟﻨﺎﻓﺫﺓ ﻋﺩﺓ ﻤﻘﺎﻴﻴﺱ ﺇﺤﺼﺎﺌﻴﺔ ﻴﻤﻜﻨﻙ ﺍﺨﺘﻴﺎﺭ ﺍﻟﻤﻘﻴﺎﺱ‬

‫ﺍﻟﺫﻱ ﺘﺭﻴﺩﻩ ﻟﻠﻤﺘﻐﻴﺭ ﺃﻭ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﻟﻴﺘﻡ ﺤﺴﺎﺒﻪ ﺃﺜﻨﺎﺀ ﻋﻤﻠﻴﺔ ﺘﻜﻭﻴﻥ ﺍﻟﺠﺩﺍﻭل ﺍﻟﺘﻜﺭﺍﺭﻴﺔ‪،‬‬ ‫ﻭﻫﺫﻩ ﺍﻟﻤﻘﺎﻴﻴﺱ ﻫﻲ‪:‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪144‬‬

‫ ﺍﻟﻤﺌﻭﻴﺎﺕ ‪ Percentiles‬ﻭﺘﻌﻁﻲ ﺍﻟﻌﺩﺩ ﺍﻟﺫﻱ ﻴﻘﻊ ﺩﻭﻨﻪ ﻨﺴﺒﺔ ﻤﻌﻴﻨﺔ ﻤﻥ ﺍﻟﺒﻴﺎﻨﺎﺕ‬‫ﻼ ﻋﻨﺩ ﺇﻋﻁﺎﺀ ﺍﻟﻌﺩﺩ ‪ 10‬ﻓﺈﻥ ﺍﻟﻨﺘﻴﺠﺔ ﻫﻲ ﺍﻟﻘﻴﻤﺔ ﺍﻟﺘﻲ ﻴﻘﻊ ﺩﻭﻨﻬﺎ ‪ %10‬ﻤﻥ‬ ‫ﻓﻤﺜ ﹰ‬ ‫ﺍﻟﺒﻴﺎﻨﺎﺕ‪.‬‬

‫‪ -‬ﺍﻟﺭﺒﻴﻌﻴﺎﺕ ‪ Quartiles‬ﻫﻲ ﺸﺒﻴﻬﺔ ﺒﻘﻴﻡ ‪ Percentiles‬ﻭﻟﻜﻥ ﻋﻨﺩ ﺃﺭﺒﺎﻉ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ‬

‫ﺃﻱ )‪.(%75 ، %50 ، %25‬‬ ‫‪ Mean -‬ﺍﻟﻤﺘﻭﺴﻁ ﺍﻟﺤﺴﺎﺒﻲ‪.‬‬

‫ ‪ Median‬ﺍﻟﻭﺴﻴﻁ‪.‬‬‫‪ Mode -‬ﺍﻟﻤﻨﻭﺍل‪.‬‬

‫‪ Minimum -‬ﺃﺼﻐﺭ ﻗﻴﻤﺔ‪.‬‬

‫‪ Maximum -‬ﺃﻜﺒﺭ ﻗﻴﻤﺔ‪.‬‬

‫‪ Sum -‬ﺍﻟﻤﺠﻤﻭﻉ‪.‬‬

‫‪ Std. deviation -‬ﺍﻻﻨﺤﺭﺍﻑ ﺍﻟﻤﻌﻴﺎﺭﻱ‪.‬‬

‫‪Variance -‬‬

‫‪Range -‬‬

‫ﺍﻟﻤﺩﻯ‪.‬‬

‫ﺍﻟﺘﺒﺎﻴﻥ‪.‬‬

‫ ‪ S.E. Mean‬ﺍﻟﺨﻁﺄ ﺍﻟﻤﻌﻴﺎﺭﻱ ﻟﻠﻤﺘﻭﺴﻁ‪.‬‬‫‪ Skewness -‬ﻤﻘﻴﺎﺱ ﺍﻻﻟﺘﻭﺍﺀ‪.‬‬

‫‪Kurtosis -‬‬

‫ﻤﻘﻴﺎﺱ ﺍﻟﺘﻔﻠﻁﺢ‪.‬‬

‫ﺸﻜل ‪ : 6-4‬ﻨﺎﻓﺫﺓ ﺍﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﺒﻴﺎﻨﻴﺔ ‪ Charts‬ﻓﻲ ﺃﻤﺭ‬ ‫ﺘﻜﻭﻴﻥ ﺍﻟﺠﺩﺍﻭل ﺍﻟﺘﻜﺭﺍﺭﻴﺔ ‪. Frequencies‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪145‬‬

‫ﺸﻜل ‪ : 7-4‬ﻨﺎﻓﺫﺓ ﺍﻹﺤﺼﺎﺀﺍﺕ ‪ Statistics‬ﻓﻲ ﺃﻤﺭ‬ ‫ﺘﻜﻭﻴﻥ ﺍﻟﺠﺩﺍﻭل ﺍﻟﺘﻜﺭﺍﺭﻴﺔ ‪. Frequencies‬‬

‫•‬

‫ﻭﺍﻵﻥ‪ ،‬ﻴﺘﺒﻘﻰ ﺨﻴﺎﺭ ﻭﺍﺤﺩ ﻤﻥ ﺒﻴﻥ ﺍﻟﺨﻴﺎﺭﺍﺕ ﺍﻟﻤﺘﺎﺤﺔ ﻭﻫﻭ ﺸﻜل ﺍﻟﺠﺩﻭل ‪Format‬‬

‫ﻓﺒﺎﺨﺘﻴﺎﺭ ﺃﻤﺭ ‪ Format‬ﺘﻔﺘﺢ ﻨﺎﻓﺫﺓ ﺍﻟﺸﻜل ﻜﻤﺎ ﻓﻲ ﺸﻜل ‪ 8-4‬ﻟﻴﺘﻡ ﺍﺨﺘﻴﺎﺭ ﻜﻴﻔﻴﺔ ﺘﺭﺘﻴﺏ‬

‫ﻓﺌﺎﺕ ﺍﻟﻤﺘﻐﻴﺭ ﻭﻗﺩ ﺍﺨﺘﺭﻨﺎﻫﺎ ﻓﻲ ﻫﺫﺍ ﺍﻟﻤﺜﺎل ﺘﺼﺎﻋﺩﻴﹰﺎ ‪. Ascending Values‬‬

‫• ﻭﻓﻲ ﺍﻟﻨﻬﺎﻴﺔ ﻴﻤﻜﻨﻙ ﺇﺨﺘﻴﺎﺭ ﺃﻤﺭ ﺍﻟﻤﻭﺍﺼﻠﺔ ‪ Continue‬ﻹﻨﻬﺎﺀ ﺍﻟﻌﻤﻠﻴﺔ ﻭﺘﻨﻔﻴﺫ‬ ‫ﺍﻷﻤﺭ ﻟﻠﺤﺼﻭل ﻋﻠﻰ ﺍﻟﻨﺘﺎﺌﺞ ﻜﻤﺎ ﻓﻲ ﺍﻟﺸﻜل ‪.9-4‬‬ ‫ﺸﻜل ‪ :8-4‬ﻨﺎﻓﺫﺓ ﺍﻟﺸﻜل ‪ Format‬ﻓﻲ ﺃﻤﺭ ﺘﻜﻭﻴﻥ ﺍﻟﺠﺩﺍﻭل ﺍﻟﺘﻜﺭﺍﺭﻴﺔ ‪.Frequencies‬‬

‫( ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬4)

146

‫ ﻤﺤﺭﺭ ﺍﻟﻨﺘﺎﺌﺞ ﻭﺘﻅﻬﺭ ﺒﻪ ﻨﺘﺎﺌﺞ ﺍﻟﺘﺤﻠﻴل ﻤﻥ ﺘﻜﻭﻴﻥ ﺠﺩﻭل ﺘﻜﺭﺍﺭﻱ ﻟﻅﺎﻫﺭﺓ‬: 9-4 ‫ﺸﻜل‬ .Frequencies ‫ﻓﺌﺔ ﺍﻟﻭﻅﻴﻔﺔ ﺒﺎﺴﺘﺨﺩﺍﻡ ﺃﻤﺭ ﺘﻜﻭﻴﻥ ﺍﻟﺠﺩﺍﻭل ﺍﻟﺘﻜﺭﺍﺭﻴﺔ‬ Frequencies Statistics Employment Category N

Valid

474

Missing

0

Mean

1.41

Median

1.00

Mode

1

Employment Category

Frequency Valid

Clerical

Percent

Cumulative Percent

363

76.6

76.6

76.6

Custodial

27

5.7

5.7

82.3

Manager

84

17.7

17.7

100.0

474

100.0

100.0

Total Employment Category 400

300

200

Frequency

Valid Percent

100

0 Clerical

Employment Category

Custodial

Manager

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪147‬‬

‫ﻭﻴﻼﺤﻅ ﺃﻥ ﻨﺘﺎﺌﺞ ﺃﻤﺭ ﺘﻜﻭﻴﻥ ﺍﻟﺠﺩﺍﻭل ﺍﻟﺘﻜﺭﺍﺭﻴﺔ ‪ Frequencies‬ﺍﻟﺴﺎﺒﻕ ﺘﺘﻜﻭﻥ‬ ‫ﻤﻥ ﺠﺩﻭﻟﻴﻥ ﻭﺸﻜل ﺒﻴﺎﻨﻲ‪ ،‬ﻓﺎﻟﺠﺩﻭل ﺍﻷﻭل ﻴﺒﻴﻥ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ﺍﻟﻤﻁﻠﻭﺒﺔ ﺒﻴﻨﻤﺎ‬

‫ﻴﻭﻀﺢ ﺍﻟﺠﺩﻭل ﺍﻟﺜﺎﻨﻲ ﺍﻟﺠﺩﻭل ﺍﻟﺘﻜﺭﺍﺭﻱ ﺍﻟﺒﺴﻴﻁ ﻤﺒﻴﻨﹰﺎ ﻓﻴﻪ ﺍﻟﺘﻜﺭﺍﺭﺍﺕ ﺍﻟﻤﻁﻠﻘﺔ ﻓﻲ ﻜل‬

‫ﻓﺌﺔ ﺒﺎﻹﻀﺎﻓﺔ ﺇﻟﻰ ﺍﻟﺘﻜﺭﺍﺭﺍﺕ ﺍﻟﻨﺴﺒﻴﺔ ﻭﻜﺫﻟﻙ ﺍﻟﺘﻜﺭﺍﺭﺍﺕ ﺍﻟﻤﺘﺠﻤﻌﺔ ﺍﻟﺼﺎﻋﺩﺓ‪ ،‬ﻻﺤﻅ‬ ‫ﺃﻴﻀﹰﺎ ﺃﻥ ﻨﺘﺎﺌﺞ ﺠﻤﻴﻊ ﺃﻭﺍﻤﺭ ﻨﻅﺎﻡ ‪ SPSS‬ﺴﻭﺍ ًﺀ ﻜﺎﻥ ﺇﺼﺩﺍﺭ ‪ 8.0‬ﺃﻭ ﺇﺼﺩﺍﺭ ‪11.0‬‬

‫ﺘﺫﻫﺏ ﺇﻟﻰ ﺸﺎﺸﺔ ﺨﺎﺼﺔ ﺒﺎﻟﻨﺘﺎﺌﺞ ﻴﻁﻠﻕ ﻋﻠﻴﻬﺎ ﻤﺤﺭﺭ ﺍﻟﻨﺘﺎﺌﺞ ‪ ، Output Viewer‬ﻜﻤﺎ‬

‫ﺃﻥ ﺍﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﺒﻴﺎﻨﻴﺔ ﻴﻤﻜﻥ ﺍﻟﺤﺼﻭل ﻋﻠﻴﻬﺎ ﺒﺄﻭﺍﻤﺭ ﺨﺎﺼﺔ ﺴﻨﺘﺤﺩﺙ ﻋﻨﻬﺎ ﻻﺤﻘﹰﺎ ‪.‬‬

‫ﻭﻜﻤﺎ ﺫﻜﺭﻨﺎ ﺴﺎﺒﻘﹰﺎ ﻓﻘﺩ ﺍﺴﺘﺨﺩﻡ ﻓﻲ ﺍﻟﻤﺜﺎل ﺍﻟﺴﺎﺒﻕ ﻤﺘﻐﻴﺭ ﻭﺼﻔﻲ ‪ ،‬ﻭﺒﺎﻟﻤﺜل‬

‫ﻴﻜﻭﻥ ﺍﻟﺘﻌﺎﻤل ﻤﻊ ﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻜﻤﻴﺔ ﺍﻟﻤﺘﻘﻁﻌﺔ ‪ Discrete‬ﺨﺎﺼﺔ ﻋﻨﺩﻤﺎ ﻴﻜﻭﻥ ﻋﺩﺩ‬

‫ﻼ ‪ Continuous‬ﺃﻭ‬ ‫ﻗﻴﻤﻬﺎ ﻤﺤﺩﻭﺩ‪ ،‬ﻭﻴﺨﺘﻠﻑ ﺍﻟﻭﻀﻊ ﻋﻨﺩﻤﺎ ﻴﻜﻭﻥ ﺍﻟﻤﺘﻐﻴﺭ ﻜﻤﻴﹰﺎ ﻤﺘﺼ ﹰ‬ ‫ﻜﻤﻴﹰﺎ ﻤﺘﻘﻁﻌﹰﺎ ﻭﻟﻜﻥ ﻴﺄﺨﺫ ﻋﺩﺩﹰﺍ ﻜﺒﻴﺭﹰﺍ ﻤﻥ ﺍﻟﻘﻴﻡ ﺍﻟﻤﺨﺘﻠﻔﺔ ﻤﺜل ﻋﺩﺩ ﺍﻟﺘﻼﻤﻴﺫ ﻓﻲ ﺍﻟﻔﺼل‬

‫ﺃﻭ ﻋﺩﺩ ﺍﻟﺯﺍﺌﺭﻴﻥ ﻟﻤﺼﻠﺤﺔ ﺘﺠﺎﺭﻴﺔ ﻤﻌﻴﻨﺔ ﻓﻲ ﺍﻟﻴﻭﻡ‪ ،‬ﺤﻴﺙ ﻓﻲ ﻫﺫﻩ ﺍﻟﺤﺎﻻﺕ ﻴﺠﺏ‬

‫ﺘﻘﺴﻴﻡ ﻗﻴﻡ ﺍﻟﻤﺘﻐﻴﺭ ﺇﻟﻰ ﻓﺌﺎﺕ ﻭﺍﺴﺘﺨﺩﺍﻡ ﺃﻤﺭ ﺍﻟﺘﺼﻨﻴﻑ ‪ Recode‬ﻜﻤﺎ ﺴﺒﻕ ﺘﻭﻀﻴﺤﻪ‬

‫ﻓﻲ ﺍﻟﻔﺼل ﺍﻟﺴﺎﺒﻕ ‪ ،‬ﻭﻴﺭﺍﻋﻰ ﻋﻨﺩ ﺘﺤﺩﻴﺩ ﺍﻟﻔﺌﺎﺕ ﺃﻥ ﻴﺤﺩﺩ ﻋﺩﺩﻫﺎ ﺒﺤﻴﺙ ﻴﻜﻭﻥ ﻁﻭل‬ ‫ﻜل ﻓﺌﺔ ﻤﻨﺎﺴﺒﹰﺎ ﻭﻋﺩﺩ ﺍﻟﻔﺌﺎﺕ ﻟﻴﺱ ﻜﺒﻴﺭﹰﺍ ﺠﺩﹰﺍ ﺃﻭ ﺼﻐﻴﺭﹰﺍ ﺠﺩﺍﹰ‪ ،‬ﻭﺍﻟﻌﺩﺩ ﺍﻟﻤﻨﺎﺴﺏ ﻟﻬﺫﻩ‬

‫ﺍﻟﻔﺌﺎﺕ ﻴﺘﺭﺍﻭﺡ ﻤﻥ ‪ 5‬ﺇﻟﻰ ‪ 15‬ﻓﺌﺔ‪ ،‬ﻜﻤﺎ ﻴﺭﺍﻋﻰ ﻋﺩﻡ ﺍﻟﺘﺩﺍﺨل ﺒﻴﻥ ﺍﻟﻔﺌﺎﺕ ﻭﻜﺫﻟﻙ ﺃﻥ‬

‫ﺘﻜﻭﻥ ﺍﻟﻔﺌﺎﺕ ﺸﺎﻤﻠﺔ ﻟﺠﻤﻴﻊ ﺍﻟﺒﻴﺎﻨﺎﺕ‪.‬‬

‫ﺃﻤﺭ ﺘﻜﻭﻴﻥ ﺍﻟﺠﺩﺍﻭل ﺍﻟﺘﻜﺭﺍﺭﻴﺔ ﺍﻟﻤﺭﻜﺒﺔ ‪: Crosstabs‬‬ ‫ﻭﻴﺘﻡ ﻤﻥ ﺨﻼل ﻫﺫﺍ ﺍﻷﻤﺭ ﺘﻜﻭﻴﻥ ﺍﻟﺘﻭﺯﻴﻊ ﺍﻟﺘﻜﺭﺍﺭﻱ ﺍﻟﻤﺯﺩﻭﺝ )ﺠﺩﺍﻭل ﺍﻗﺘﺭﺍﻥ(‬

‫‪ Contingency Tables‬ﻟﻤﺘﻐﻴﺭﻴﻥ ﻭﺼﻔﻴﻴﻥ )ﺃﻭ ﻜﻤﻴﻴﻥ ﻤﺘﻘﻁﻌﻴﻥ( ﺒﺤﻴﺙ ﻴﺘﻡ ﺘﺤﺩﻴﺩ‬ ‫ﺃﺤﺩ ﺍﻟﻤﺘﻐﻴﺭﻴﻥ ﻓﻲ ﺍﻟﺼﻔﻭﻑ ﻭﺍﻟﻤﺘﻐﻴﺭ ﺍﻵﺨﺭ ﻓﻲ ﺍﻷﻋﻤﺩﺓ ‪ ،‬ﻭﻴﻤﻜﻥ ﻜﺫﻟﻙ ﻤﻥ ﺨﻼل‬ ‫ﻫﺫﺍ ﺍﻷﻤﺭ ﺘﻜﻭﻴﻥ ﺠﺩﺍﻭل ﺍﻗﺘﺭﺍﻥ ﻷﻜﺜﺭ ﻤﻥ ﻤﺘﻐﻴﺭﻴﻥ ﻭﺼﻔﻴﻴﻥ ﺃﻭ ﻜﻤﻴﻴﻥ ﻤﺘﻘﻁﻌﻴﻥ‪،‬‬

‫ﻜﺫﻟﻙ ﻴﻤﻜﻥ ﺃﻥ ﺘﺤﺘﻭﻱ ﺍﻟﺠﺩﺍﻭل ﻋﻠﻰ ﺍﻟﺘﻜﺭﺍﺭﺍﺕ ﺃﻭ ﺍﻟﻨﺴﺏ ﺍﻟﻤﺌﻭﻴﺔ ﻟﻠﺨﻼﻴﺎ ﺒﺎﻟﻨﺴﺒﺔ‬

‫ﻟﻠﺼﻔﻭﻑ ﺃﻭ ﻟﻸﻋﻤﺩﺓ ﻟﻠﻤﺠﻤﻭﻉ ﺃﻭ ﺠﻤﻴﻊ ﻫﺫﻩ ﺍﻟﻘﻴﻡ‪.‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪148‬‬

‫ﻭﻟﺘﻭﻀﻴﺢ ﻁﺭﻴﻘﺔ ﺘﻜﻭﻴﻥ ﺍﻟﺠﺩﺍﻭل ﺍﻟﺘﻜﺭﺍﺭﻴﺔ ﺍﻟﻤﺭﻜﺒﺔ ﺒﺎﺴﺘﺨﺩﺍﻡ ﻫﺫﺍ ﺍﻷﻤﺭ‬ ‫ﺴﻭﻑ ﻨﺴﺘﺨﺩﻡ ﺒﻴﺎﻨﺎﺕ ﺍﻟﻤﺘﻐﻴﺭ ﺍﻟﻭﺼﻔﻲ ‪ jobcat‬ﺍﻟﺫﻱ ﻴﻤﺜل ﻓﺌﺔ ﺍﻟﻭﻅﻴﻔﺔ‬

‫‪ Employment Category‬ﻭﺍﻟﻤﺘﻐﻴﺭ ﺍﻟﻭﺼﻔﻲ ‪ gender‬ﺍﻟﺫﻱ ﻴﻤﺜل ﺍﻟﻨﻭﻉ )‪ m‬ﻟﻠﺫﻜﺭ‬ ‫ﻭ ‪ f‬ﻟﻸﻨﺜﻰ( ﻓﻲ ﻤﻠﻑ ‪ Employee data‬ﺍﻟﻤﺸﺎﺭ ﺇﻟﻴﻪ ﺴﺎﺒﻘﺎﹰ‪ ،‬ﻭﻟﻬﺫﺍ ﻓﺈﻨﻨﺎ ﻨﺘﺒﻊ ﻤﺎ ﻴﻠﻲ‪:‬‬

‫• ﺍﻀﻐﻁ ﺒﺎﻟﻔﺎﺭﺓ ﻋﻠﻰ ﺍﻷﻤﺭ ‪ Crosstabs‬ﻓﻲ ﻗﺎﺌﻤﺔ ﺍﻹﺤﺼﺎﺀﺍﺕ ﺍﻟﻭﺼﻔﻴﺔ‬

‫‪) Descriptive Statistics‬ﺃﻭ ﻗﺎﺌﻤﺔ ﺘﻠﺨﻴﺹ ‪ Summarize‬ﻓﻲ ﺇﺼﺩﺍﺭ ‪ (8.0‬ﻓﺘﻔﺘﺢ‬ ‫ﻨﺎﻓﺫﺓ ﺘﻜﻭﻴﻥ ﺠﺩﺍﻭل ﺍﻻﻗﺘﺭﺍﻥ ‪ Crosstabs‬ﻜﻤﺎ ﻓﻲ ﺍﻟﺸﻜل ‪. 10-4‬‬

‫• ﺃﺩﺨل ﺃﺤﺩ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﻤﻥ ﻗﺎﺌﻤﺔ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﻋﻠﻰ ﻴﺴﺎﺭ ﺍﻟﻨﺎﻓﺫﺓ ﺇﻟﻰ ﺍﻟﺼﻔﻭﻑ‬

‫)‪ Raw(s‬ﻋﻥ ﻁﺭﻴﻕ ﺍﻟﻀﻐﻁ ﻋﻠﻰ ﺍﺴﻡ ﻫﺫﺍ ﺍﻟﻤﺘﻐﻴﺭ ﻭﺇﺯﺍﺤﺘﻪ ﺒﺎﻟﺴﻬﻡ ﺇﻟﻰ ﺍﻟﻤﺭﺒﻊ‬

‫ﺍﻟﻌﻠﻭﻱ ﻋﻠﻰ ﺍﻟﻴﻤﻴﻥ‪ ،‬ﻭﺒﺎﻟﻤﺜل ﺃﺩﺨل ﺍﻟﻤﺘﻐﻴﺭ ﺍﻵﺨﺭ ﻤﻥ ﻗﺎﺌﻤﺔ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺇﻟﻰ ﺍﻷﻋﻤﺩﺓ‬

‫)‪ Column(s‬ﻋﻥ ﻁﺭﻴﻕ ﺍﻟﻀﻐﻁ ﻋﻠﻰ ﺍﺴﻡ ﻫﺫﺍ ﺍﻟﻤﺘﻐﻴﺭ ﻭﺇﺯﺍﺤﺘﻪ ﺒﺎﻟﺴﻬﻡ ﺇﻟﻰ ﺍﻟﻤﺭﺒﻊ‬ ‫ﺍﻷﻭﺴﻁ ﻋﻠﻰ ﺍﻟﻴﻤﻴﻥ ‪ ،‬ﻭﻴﺤﺴﻥ ﻫﻨﺎ ﺍﺨﺘﻴﺎﺭ ﺍﻟﻤﺘﻐﻴﺭ ﺫﻭ ﺍﻟﻔﺌﺎﺕ ﺍﻷﻜﺜﺭ ﻋﺩﺩﺍ ﻟﻴﻜﻭﻥ ﻓﻲ‬

‫ﺍﻟﺼﻔﻭﻑ ﺤﺘﻰ ﻻ ﻴﻜﻭﻥ ﺍﻟﺠﺩﻭل ﻋﺭﻴﻀﹰﺎ ‪.‬‬

‫ﺸﻜل ‪ : 10-4‬ﻨﺎﻓﺫﺓ ﺘﻜﻭﻴﻥ ﺠﺩﺍﻭل ﺍﻻﻗﺘﺭﺍﻥ ‪ Crosstabs‬ﻓﻲ ﻗﺎﺘﻤﺔ ﺍﻟﺘﺤﻠﻴل ﺍﻹﺤﺼﺎﺌﻲ ‪.‬‬

‫• ﻴﻤﻜﻥ ﻤﻥ ﺨﻼل ﻫﺫﺍ ﺍﻷﻤﺭ ﺘﻜﻭﻴﻥ ﺭﺴﻡ ﺒﻴﺎﻨﻲ ﺒﺸﻜل ﺍﻷﻋﻤﺩﺓ ﺍﻟﻤﺘﻼﺼﻘﺔ‬ ‫‪ Clustered Bar Charts‬ﻭﺫﻟﻙ ﺒﺎﻟﻀﻐﻁ ﻋﻠﻰ ﺍﻟﺨﻴﺎﺭ ‪Display clustered bar‬‬

‫‪ charts‬ﻓﻲ ﺃﺴﻔل ﻨﺎﻓﺫﺓ ﺍﻟﻤﺤﺎﻭﺭﺓ ‪. Crosstabs‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪149‬‬

‫• ﻴﻤﻜﻥ ﺃﻴﻀﹰﺎ ﻤﻥ ﺨﻼل ﻫﺫﺍ ﺍﻷﻤﺭ ﺤﺴﺎﺏ ﺒﻌﺽ ﺍﻹﺤﺼﺎﺀﺍﺕ ﺍﻟﻤﺘﻌﻠﻘﺔ ﺒﺠﺩﺍﻭل‬ ‫ﺍﻹﻗﺘﺭﺍﻥ ﻭﺍﻟﻌﻼﻗﺔ ﺒﻴﻥ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﻭﺫﻟﻙ ﺒﺎﻟﻀﻐﻁ ﻋﻠﻰ ﺨﻴﺎﺭ ﺍﻹﺤﺼﺎﺀﺍﺕ ‪Statistics‬‬

‫ﻓﻲ ﺃﺴﻔل ﻨﺎﻓﺫﺓ ﺍﻟﻤﺤﺎﻭﺭﺓ ‪ Crosstabs‬ﻟﺘﻔﺘﺢ ﻨﺎﻓﺫﺓ ﻤﺤﺎﻭﺭﺓ ﺠﺩﻴﺩﺓ ﻭﻫﻲ ﻨﺎﻓﺫﺓ‬ ‫ﺍﻹﺤﺼﺎﺀﺍﺕ ‪ Statistics‬ﻜﻤﺎ ﻓﻲ ﺸﻜل ‪ ،11-4‬ﻭﺘﺤﺘﻭﻱ ﻋﻠﻰ ﺠﻤﻴﻊ ﺍﻹﺤﺼﺎﺀﺍﺕ‬

‫‪ Statistics‬ﺍﻟﺘﻲ ﻴﻤﻜﻥ ﺤﺴﺎﺒﻬﺎ ﻻﺨﺘﺒﺎﺭ ﻓﺭﻀﻴﺎﺕ ﺘﺘﻌﻠﻕ ﺒﺎﻟﻌﻼﻗﺔ ﺒﻴﻥ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ‪،‬‬ ‫ﻫﺫﻩ ﺍﻹﺤﺼﺎﺀﺍﺕ ﺴﻴﺘﻡ ﺍﻟﺤﺩﻴﺙ ﻋﻨﻬﺎ ﻻﺤﻘﹰﺎ ‪ ،‬ﻭﻟﻜﻥ ﻴﺠﺏ ﻤﻌﺭﻓﺔ ﺃﻨﻪ ﻴﻤﻜﻨﻨﺎ ﻤﻥ ﻫﻨﺎ‬ ‫ﺤﺴﺎﺏ ﺃﻱ ﻤﻥ ﺃﻭ ﺠﻤﻴﻊ ﺍﻹﺤﺼﺎﺀﺍﺕ ﺍﻟﻤﺫﻜﻭﺭﺓ ﻓﻲ ﻫﺫﺍ ﺍﻟﻤﺭﺒﻊ ﺃﺜﻨﺎﺀ ﺘﻜﻭﻴﻥ ﺠﺩﺍﻭل‬ ‫ﺍﻻﻗﺘﺭﺍﻥ‪ ،‬ﻜﻤﺎ ﻴﻤﻜﻨﻨﺎ ﺃﻥ ﻨﻔﺭﺽ ﻅﻬﻭﺭ ﺍﻹﺤﺼﺎﺀﺍﺕ ﻓﻘﻁ ﺒﺩﻭﻥ ﺍﻟﺠﺩﺍﻭل ﻭﺫﻟﻙ‬

‫ﺒﺎﻟﻀﻐﻁ ﻋﻠﻰ ﺍﻟﺨﻴﺎﺭ ‪ Suppress Tables‬ﻓﻲ ﺃﺴﻔل ﻨﺎﻓﺫﺓ ﺍﻟﻤﺤﺎﻭﺭﺓ ‪ Crosstabs‬ﻓﻲ‬ ‫ﺍﻟﺸﻜل ‪.10-4‬‬

‫ﺸﻜل ‪ : 10-4‬ﻨﺎﻓﺫﺓ ﺍﻹﺤﺼﺎﺀﺍﺕ ‪ Statistics‬ﺍﻟﻤﺘﻔﺭﻋﺔ ﻤﻥ ﻨﺎﻓﺫﺓ ﺘﻜﻭﻴﻥ ﺠﺩﺍﻭل‬ ‫ﺍﻻﻗﺘﺭﺍﻥ ‪ Crosstabs‬ﻓﻲ ﻗﺎﺘﻤﺔ ﺍﻟﺘﺤﻠﻴل ﺍﻹﺤﺼﺎﺌﻲ ‪.‬‬

‫• ﻭﻴﻤﻜﻥ ﺘﺤﺩﻴﺩ ﻤﺤﺘﻭﻴﺎﺕ ﺨﻼﻴﺎ ﺍﻟﺠﺩﺍﻭل ﺍﻟﺘﻲ ﻴﺘﻡ ﺘﻜﻭﻴﻨﻬﺎ ﻋﻥ ﻁﺭﻴﻕ ﻓﺘﺢ ﻨﺎﻓﺫﺓ‬

‫ﻤﺤﺘﻭﻴﺎﺕ ﺨﻼﻴﺎ ﺍﻟﺠﺩﻭل ‪ Cell Display‬ﻜﻤﺎ ﻓﻲ ﺸﻜل ‪ 11-4‬ﻭﺫﻟﻙ ﺒﺎﻟﻀﻐﻁ ﻋﻠﻰ‬

‫ﺍﻟﺨﻴﺎﺭ ‪ Cell Display‬ﻓﻲ ﺃﺴﻔل ﻨﺎﻓﺫﺓ ﺍﻟﻤﺤﺎﻭﺭﺓ ‪ Crosstabs‬ﻭﻤﻥ ﺜﻡ ﺍﺨﺘﻴﺎﺭ ﺍﻟﻘﻴﻡ‬

‫‪ Counts‬ﺍﻷﺼﻠﻴﺔ )ﺍﻟﻤﺸﺎﻫﺩﺓ( ‪ Observed‬ﺃﻭ ﺍﻟﻘﻴﻡ ﺍﻟﻤﺘﻭﻗﻌﺔ ‪ Expected‬ﺃﻭ ﺍﻟﻨﺴﺏ‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪150‬‬

‫ﺍﻟﻤﺌﻭﻴﺔ ‪ Percentages‬ﺒﺎﻟﻨﺴﺒﺔ ﻟﻠﺼﻔﻭﻑ ‪ Row‬ﺃﻭ ﺍﻷﻋﻤﺩﺓ ‪ Column‬ﺃﻭ ﺍﻟﻤﺠﻤﻭﻉ‬ ‫ﺍﻟﻜﻠﻲ ‪ ،Total‬ﻭﻜﺫﻟﻙ ﻴﻤﻜﻥ ﺃﻴﻀﺎ ﺍﺴﺘﺨﺭﺍﺝ ﺍﻷﺨﻁﺎﺀ )ﺍﻟﺒﻭﺍﻗﻲ( ‪ Residuals‬ﺍﻟﺨﺎﻡ‬

‫‪ Unstandardized‬ﺃﻭ ﺍﻟﻤﻌﻴﺎﺭﻴﺔ ‪ Standardized‬ﺃﻭ ﺍﻟﻤﻌﻴﺎﺭﻴﺔ ﺍﻟﻤﻌﺩﻟﺔ ‪Adjusted‬‬

‫‪ ،Standardized‬ﺤﻴﺙ ﺘﻌﺭﻑ ﺍﻷﺨﻁﺎﺀ ﺒﺄﻨﻬﺎ ﺍﻟﻔﺭﻕ ﺒﻴﻥ ﺍﻟﻘﻴﻡ ﺍﻟﻤﺸﺎﻫﺩﺓ ﻭﺍﻟﻘﻴﻡ‬ ‫ﺍﻟﻤﺘﻭﻗﻌﺔ ﻭﻜﻲ ﺘﺼﺒﺢ ﻤﻌﻴﺎﺭﻴﺔ ﺘﻘﺴﻡ ﻋﻠﻰ ﺍﻟﺨﻁﺄ ﺍﻟﻤﻌﻴﺎﺭﻱ ﻟﻠﺒﻴﺎﻨﺎﺕ‪ ،‬ﻭﻴﻤﻜﻨﻨﺎ ﺍﻟﺤﺼﻭل‬

‫ﻋﻠﻰ ﺃﻜﺜﺭ ﻤﻥ ﺨﻴﺎﺭ ﻭﺍﺤﺩ ﻓﻲ ﻫﺫﻩ ﺍﻟﻨﺎﻓﺫﺓ‪.‬‬

‫ﺸﻜل ‪ : 11-4‬ﻨﺎﻓﺫﺓ ﻤﺤﺘﻭﻴﺎﺕ ﺨﻼﻴﺎ ﺍﻟﺠﺩﻭل ‪ Cell Display‬ﺍﻟﻤﺘﻔﺭﻋﺔ ﻤﻥ ﻨﺎﻓﺫﺓ‬ ‫ﺘﻜﻭﻴﻥ ﺠﺩﺍﻭل ﺍﻻﻗﺘﺭﺍﻥ ‪ Crosstabs‬ﻓﻲ ﻗﺎﺘﻤﺔ ﺍﻟﺘﺤﻠﻴل ﺍﻹﺤﺼﺎﺌﻲ ‪.‬‬

‫• ﻓﻲ ﺍﻟﻨﻬﺎﻴﺔ ﺒﺎﻟﻀﻐﻁ ﻋﻠﻰ ﻤﻔﺘﺎﺡ ﺍﻻﺴﺘﻤﺭﺍﺭ ‪ Ok‬ﺴﻴﺘﻡ ﺘﻜﻭﻴﻥ ﺍﻟﺠﺩﻭل ﺍﻟﻤﻁﻠﻭﺏ‬ ‫ﻭﺤﺴﺎﺏ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻟﺘﻲ ﺘﻡ ﺘﺤﺩﻴﺩﻫﺎ ﻭﺘﻅﻬﺭ ﺍﻟﻨﺘﺎﺌﺞ ﻜﻤﺎ ﻓﻲ ﻤﺤﺭﺭ ﺍﻟﻨﺘﺎﺌﺞ ﻜﻤﺎ ﻓﻲ‬

‫ﺍﻟﺸﻜل ‪ 12 -4‬ﺃﺩﻨﺎﻩ‪.‬‬

‫ﻭﻴﺠﺩﺭ ﺒﺎﻟﺫﻜﺭ ﺃﻨﻪ ﻴﻤﻜﻥ ﺘﻁﺒﻴﻕ ﺍﻷﻤﺭ ‪ Crosstabs‬ﻋﻠﻰ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻨﻭﻋﻴﺔ‬

‫ﻭﺍﻟﻅﻭﺍﻫﺭ ﺫﺍﺕ ﻓﺌﺎﺕ ﻤﺤﺩﺩﺓ ﻓﻘﻁ ‪ ،‬ﻭﻻ ﻴﺠﺏ ﺘﻁﺒﻴﻕ ﻫﺫﺍ ﺍﻷﻤﺭ ﻋﻠﻰ ﺍﻟﻅﻭﺍﻫﺭ ﺍﻟﻜﻤﻴﺔ‬

‫ﺍﻟﺘﺭﺘﻴﺒﻴﺔ ﻭﺍﻟﻔﺘﺭﺍﺕ ﻋﻠﻰ ﺍﻹﻁﻼﻕ‪ ،‬ﻫﺫﺍ ﺍﻷﻤﺭ ﺴﻴﺘﻡ ﺘﻭﻀﻴﺤﻪ ﻓﻲ ﺍﻟﺒﻨﺩ ﺍﻟﺘﺎﻟﻲ ‪.‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪151‬‬

‫ﺷﻜﻞ ‪ : 12-4‬ﻧﺘﺎﺋﺞ اﺳﺘﺨﺪام أﻣﺮ ﺗﻜﻮﻳﻦ اﻟﺠﺪاول اﻟﺘﻜﺮارﻳﺔ اﻟﻤﺰدوﺟﺔ‬ ‫‪ Crosstabs‬ﻋﻠﻰ ﺑﻴﺎﻧﺎت ﻣﻠﻒ ‪Employee Data‬‬ ‫)ﻳﺘﺒﻌﻪ ﻓﻲ اﻟﺼﻔﺤﺔ اﻟﻼﺣﻘﺔ(‬

‫‪Crosstabs‬‬ ‫‪Case Processing Summary‬‬ ‫‪Cases‬‬ ‫‪Total‬‬ ‫‪Percent‬‬ ‫‪100.0%‬‬

‫‪Missing‬‬ ‫‪Percent‬‬

‫‪N‬‬ ‫‪474‬‬

‫‪Valid‬‬ ‫‪N‬‬

‫‪0‬‬

‫‪.0%‬‬

‫‪Percent‬‬ ‫‪100.0%‬‬

‫‪N‬‬ ‫‪474‬‬

‫‪Employment‬‬ ‫‪Category * Gender‬‬

‫‪Employment Category * Gender Crosstabulation‬‬ ‫‪Gender‬‬ ‫‪Male‬‬

‫‪Total‬‬

‫‪Female‬‬

‫‪363‬‬

‫‪157‬‬

‫‪206‬‬

‫‪76.6%‬‬

‫‪33.1%‬‬

‫‪43.5%‬‬

‫‪27‬‬

‫‪27‬‬

‫‪5.7%‬‬

‫‪5.7%‬‬

‫‪84‬‬

‫‪74‬‬

‫‪10‬‬

‫‪17.7%‬‬

‫‪15.6%‬‬

‫‪2.1%‬‬

‫‪474‬‬

‫‪258‬‬

‫‪216‬‬

‫‪100.0%‬‬

‫‪54.4%‬‬

‫‪45.6%‬‬

‫‪Count‬‬

‫‪Clerical‬‬

‫‪Total %‬‬ ‫‪Count‬‬

‫‪Employment‬‬ ‫‪Category‬‬

‫‪Custodial‬‬

‫‪Total %‬‬ ‫‪Count‬‬

‫‪Manager‬‬

‫‪Total %‬‬ ‫‪Count‬‬

‫‪Total‬‬

‫‪Total %‬‬

‫• ﻻﺤـﻅ ﺃﻥ ﻫﻨﺎﻙ ﺒﻌﺽ ﺍﻟﻤﻘﺎﻴﻴﺱ ﻤﺎﻻ ﻴﻤﻜﻥ ﺤﺴﺎﺒﻪ ﻤﺜل ﻤﻌﺎﻤﻼﺕ ﺍﻻﺭﺘﺒﺎﻁ ﺒﻜل‬ ‫ﺃﺸﻜﺎﻟﻬﺎ‪ ،‬ﻓﻬﻲ ﻻ ﺘﺤﺴﺏ ﺇﻻ ﻟﻠﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻜﻤﻴﺔ ﻓﻘﻁ ﺒﻴﻨﻤﺎ ﻫﻨﺎﻙ ﻤﺘﻐﻴﺭ ﻋﺭﻑ ﻋﻠﻰ ﺃﻨﻪ‬

‫ﻤﺘﻐﻴـﺭ ﻭﺼﻔﻲ ﻭﻫﻭ ﻤﺘﻐﻴﺭ ﺍﻟﻨﻭﻉ ‪ Gender‬ﺤﻴﺙ ﻴﺄﺨﺫ ﺍﻟﻘﻴﻤﺔ ‪ m‬ﻟﻠﺫﻜﺭ ﻭﺍﻟﻘﻴﻤﺔ ‪f‬‬

‫ﻟﻸﻨﺜﻰ‪.‬‬

‫• ﺘﺎﺒﻊ ﺒﺎﻗﻲ ﻨﺘﺎﺌﺞ ﺍﻷﻤﺭ ﺍﻟﺫﻱ ﺘﻡ ﺘﻨﻔﻴﺫﻩ ﻓﻲ ﺍﻟﺼﻔﺤﺔ ﺍﻟﻼﺤﻘﺔ‪.‬‬

‫( ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬4)

152

Chi-Square Tests

Value Pearson Chi-Square

Asymp. Sig. (2-sided)

df a

79.277

2

.000

95.463

2

.000

Continuity Correction Likelihood Ratio Linear-by-Linear Association N of Valid Cases

474

a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 12.30.

Symmetric Measuresc

Value Nominal by Nominal

Contingency Coefficient

N of Valid Cases

Asymp.

Std. Errora

Approx. Tb Approx. Sig.

.379

.000

474

a. Not assuming the null hypothesis. b. Using the asymptotic standard error assuming the null hypothesis. c. Correlation statistics are available for numeric data only.

300

200

100

Count

Gender Female 0

Male Clerical

Employment Category

Custodial

Manager

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪153‬‬

‫‪ .2 .4 .4‬وﺻﻒ اﻟﺒﻴﺎﻧﺎت اﻟﻜﻤﻴﺔ ‪Describing Numerical Data :‬‬ ‫ﻟﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ ﺍﻟﻜﻤﻴﺔ ﻴﻤﻜﻥ ﺍﺴﺘﺨﺩﺍﻡ ﺍﻷﻭﺍﻤﺭ ﺍﻟﺜﻼﺙ ﺍﻟﺘﺎﻟﻴﺔ‪:‬‬ ‫•‬ ‫•‬ ‫•‬ ‫•‬

‫ﺍﻟﺠﺩﺍﻭل ﺍﻟﺘﻜﺭﺍﺭﻴﺔ ‪Frequencies‬‬

‫ﺤﺴﺎﺏ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ﺍﻟﻭﺼﻔﻴﺔ ‪Descriptives‬‬ ‫ﺤﺴﺎﺏ ﻭﻤﻘﺎﺭﻨﺔ ﺍﻟﻤﺘﻭﺴﻁﺎﺕ ‪Means‬‬ ‫ﺍﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪Explore‬‬

‫ﻓﺎﻷﻤﺭ ﺍﻷﻭل ﺘﻜﻭﻴﻥ ﺍﻟﺠﺩﺍﻭل ﺍﻟﺘﻜﺭﺍﺭﻴﺔ ‪ Frequencies‬ﻭﻫﻭ ﻤﻥ ﺒﻴﻥ ﺃﻭﺍﻤﺭ‬

‫ﺤﺴﺎﺏ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻟﻭﺼﻔﻴﺔ ‪ Descriptive Statistics‬ﻭﻴﻌﻁﻲ ﺠﺩﺍﻭل ﺘﻜﺭﺍﺭﻴﺔ ﺒﺴﻴﻁﺔ‬

‫ﻟﻜل ﻤﺘﻐﻴﺭ ﻋﻠﻰ ﺤﺩﻩ ﻭﺫﻟﻙ ﻤﻬﻤﺎ ﻜﺎﻥ ﻨﻭﻉ ﻫﺫﻩ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺒﻤﺎ ﻓﻲ ﺫﻟﻙ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ‬

‫ﺍﻟﻜﻤﻴﺔ‪ ،‬ﻭﻴﺴﺘﺨﺩﻡ ﻫﺫﺍ ﺍﻷﻤﺭ ﻋﺎﺩﺓ ﻓﻲ ﺤﺎﻟﺔ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻜﻤﻴﺔ ﻟﺤﺴﺎﺏ ﺍﻟﻤﻘﺎﻴﻴﺱ‬ ‫ﺍﻹﺤﺼﺎﺌﻴﺔ ﺍﻟﻤﺨﺘﻠﻔﺔ ﺇﺫ ﺃﻨﻪ ﻴﺘﻀﻤﻥ ﺨﻴﺎﺭﺍﺕ ﺘﺘﻴﺢ ﺤﺴﺎﺏ ﻤﺠﻤﻭﻋﺔ ﻜﺒﻴﺭﺓ ﻤﻥ‬

‫ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ﺒﺎﻹﻀﺎﻓﺔ ﺇﻟﻰ ﺒﻌﺽ ﺍﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﺒﻴﺎﻨﻴﺔ ‪.‬‬

‫ﻭﺒﺎﻟﻤﺜل ﻴﻌﻁﻲ ﺃﻤﺭ ﺤﺴﺎﺏ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ‪ Descriptives‬ﺠﻤﻴﻊ ﻫﺫﻩ‬

‫ﺍﻟﻤﻘﺎﻴﻴﺱ‪ ،‬ﺃﻤﺎ ﺃﻤﺭ ﺤﺴﺎﺏ ﺍﻟﻤﺘﻭﺴﻁﺎﺕ ‪ Means‬ﻓﻬﻭ ﻤﻥ ﺒﻴﻥ ﺃﻭﺍﻤﺭ ﻗﺎﺌﻤﺔ ﺍﻟﻤﻘﺎﺭﻨﺔ‬ ‫ﺒﻴﻥ ﺍﻟﻤﺘﻭﺴﻁﺎﺕ ‪ Compare means‬ﻓﺈﻨﻪ ﻴﻤﻜِﻥ ﻤﻥ ﺘﻘﺴﻴﻡ ﺍﻟﺒﻴﺎﻨﺎﺕ ﺇﻟﻰ ﻁﺒﻘﺎﺕ ﺤﺴﺏ‬

‫ﻓﺌﺎﺕ ﻤﺘﻐﻴﺭ ﺁﺨﺭ ﻤﺜل ﺍﻟﺠﻨﺱ ﺃﻭ ﺍﻟﻭﻅﻴﻔﺔ ﺃﻭ ﻏﻴﺭﻩ ﻭﻴﻤﻜِﻥ ﻤﻥ ﺤﺴﺎﺏ ﺍﻟﻤﺘﻭﺴﻁﺎﺕ‬ ‫ﻭﺍﻻﻨﺤﺭﺍﻑ ﺍﻟﻤﻌﻴﺎﺭﻱ ﻟﻠﻤﺠﺘﻤﻌﺎﺕ ﻭﻟﻠﻁﺒﻘﺎﺕ ﺍﻟﻤﺨﺘﻠﻔﺔ ﺩﺍﺨل ﺍﻟﻤﺠﺘﻤﻌﺎﺕ‪ ،‬ﻭﻴﺤﺘﻭﻱ‬

‫ﻋﻠﻰ ﺨﻴﺎﺭ ﺁﺨﺭ ﻟﺘﻜﻭﻴﻥ ﺠﺩﻭل ﺘﺤﻠﻴل ﺍﻟﺘﺒﺎﻴﻥ ﺒﻬﺩﻑ ﺍﺨﺘﺒﺎﺭ ﻓﺭﻀﻴﺎﺕ ﺘﺘﻌﻠﻕ ﺒﺘﺴﺎﻭﻱ‬ ‫ﻤﺘﻭﺴﻁﺎﺕ ﺍﻟﻁﺒﻘﺎﺕ ﺍﻟﻤﺨﺘﻠﻔﺔ ﻓﻲ ﺍﻟﺒﻴﺎﻨﺎﺕ‪ ،‬ﻭﻴﺠﺏ ﺍﻟﻤﻼﺤﻅﺔ ﻫﻨﺎ ﺃﻥ ﺃﻤﺭ ﺤﺴﺎﺏ‬

‫ﺍﻟﻤﺘﻭﺴﻁﺎﺕ ‪ Means‬ﻻﻴﻤﻜﻥ ﺍﺴﺘﺨﺩﺍﻤﻪ ﺇﻻ ﻓﻲ ﺤﺎﻟﺔ ﻭﺠﻭﺩ ﻁﺒﻘﺎﺕ ﻤﻌﺭﻓﺔ ﻓﻲ‬

‫ﺍﻟﺒﻴﺎﻨﺎﺕ ﻋﻥ ﻁﺭﻴﻕ ﻓﺌﺎﺕ ﻤﺘﻐﻴﺭ ﺁﺨﺭ‪ ،‬ﻭﺇﺫﺍ ﻟﻡ ﻴﻜﻥ ﺍﻷﻤﺭ ﻜﺫﻟﻙ ﻓﺈﻨﻪ ﻴﺠﺏ ﺍﺴﺘﺨﺩﺍﻡ‬

‫ﻻ ﻤﻥ ﺃﻤﺭ ﺤﺴﺎﺏ ﺍﻟﻤﺘﻭﺴﻁﺎﺕ‬ ‫ﺃﻤﺭ ﺤﺴﺎﺏ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ‪ Descriptives‬ﺒﺩ ﹰ‬

‫‪. Means‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪154‬‬

‫ﺃﻤﺎ ﺍﻷﻤﺭ ﺍﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪ Explore‬ﻓﻬﻭ ﻴﻤﻜﻥ ﻤﻥ ﺤﺴﺎﺏ ﻤﺠﻤﻭﻋﺔ ﻜﺒﻴﺭﺓ‬ ‫ﻤﻥ ﺍﻹﺤﺼﺎﺀﺍﺕ ‪ Statistics‬ﺒﺎﻹﻀﺎﻓﺔ ﺇﻟﻰ ﻤﺠﻤﻭﻋﺔ ﻜﺒﻴﺭﺓ ﻤﻥ ﺍﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﺒﻴﺎﻨﻴﺔ ‪.‬‬ ‫ﻭﺴﻨﺘﺤﺩﺙ ﺍﻵﻥ ﻋﻥ ﻜﻴﻔﻴﺔ ﺍﺴﺘﺨﺩﺍﻡ ﻜل ﺃﻤﺭ ﻤﻥ ﻫﺫﻩ ﺍﻷﻭﺍﻤﺭ ﻜل ﻋﻠﻰ ﺤﺩﻩ ‪.‬‬ ‫ﺃﻤﺭ ﺘﻜﻭﻴﻥ ﺍﻟﺠﺩﺍﻭل ﺍﻟﺘﻜﺭﺍﺭﻴﺔ ‪: Frequencies‬‬ ‫ﻭﻴﻤﻜﻥ ﺘﻭﻀﻴﺢ ﻫﺫﺍ ﺍﻷﻤﺭ ﺒﺎﻟﻤﺜﺎل ﺍﻟﺘﺎﻟﻲ ﻋﻠﻰ ﻤﺘﻐﻴﺭ ﻜﻤﻲ ﻤﺘﺼل‬

‫‪ Continuous‬ﺍﻵﻥ ﻭﺫﻟﻙ ﺒﻬﺩﻑ ﺭﺴﻡ ﺍﻟﻤﺩﺭﺝ ﺍﻟﺘﻜﺭﺍﺭﻱ ﺍﻟﺫﻱ ﻴﺭﺴﻡ ﻓﻘﻁ ﻟﻠﻤﺘﻐﻴﺭﺍﺕ‬

‫ﺍﻟﻜﻤﻴﺔ ﺍﻟﻤﺘﺼﻠﺔ ﺒﺎﻹﻀﺎﻓﺔ ﺇﻟﻰ ﺤﺴﺎﺏ ﺒﻌﺽ ﺍﻟﻤﺌﻴﻨﻴﺎﺕ‪ ،‬ﻭﺴﻨﺄﺨﺫ ﺒﺎﻟﺘﺤﺩﻴﺩ ﺍﻟﻤﺘﻐﻴﺭ‬

‫ﺍﻟﻜﻤﻲ ﺍﻟﻤﺘﺼل ‪ prevexp‬ﺍﻟﺫﻱ ﻴﻤﺜل ﺍﻟﺨﺒﺭﺓ ﺍﻟﺴﺎﺒﻘﺔ ﺒﺎﻟﺸﻬﺭ ‪Previous experience‬‬

‫)‪ (months‬ﻓﻲ ﻤﻠﻑ ‪ Employee data‬ﺍﻟﻤﺸﺎﺭ ﺇﻟﻴﻪ ﺴﺎﺒﻘﹰﺎ ‪ ،‬ﻭﻟﺫﻟﻙ ﻓﺈﻨﻨﺎ ﻨﺘﺒﻊ ﻤﺎ ﻴﻠﻲ‪:‬‬

‫• ﺍﻀﻐﻁ ﺒﺎﻟﻔﺎﺭﺓ ﻋﻠﻰ ‪ Frequencies‬ﻓﻲ ﻗﺎﺌﻤﺔ ﺍﻹﺤﺼﺎﺀﺍﺕ ﺍﻟﻭﺼﻔﻴﺔ‬

‫‪) Descriptive Statistics‬ﺃﻭﻗﺎﺌﻤﺔ ﺘﻠﺨﻴﺹ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪ Summarize‬ﻓﻲ ﺇﺼﺩﺍﺭ ‪(8.0‬‬ ‫ﻓﺘﻔﺘﺢ ﻨﺎﻓﺫﺓ ﺍﻟﺠﺩﺍﻭل ﺍﻟﺘﻜﺭﺍﺭﻴﺔ ‪ Frequencies‬ﻜﻤﺎ ﻓﻲ ﺍﻟﺸﻜل ‪. 13-4‬‬

‫•‬

‫ﻓﻲ ﻨﺎﻓﺫﺓ ﺤﻭﺍﺭ ﺍﻟﺠﺩﺍﻭل ﺍﻟﺘﻜﺭﺍﺭﻴﺔ ‪ Frequencies‬ﺃﺩﺨل ﺍﺴﻡ ﺍﻟﻤﺘﻐﻴﺭ ‪Previous‬‬

‫)‪ experience (prevexp‬ﻓﻲ ﻤﺭﺒﻊ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ‪ Variables‬ﻓﻲ ﺍﻟﺠﺯﺀ ﺍﻷﻴﻤﻥ ﻤﻥ‬ ‫ﺍﻟﻨﺎﻓﺫﺓ ﺒﺈﺯﺍﺤﺘﻪ ﺒﺎﻟﺴﻬﻡ ﻤﻥ ﺍﻟﺠﺯﺀ ﺍﻷﻴﺴﺭ ﻜﻤﺎ ﻓﻲ ﺍﻟﺸﻜل ‪ ،13-4‬ﻭﻴﺠﺩﺭ ﺒﺎﻟﺫﻜﺭ ﺃﻥ‬

‫ﻴﻤﻜﻥ ﺍﺨﺘﻴﺎﺭ ﺃﻱ ﻋﺩﺩ ﻤﻥ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﻟﺘﻨﻔﻴﺫ ﻫﺫﺍ ﺍﻷﻤﺭ ﻋﻠﻴﻬﻡ ﺒﻨﻔﺱ ﺍﻟﻁﺭﻴﻘﺔ‪.‬‬

‫• ﻓﻲ ﺃﺴﻔل ﺍﻟﻨﺎﻓﺫﺓ ﺍﻟﻤﺸﺎﺭ ﺇﻟﻴﻬﺎ ﻗﻡ ﺒﺈﻟﻐﺎﺀ ﺍﺨﺘﻴﺎﺭ ﻋﺭﺽ ﺍﻟﺠﺩﺍﻭل ﺍﻟﺘﻜﺭﺍﺭﻴﺔ‬

‫‪ Display frequency tables‬ﻨﻅﺭﹰﺍ ﻟﻌﺩﻡ ﺠﺩﻭﺍﻫﺎ ﻓﻲ ﺤﺎﻟﺔ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻜﻤﻴﺔ‬ ‫ﺍﻟﻤﺘﺼﻠﺔ ﻭﻴﺴﺘﺨﺩﻡ ﻓﻘﻁ ﻓﻲ ﺤﺎﻟﺔ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﺼﻨﻔﺔ ﺒﺎﺴﺘﺨﺩﺍﻡ ﺍﻷﻤﺭ ‪.Recode‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪155‬‬

‫ﺸﻜل ‪ : 13-4‬ﻨﺎﻓﺫﺓ ﺍﻟﺠﺩﺍﻭل ﺍﻟﺘﻜﺭﺍﺭﻴﺔ ‪ Frequencies‬ﻓﻲ ﻗﺎﺌﻤﺔ ﺍﻹﺤﺼﺎﺀﺍﺕ ﺍﻟﻭﺼﻔﻴﺔ‬ ‫‪ ) Descriptive Statistics‬ﺃﻭ ﻗﺎﺌﻤﺔ ﺘﻠﺨﻴﺹ ‪ Summarize‬ﻓﻲ ﺇﺼﺩﺍﺭ ‪. (8.0‬‬

‫• ﻓﻲ ﻨﻔﺱ ﺍﻟﻨﺎﻓﺫﺓ ﻭﻓﻲ ﺃﺴﻔﻠﻬﺎ ﺍﺨﺘﺭ ﻤﺭﺒﻊ ﺍﻟﺤﻭﺍﺭ ﺍﻟﺨﺎﺹ ﺒﺎﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﺒﻴﺎﻨﻴﺔ‬

‫‪ Charts‬ﻟﻴﺘﻡ ﻓﺘﺢ ﻨﺎﻓﺫﺓ ﺍﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﺒﻴﺎﻨﻴﺔ ‪ Charts‬ﻜﻤﺎ ﻓﻲ ﺍﻟﺸﻜل ‪ ، 14-4‬ﻭﺍﺨﺘﺭ‬ ‫ﺸﻜل ﺍﻟﻤﺩﺭﺝ ﺍﻟﺘﻜﺭﺍﺭﻱ ‪ Histogram‬ﻤﻥ ﺍﻟﻨﺎﻓﺫﺓ ﺍﻟﺠﺩﻴﺩﺓ‪ ،‬ﻭﺫﻟﻙ ﻷﻨﻪ ﺍﻟﺸﻜل ﺍﻟﻤﻨﺎﺴﺏ‬ ‫ﻟﻠﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻜﻤﻴﺔ‪ ،‬ﺜﻡ ﺍﺨﺘﺭ ﻜﺫﻟﻙ ﺭﺴﻡ ﺍﻟﻤﻨﺤﻨﻰ ﺍﻟﻁﺒﻴﻌﻲ ‪. With Normal Curve‬‬ ‫ﺸﻜل ‪ : 14-4‬ﻨﺎﻓﺫﺓ ﺍﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﺒﻴﺎﻨﻴﺔ ‪ Charts‬ﻓﻲ ﻨﺎﻓﺫﺓ ﺍﻟﺠﺩﺍﻭل ﺍﻟﺘﻜﺭﺍﺭﻴﺔ‪.‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪156‬‬

‫• ﻓﻲ ﻨﺎﻓﺫﺓ ﺍﻟﺠﺩﺍﻭل ﺍﻟﺘﻜﺭﺍﺭﻴﺔ ‪ Frequencies‬ﺍﻟﺴﺎﺒﻘﺔ ﻭﻓﻲ ﺃﺴﻔﻠﻬﺎ ﺍﺨﺘﺭ ﻤﺭﺒﻊ‬ ‫ﺍﻟﺤﻭﺍﺭ ﺍﻟﺨﺎﺹ ﺒﺎﺨﺘﻴﺎﺭ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ‪ Statistics‬ﻟﻴﺘﻡ ﻓﺘﺢ ﻨﺎﻓﺫﺓ ﺍﻟﻤﻘﺎﻴﻴﺱ‬

‫ﺍﻹﺤﺼﺎﺌﻴﺔ ‪ Statistics‬ﻜﻤﺎ ﻓﻲ ﺍﻟﺸﻜل ‪ ، 15-4‬ﻭﺘﻅﻬﺭ ﺍﻟﻨﺎﻓﺫﺓ ﻓﻲ ﺍﻟﺸﻜل ﺒﺠﻤﻴﻊ‬ ‫ﺍﻟﺨﻴﺎﺭﺍﺕ ﺍﻟﻤﻁﻠﻭﺒﺔ ‪.‬‬

‫• ﻤﻥ ﺒﻴﻥ ﻤﻘﺎﻴﻴﺱ ﺍﻟﻨﺯﻋﺔ ﺍﻟﻤﺭﻜﺯﻴﺔ )ﺍﻟﻤﺘﻭﺴﻁﺎﺕ( ‪ Central Tendency‬ﺍﺨﺘﺭ‬ ‫ﺍﻟﻤﺘﻭﺴﻁ ﺍﻟﺤﺴﺎﺒﻲ ‪ mean‬ﻭﻜﺫﻟﻙ ﺍﻟﻭﺴﻴﻁ ‪ Median‬ﻓﻲ ﺍﻟﻨﺎﻓﺫﺓ ﺍﻟﺠﺩﻴﺩﺓ ‪.‬‬

‫•‬

‫ﻤﻥ ﺒﻴﻥ ﻤﻘﺎﻴﻴﺱ ﺍﻟﺘﺸﺘﺕ ‪ Dispersion‬ﺍﺨﺘﺭ ﺍﻻﻨﺤﺭﺍﻑ ﺍﻟﻤﻌﻴﺎﺭﻱ ‪Standard‬‬

‫‪ Deviation‬ﻭﻜﺫﻟﻙ ﺍﻟﻘﻴﻤﺘﻴﻥ ﺍﻟﺼﻐﺭﻯ ‪ Minimum‬ﻭﺍﻟﻌﻅﻤﻰ ‪ Maximum‬ﻓﻲ ﻨﻔﺱ‬ ‫ﺍﻟﻨﺎﻓﺫﺓ ‪.‬‬

‫• ﻤﻥ ﺒﻴﻥ ﺍﻟﻤﺌﻴﻨﻴﺎﺕ ‪ Percentile Values‬ﺍﺨﺘﺭ ﺍﻟﺭﺒﻴﻌﻴﺎﺕ ‪ Quartiles‬ﻭﻜﺫﻟﻙ‬ ‫ﺍﻟﻤﺌﻴﻨﻴﺎﺕ ‪ ، Percentiles‬ﻭﻴﻤﻜﻨﻙ ﻫﻨﺎ ﺍﺨﺘﻴﺎﺭ ﺃﻱ ﻋﺩﺩ ﻤﻥ ﺍﻟﻤﺌﻴﻨﻴﺎﺕ ‪ ،‬ﻭﻟﻜﻥ ﻓﻲ ﻫﺫﺍ‬

‫ﺍﻟﻤﺜﺎل ﺴﻨﺨﺘﺎﺭ ﺍﻟﻘﻴﻤﺔ ﺍﻟﺘﻲ ﻴﻘل ﻋﻨﻬﺎ ‪ 90%‬ﻤﻥ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪ ،‬ﻓﻘﻡ ﺒﻜﺘﺎﺒﺔ ‪ 90‬ﻓﻲ ﺍﻟﻤﺭﺒﻊ‬ ‫ﺍﻟﺼﻐﻴﺭ ﺍﻟﻤﻘﺎﺒل ﺜﻡ ﺍﻀﻐﻁ ﻋﻠﻰ ﺍﻷﻤﺭ ﺃﻀﻑ ‪ Add‬ﻹﺯﺍﺤﺘﻪ ﺇﻟﻰ ﺍﻟﻤﺭﺒﻊ ﺍﻷﻜﺒﺭ‪.‬‬

‫ﺸﻜل ‪ : 15-4‬ﻨﺎﻓﺫﺓ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ‪ Statistics‬ﻀﻤﻥ ﻨﺎﻓﺫﺓ ﺍﻟﺠﺩﺍﻭل ﺍﻟﺘﻜﺭﺍﺭﻴﺔ‪.‬‬

‫( ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬4)

157

Frequencies ‫ ﻧﺘﺎﺋﺞ اﺳﺘﺨﺪام أﻣﺮ ﺗﻜﻮﻳﻦ اﻟﺠﺪاول اﻟﺘﻜﺮارﻳﺔ‬: 16-4 ‫ﺷﻜﻞ‬ Employee Data ‫ﻋﻠﻰ ﺑﻴﺎﻧﺎت ﻣﻠﻒ‬ Frequencies Statistics Previous Experience (months) N

Valid Missing

474 0

Mean

95.86

Median

55.00

Std. Deviation

104.59

Minimum

0

Maximum

476

Percentiles

100

25

19.00

50

55.00

75

140.00

90

262.50

Previous Experience ( )

80

60

40

Fr eq ue nc y

20

Std. Dev = 104.59 Mean = 95.9 N = 474.00

0

0.0 40. 80. 12 16 20 24 28 32 36 40 44 48 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Previous Experience (months)

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪158‬‬

‫• ﻓﻲ ﺍﻟﻨﻬﺎﻴﺔ ﺍﻀﻐﻁ ﻋﻠﻰ ﻤﺭﺒﻊ ﺍﻻﺴﺘﻤﺭﺍﺭ ‪ Continue‬ﻟﻠﺨﺭﻭﺝ ﻤﻥ ﻨﺎﻓﺫﺓ ﺍﻟﻤﻘﺎﻴﻴﺱ‬ ‫ﺍﻹﺤﺼﺎﺌﻴﺔ ‪ Statistics‬ﻭﺍﻟﻌﻭﺩﺓ ﺇﻟﻰ ﻨﺎﻓﺫﺓ ﺍﻟﺠﺩﺍﻭل ﺍﻟﺘﻜﺭﺍﺭﻴﺔ ‪ Frequencies‬ﻭﺘﺄﻜﺩ‬

‫ﻤﻥ ﺍﻨﻪ ﻗﺩ ﺘﻡ ﺇﻟﻐﺎﺀ ﺍﺨﺘﻴﺎﺭ ﻋﺭﺽ ﺍﻟﺠﺩﺍﻭل ﺍﻟﺘﻜﺭﺍﺭﻴﺔ ﻓﻲ ﻫﺫﺍ ﺍﻟﻤﺭﺒﻊ ﺤﻴﺙ ﺃﻥ ﺘﻜﻭﻴﻥ‬

‫ﻤﺜل ﻫﺫﻩ ﺍﻟﺠﺩﺍﻭل ﻻ ﻴﻤﻜﻥ ﺃﻥ ﻴﺘﻡ ﺒﺼﻭﺭﺓ ﺼﺤﻴﺤﺔ ﻋﻠﻰ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻜﻤﻴﺔ ﺍﻟﻤﺘﺼﻠﺔ‬

‫ﺇﻻ ﺒﻌﺩ ﺘﻘﺴﻴﻡ ﻤﺩﻯ ﺘﻠﻙ ﺍﻟﻤﺘﻐﻴﺭ ﺇﻟﻰ ﻓﺌﺎﺕ ﺒﺎﺴﺘﺨﺩﺍﻡ ﺃﻤﺭ ﺍﻟﺘﺼﻨﻴﻑ ‪. Recode‬‬ ‫•‬

‫ﺍﻀﻐﻁ ﺍﻵﻥ ﻋﻠﻰ ﻤﺭﺒﻊ ﺍﻹﻨﻬﺎﺀ ‪ Ok‬ﻓﻲ ﻨﺎﻓﺫﺓ ﺍﻟﺠﺩﺍﻭل ﺍﻟﺘﻜﺭﺍﺭﻴﺔ ‪Frequencies‬‬

‫ﻟﻴﺘﻡ ﺘﻨﻔﻴﺫ ﺍﻷﻤﺭ ﻭﺘﻅﻬﺭ ﺍﻟﻨﺘﺎﺌﺞ ﻜﻤﺎ ﻓﻲ ﺍﻟﺸﻜل ‪ 16-4‬ﺃﻋﻼﻩ ‪.‬‬

‫ﺃﻤﺭ ﺤﺴﺎﺏ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ﺍﻟﻭﺼﻔﻴﺔ ‪: Descriptives‬‬

‫ﻴﺴﺘﺨﺩﻡ ﺃﻤﺭ ﺤﺴﺎﺏ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ‪ Descriptives‬ﻋﺎﺩﺓ ﻓﻲ ﺤﺎﻟﺔ‬

‫ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻜﻤﻴﺔ ﻓﻘﻁ ﻭﺫﻟﻙ ﻟﺤﺴﺎﺏ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ﺍﻟﻤﺨﺘﻠﻔﺔ‪ ،‬ﻓﻬﻭ ﻴﺘﻀﻤﻥ‬ ‫ﺨﻴﺎﺭﺍﺕ ﺘﺘﻴﺢ ﺤﺴﺎﺏ ﻤﺠﻤﻭﻋﺔ ﻜﺒﻴﺭﺓ ﻤﻥ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ﺒﺎﻹﻀﺎﻓﺔ ﺇﻟﻰ ﺒﻌﺽ‬

‫ﺍﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﺒﻴﺎﻨﻴﺔ‪ ،‬ﻭﻴﻤﻜﻥ ﺘﻭﻀﻴﺢ ﻫﺫﺍ ﺍﻷﻤﺭ ﺒﺎﻟﻤﺜﺎل ﺍﻟﺘﺎﻟﻲ ﻋﻠﻰ ﻨﻔﺱ ﺍﻟﻤﺘﻐﻴﺭ‬

‫ﺍﻟﻜﻤﻲ ﺍﻟﻤﺘﺼل ‪ Continuous‬ﺍﻟﺴﺎﺒﻕ ﺍﻵﻥ ﻭﺫﻟﻙ ﺒﻬﺩﻑ ﺤﺴﺎﺏ ﺒﻌﺽ ﺍﻟﻤﻘﺎﻴﻴﺱ‬ ‫ﺍﻹﺤﺼﺎﺌﻴﺔ‪ ،‬ﻭﺴﻨﺄﺨﺫ ﺒﺎﻟﺘﺤﺩﻴﺩ ﺍﻟﻤﺘﻐﻴﺭ ﺍﻟﻜﻤﻲ ﺍﻟﻤﺘﺼل ‪ prevexp‬ﺍﻟﺫﻱ ﻴﻤﺜل ﺍﻟﺨﺒﺭﺓ‬

‫ﺍﻟﺴﺎﺒﻘﺔ ﺒﺎﻟﺸﻬﺭ )‪ Previous experience (months‬ﻓﻲ ﻤﻠﻑ ‪Employee data‬‬

‫ﺍﻟﻤﺸﺎﺭ ﺇﻟﻴﻪ ﺴﺎﺒﻘﹰﺎ ‪ ،‬ﻭﻟﺫﻟﻙ ﻓﺈﻨﻨﺎ ﻨﺘﺒﻊ ﻤﺎ ﻴﻠﻲ‪:‬‬

‫• ﺍﻀﻐﻁ ﺒﺎﻟﻔﺎﺭﺓ ﻋﻠﻰ ‪ Descriptives‬ﻓﻲ ﻗﺎﺌﻤﺔ ﺍﻹﺤﺼﺎﺀﺍﺕ ﺍﻟﻭﺼﻔﻴﺔ‬

‫‪) Descriptive Statistics‬ﻗﺎﺌﻤﺔ ﺘﻠﺨﻴﺹ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪ Summarize‬ﻓﻲ ﺇﺼﺩﺍﺭ ‪(8.0‬‬ ‫ﻓﺘﻔﺘﺢ ﻨﺎﻓﺫﺓ ﺤﺴﺎﺏ ﺍﻹﺤﺼﺎﺀﺍﺕ ﺍﻟﻭﺼﻔﻴﺔ ‪ Descriptives‬ﻜﻤﺎ ﻓﻲ ﺍﻟﺸﻜل ‪. 17-4‬‬

‫• ﻓﻲ ﻨﺎﻓﺫﺓ ﺤﻭﺍﺭ ﺍﻹﺤﺼﺎﺀﺍﺕ ﺍﻟﻭﺼﻔﻴﺔ ‪ Descriptives‬ﺃﺩﺨل ﺍﺴﻡ ﺍﻟﻤﺘﻐﻴﺭ‬ ‫‪ prevexp‬ﻓﻲ ﻤﺭﺒﻊ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ‪ Variables‬ﻓﻲ ﺍﻟﺠﺯﺀ ﺍﻷﻴﻤﻥ ﻤﻥ ﺍﻟﻨﺎﻓﺫﺓ ﺒﺈﺯﺍﺤﺘﻪ‬ ‫ﺒﺎﻟﺴﻬﻡ ﻤﻥ ﺍﻟﺠﺯﺀ ﺍﻷﻴﺴﺭ ﻜﻤﺎ ﻓﻲ ﺍﻟﺸﻜل ‪ ،17-4‬ﻭﻴﺠﺩﺭ ﺒﺎﻟﺫﻜﺭ ﺃﻨﻪ ﻴﻤﻜﻥ ﺍﺨﺘﻴﺎﺭ ﺃﻱ‬

‫ﻋﺩﺩ ﻤﻥ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﻟﺘﻨﻔﻴﺫ ﻫﺫﺍ ﺍﻷﻤﺭ ﻋﻠﻴﻬﻡ ﺒﻨﻔﺱ ﺍﻟﻁﺭﻴﻘﺔ‪.‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪159‬‬

‫ﺸﻜل ‪ : 17-4‬ﻨﺎﻓﺫﺓ ﺤﺴﺎﺏ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻟﻭﺼﻔﻴﺔ ‪ Descriptives‬ﻓﻲ ﻗﺎﺌﻤﺔ ﺍﻹﺤﺼﺎﺀﺍﺕ‬ ‫ﺍﻟﻭﺼﻔﻴﺔ ‪) Descriptive Statistics‬ﻗﺎﺌﻤﺔ ﺘﻠﺨﻴﺹ ‪ Summarize‬ﻓﻲ ﺇﺼﺩﺍﺭ ‪.(8.0‬‬

‫• ﻓﻲ ﻨﺎﻓﺫﺓ ﺤﻭﺍﺭ ﺍﻹﺤﺼﺎﺀﺍﺕ ﺍﻟﻭﺼﻔﻴﺔ ‪ Descriptives‬ﺍﻟﺴﺎﺒﻘﺔ ﻭﻓﻲ ﺃﺴﻔﻠﻬﺎ ﺍﺨﺘﺭ‬

‫ﻤﺭﺒﻊ ﺍﻟﺤﻭﺍﺭ ‪ Options‬ﺍﻟﺨﺎﺹ ﺒﺎﺨﺘﻴﺎﺭ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ﻟﻴﺘﻡ ﻓﺘﺢ ﻨﺎﻓﺫﺓ ﺍﺨﺘﻴﺎﺭ‬

‫ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ‪ Options‬ﻜﻤﺎ ﻓﻲ ﺍﻟﺸﻜل ‪ ، 18-4‬ﻭﺘﻅﻬﺭ ﺍﻟﻨﺎﻓﺫﺓ ﻓﻲ ﺍﻟﺸﻜل‬

‫ﺒﺠﻤﻴﻊ ﺍﻟﺨﻴﺎﺭﺍﺕ ﺍﻟﻤﻁﻠﻭﺒﺔ ‪.‬‬

‫• ﻤﻥ ﺒﻴﻥ ﻤﻘﺎﻴﻴﺱ ﺍﻟﻨﺯﻋﺔ ﺍﻟﻤﺭﻜﺯﻴﺔ )ﺍﻟﻤﺘﻭﺴﻁﺎﺕ( ‪ Central Tendency‬ﺍﺨﺘﺭ‬

‫ﺍﻟﻤﺘﻭﺴﻁ ﺍﻟﺤﺴﺎﺒﻲ ‪ Mean‬ﻓﻲ ﺍﻟﻨﺎﻓﺫﺓ ﺍﻟﺠﺩﻴﺩﺓ ‪.‬‬ ‫•‬

‫ﻤﻥ ﺒﻴﻥ ﻤﻘﺎﻴﻴﺱ ﺍﻟﺘﺸﺘﺕ ‪ Dispersion‬ﺍﺨﺘﺭ ﺍﻻﻨﺤﺭﺍﻑ ﺍﻟﻤﻌﻴﺎﺭﻱ ‪Standard‬‬

‫‪ Deviation‬ﻭﻜﺫﻟﻙ ﺍﻟﻘﻴﻤﺘﻴﻥ ﺍﻟﺼﻐﺭﻯ ‪ Minimum‬ﻭﺍﻟﻌﻅﻤﻰ ‪ Maximum‬ﻓﻲ ﻨﻔﺱ‬ ‫ﺍﻟﻨﺎﻓﺫﺓ ‪.‬‬

‫• ﻤﻥ ﺒﻴﻥ ﺨﻴﺎﺭﺍﺕ ﺘﺭﺘﻴﺏ ﻋﺭﺽ ﺍﻟﻨﺘﺎﺌﺞ ‪ Display Order‬ﺍﺨﺘﺭ ﻗﺎﺌﻤﺔ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ‬

‫‪ ، Variable List‬ﻭﻫﺫﺍ ﺍﻟﺨﻴﺎﺭ ﻟﻪ ﺃﻫﻤﻴﺔ ﻓﻘﻁ ﻋﻨﺩﻤﺎ ﻴﻜﻭﻥ ﻫﻨﺎﻙ ﻓﻲ ﻗﺎﺌﻤﺔ ﺃﺴﻤﺎﺀ‬

‫ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﺭﺍﺩ ﺤﺴﺎﺏ ﻤﻘﺎﻴﻴﺴﻬﺎ ﻋﺩﺩ ﻜﺒﻴﺭ ﻤﻨﻬﺎ ﻓﻴﻘﻭﻡ ﺍﻟﻨﻅﺎﻡ ﺒﺘﺭﺘﻴﺏ ﺍﻟﻨﺘﺎﺌﺞ‬ ‫ﺒﺎﻟﺸﻜل ﺍﻟﺫﻱ ﺘﺭﻴﺩﻩ‪ ،‬ﻓﻴﻤﻜﻨﻙ ﺘﺭﺘﻴﺏ ﻅﻬﻭﺭ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﻜﻤﺎ ﻭﻀﻌﺕ ﻓﻲ ﺍﻟﻘﺎﺌﻤﺔ ﺃﻭ‬ ‫ﺃﺒﺠﺩﻴﹰﺎ ﺃﻭ ﺘﺭﺘﻴﺏ ﺘﺼﺎﻋﺩﻱ ﺃﻭ ﺘﻨﺎﺯﻟﻲ ﺤﺴﺏ ﻗﻴﻡ ﻤﺘﻭﺴﻁﺎﺘﻬﺎ ﺍﻟﺤﺴﺎﺒﻴﺔ‪.‬‬

‫( ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬4)

160

‫ ﻓﻲ ﻨﺎﻓﺫﺓ ﺤﺴﺎﺏ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻟﻭﺼﻔﻴﺔ‬Options ‫ ﻨﺎﻓﺫﺓ ﺨﻴﺎﺭﺍﺕ ﺍﻟﻤﻘﺎﻴﻴﺱ‬: 18-4 ‫ﺸﻜل‬ . Descriptive Statistics ‫ ﻓﻲ ﻗﺎﺌﻤﺔ ﺍﻹﺤﺼﺎﺀﺍﺕ ﺍﻟﻭﺼﻔﻴﺔ‬Descriptives

Descriptives ‫ ﻧﺘﺎﺋﺞ اﺳﺘﺨﺪام أﻣﺮ ﺣﺴﺎب اﻟﻤﻘﺎﻳﻴﺲ اﻹﺣﺼﺎﺋﻴﺔ‬: 19-4 ‫ﺷﻜﻞ‬ Employee Data ‫ﻋﻠﻰ ﺑﻴﺎﻧﺎت ﻣﻠﻒ‬ Descriptives Descriptive Statistics N Months since Hire

474

Valid N (listwise)

474

Minimum

Maximum

63

98

Mean 81.11

Std. Deviation 10.06

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪161‬‬

‫• ﻓﻲ ﺍﻟﻨﻬﺎﻴﺔ ﺍﻀﻐﻁ ﻋﻠﻰ ﻤﺭﺒﻊ ﺍﻻﺴﺘﻤﺭﺍﺭ ‪ Continue‬ﻟﻠﺨﺭﻭﺝ ﻤﻥ ﻨﺎﻓﺫﺓ ﺨﻴﺎﺭﺍﺕ‬ ‫ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ‪ Options‬ﻭﺍﻟﻌﻭﺩﺓ ﺇﻟﻰ ﻨﺎﻓﺫﺓ ﺤﻭﺍﺭ ﺍﻹﺤﺼﺎﺀﺍﺕ ﺍﻟﻭﺼﻔﻴﺔ‬

‫‪ Descriptives‬ﻭﻤﻥ ﺜﻡ ﺍﻀﻐﻁ ﻋﻠﻰ ﻤﺭﺒﻊ ﺍﻹﻨﻬﺎﺀ ‪ Ok‬ﻟﻴﺘﻡ ﺘﻨﻔﻴﺫ ﺍﻷﻤﺭ ﻭﺘﻅﻬﺭ‬ ‫ﺍﻟﻨﺘﺎﺌﺞ ﻜﻤﺎ ﻓﻲ ﺍﻟﺸﻜل ‪ 19-4‬ﺃﻋﻼﻩ ‪.‬‬

‫ﺃﻤﺭ ﺤﺴﺎﺏ ﺍﻟﻤﺘﻭﺴﻁﺎﺕ ‪: Means‬‬ ‫ﻭﻫﺫﺍ ﺍﻷﻤﺭ ﺒﺨﻼﻑ ﺃﻭﺍﻤﺭ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ ﺍﻷﺨﺭﻯ ﻴﺄﺘﻲ ﻀﻤﻥ ﻗﺎﺌﻤﺔ ﺃﻭﺍﻤﺭ‬

‫ﺍﻟﻤﻘﺎﺭﻨﺔ ﺒﻴﻥ ﺍﻟﻤﺘﻭﺴﻁﺎﺕ ‪ Compare Means‬ﻭﻟﻴﺱ ﻀﻤﻥ ﻤﺠﻤﻭﻋﺎﺕ ﻭﺼﻑ‬

‫ﻭﺘﻠﺨﻴﺹ ﺍﻟﺒﻴﺎﻨﺎﺕ‪ ،‬ﻭﻴﻬﺩﻑ ﺃﻤﺭ ﺤﺴﺎﺏ ﺍﻟﻤﺘﻭﺴﻁﺎﺕ ‪ Means‬ﺇﻟﻰ ﺤﺴﺎﺏ ﺍﻟﻤﻘﺎﻴﻴﺱ‬

‫ﺍﻟﻭﺼﻔﻴﺔ ﻤﺜل ﺍﻟﻤﺘﻭﺴﻁﺎﺕ ﻭﺍﻻﻨﺤﺭﺍﻓﺎﺕ ﺍﻟﻤﻌﻴﺎﺭﻴﺔ ﻟﻅﺎﻫﺭﺓ ﺃﻭ ﻤﺘﻐﻴﺭ ﻤﺤﺩﺩ ﻭﺫﻟﻙ ﻓﻲ‬ ‫ﺍﻟﻤﺠﺘﻤﻌﺎﺕ ﻭﻓﻲ ﺍﻟﻁﺒﻘﺎﺕ ﺍﻟﻤﺨﺘﻠﻔﺔ ﺩﺍﺨل ﺍﻟﻤﺠﺘﻤﻌﺎﺕ‪ ،‬ﻭﻟﺘﻨﻔﻴﺫ ﻫﺫﺍ ﺍﻷﻤﺭ ﻻﺒﺩ ﻤﻥ‬ ‫ﺘﻘﺴﻴﻡ ﺍﻟﺒﻴﺎﻨﺎﺕ ﺇﻟﻰ ﻁﺒﻘﺎﺕ ﺤﺴﺏ ﻓﺌﺎﺕ ﻤﺘﻐﻴﺭ ﻨﻭﻋﻲ ﺁﺨﺭ ﻤﺜل ﺍﻟﺠﻨﺱ ﺃﻭ ﺍﻟﻭﻅﻴﻔﺔ ﺃﻭ‬

‫ﻏﻴﺭﻩ‪ ،‬ﻭﻴﺤﺘﻭﻱ ﻫﺫﺍ ﺍﻷﻤﺭ ﻋﻠﻰ ﺨﻴﺎﺭ ﺁﺨﺭ ﻟﺘﻜﻭﻴﻥ ﺠﺩﻭل ﺘﺤﻠﻴل ﺍﻟﺘﺒﺎﻴﻥ ﺒﻬﺩﻑ ﺍﺨﺘﺒﺎﺭ‬

‫ﻓﺭﻀﻴﺎﺕ ﺘﺘﻌﻠﻕ ﺒﺘﺴﺎﻭﻱ ﻤﺘﻭﺴﻁﺎﺕ ﺍﻟﻁﺒﻘﺎﺕ ﺍﻟﻤﺨﺘﻠﻔﺔ ﻓﻲ ﺍﻟﻤﺠﺘﻤﻊ‪ ،‬ﻭﻴﺠﺏ ﺍﻟﺘﺄﻜﻴﺩ‬

‫ﻋﻠﻰ ﺃﻥ ﺃﻤﺭ ﺤﺴﺎﺏ ﺍﻟﻤﺘﻭﺴﻁﺎﺕ ‪ Means‬ﻻ ﻴﻤﻜﻥ ﺍﺴﺘﺨﺩﺍﻤﻪ ﺇﻻ ﻓﻲ ﺤﺎﻟﺔ ﻭﺠﻭﺩ‬ ‫ﻁﺒﻘﺎﺕ ﻤﻌﺭﻓﺔ ﻓﻲ ﺍﻟﺒﻴﺎﻨﺎﺕ ﻋﻥ ﻁﺭﻴﻕ ﻓﺌﺎﺕ ﻤﺘﻐﻴﺭ ﻨﻭﻋﻲ ﺃﻭ ﻤﺘﻐﻴﺭ ﻜﻤﻲ ﻤﺘﻘﻁﻊ‬

‫ﺁﺨﺭ‪ ،‬ﻭﻟﺘﻨﻔﻴﺫ ﺃﻤﺭ ﺤﺴﺎﺏ ﺍﻟﻤﺘﻭﺴﻁﺎﺕ ‪ Means‬ﺴﻨﺄﺨﺫ ﺍﻟﻤﺘﻐﻴﺭ ﺍﻟﻜﻤﻲ ﺍﻟﻤﺘﺼل‬

‫‪ prevexp‬ﺍﻟﺫﻱ ﻴﻤﺜل ﺍﻟﺨﺒﺭﺓ ﺍﻟﺴﺎﺒﻘﺔ ﺒﺎﻟﺸﻬﺭ )‪ Previous experience (months‬ﻓﻲ‬ ‫ﻤﻠﻑ ‪ Employee data‬ﺍﻟﻤﺸﺎﺭ ﺇﻟﻴﻪ ﺴﺎﺒﻘﹰﺎ ﻭﺴﻨﻬﺩﻑ ﺇﻟﻰ ﺤﺴﺎﺏ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ‬

‫ﻟﻤﺘﻐﻴﺭ ﺍﻟﺨﺒﺭﺓ ﺍﻟﺴﺎﺒﻘﺔ ‪ prevexp‬ﺍﻟﻜﻤﻲ ﻭﺫﻟﻙ ﻓﻲ ﺍﻟﻁﺒﻘﺎﺕ ﺍﻟﻭﻅﻴﻔﻴﺔ ﺍﻟﻤﺨﺘﻠﻔﺔ ﺍﻟﻤﻌﺭﻓﺔ‬ ‫ﻓﻲ ﺍﻟﻤﺘﻐﻴﺭ ﺍﻟﻭﺼﻔﻲ ‪ ، jobcat‬ﻭﻟﺫﻟﻙ ﺴﻨﻘﻭﻡ ﺒﺈﺘﺒﺎﻉ ﺍﻟﺨﻁﻭﺍﺕ ﺍﻟﺘﺎﻟﻴﺔ ‪:‬‬

‫• ﺍﻀﻐﻁ ﺒﺎﻟﻔﺎﺭﺓ ﻋﻠﻰ ﺃﻤﺭ ﺤﺴﺎﺏ ﺍﻟﻤﺘﻭﺴﻁﺎﺕ ‪ Means‬ﻓﻲ ﻗﺎﺌﻤﺔ ﺍﻟﻤﻘﺎﺭﻨﺔ ﺒﻴﻥ‬

‫ﺍﻟﻤﺘﻭﺴﻁﺎﺕ ‪ Compare Means‬ﻓﺘﻔﺘﺢ ﻨﺎﻓﺫﺓ ﺤﺴﺎﺏ ﺍﻟﻤﺘﻭﺴﻁﺎﺕ ‪ Means‬ﻜﻤﺎ ﻓﻲ‬

‫ﺍﻟﺸﻜل ‪. 20-4‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪162‬‬

‫ﺸﻜل ‪ : 20-4‬ﻨﺎﻓﺫﺓ ﺃﻤﺭ ﺤﺴﺎﺏ ﺍﻟﻤﺘﻭﺴﻁﺎﺕ ‪ Means‬ﻓﻲ ﻗﺎﺌﻤﺔ ﺍﻟﻤﻘﺎﺭﻨﺔ ﺒﻴﻥ‬ ‫ﺍﻟﻤﺘﻭﺴﻁﺎﺕ ‪ Compare Means‬ﺩﺍﺨل ﻗﺎﺌﻤﺔ ﺍﻟﺘﺤﻠﻴل ﺍﻹﺤﺼﺎﺌﻲ ﺍﻟﺭﺌﻴﺴﻴﺔ ‪.‬‬

‫• ﻓﻲ ﻨﺎﻓﺫﺓ ﺤﺴﺎﺏ ﺍﻟﻤﺘﻭﺴﻁﺎﺕ ‪ Means‬ﺃﺩﺨل ﺍﺴﻡ ﺍﻟﻤﺘﻐﻴﺭ ‪ prevexp‬ﻓﻲ ﻤﺭﺒﻊ‬ ‫ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺘﺎﺒﻌﺔ ‪ Dependent List‬ﻓﻲ ﺍﻟﺠﺯﺀ ﺍﻷﻴﻤﻥ ﺍﻟﻌﻠﻭﻱ ﻤﻥ ﺍﻟﻨﺎﻓﺫﺓ ﺒﺈﺯﺍﺤﺘﻪ‬

‫ﺒﺎﻟﺴﻬﻡ ﻤﻥ ﺍﻟﺠﺯﺀ ﺍﻷﻴﺴﺭ ﻜﻤﺎ ﻓﻲ ﺍﻟﺸﻜل ‪ ،20-4‬ﻭﻴﺠﺩﺭ ﺒﺎﻟﺫﻜﺭ ﺃﻨﻪ ﻴﻤﻜﻥ ﺍﺨﺘﻴﺎﺭ ﺃﻱ‬

‫ﻋﺩﺩ ﻤﻥ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﻟﺘﻨﻔﻴﺫ ﻫﺫﺍ ﺍﻷﻤﺭ ﻋﻠﻴﻬﻡ ﺒﻨﻔﺱ ﺍﻟﻁﺭﻴﻘﺔ‪.‬‬

‫• ﺃﺩﺨل ﺍﺴﻡ ﻤﺘﻐﻴﺭ ﺍﻟﺘﺼﻨﻴﻑ ‪ jobcat‬ﻓﻲ ﻤﺭﺒﻊ ﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺘﺼﻨﻴﻑ ﺘﺤﺕ ﺍﺴﻡ‬ ‫ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﺴﺘﻘﻠﺔ ‪ Independent List‬ﻓﻲ ﺍﻟﺠﺯﺀ ﺍﻷﻴﻤﻥ ﺍﻟﺴﻔﻠﻲ ﻤﻥ ﺍﻟﻨﺎﻓﺫﺓ‬ ‫ﺒﺈﺯﺍﺤﺘﻪ ﺒﺎﻟﺴﻬﻡ ﻤﻥ ﺍﻟﺠﺯﺀ ﺍﻷﻴﺴﺭ‪.‬‬

‫• ﻓﻲ ﻨﺎﻓﺫﺓ ﺤﺴﺎﺏ ﺍﻟﻤﺘﻭﺴﻁﺎﺕ ‪ Means‬ﺍﻟﺴﺎﺒﻘﺔ ﻭﻓﻲ ﺃﺴﻔﻠﻬﺎ ﺍﺨﺘﺭ ﻤﺭﺒﻊ ﺍﻟﺤﻭﺍﺭ‬ ‫‪ Options‬ﺍﻟﺨﺎﺹ ﺒﺎﺨﺘﻴﺎﺭ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ﻟﻴﺘﻡ ﻓﺘﺢ ﻨﺎﻓﺫﺓ ﺍﺨﺘﻴﺎﺭ ﺍﻟﻤﻘﺎﻴﻴﺱ‬

‫ﺍﻹﺤﺼﺎﺌﻴﺔ ‪ Options‬ﻜﻤﺎ ﻓﻲ ﺍﻟﺸﻜل ‪ ، 21-4‬ﻭﺘﻅﻬﺭ ﺍﻟﻨﺎﻓﺫﺓ ﻓﻲ ﺍﻟﺸﻜل ﺒﺠﻤﻴﻊ‬ ‫ﺍﻟﺨﻴﺎﺭﺍﺕ ﺍﻟﻤﻁﻠﻭﺒﺔ ‪.‬‬

‫• ﻤﻥ ﺒﻴﻥ ﻤﻘﺎﻴﻴﺱ ﺍﻟﻨﺯﻋﺔ ﺍﻟﻤﺭﻜﺯﻴﺔ )ﺍﻟﻤﺘﻭﺴﻁﺎﺕ( ‪ Central Tendency‬ﺍﺨﺘﺭ‬ ‫ﺍﻟﻤﺘﻭﺴﻁ ﺍﻟﺤﺴﺎﺒﻲ ‪ Mean‬ﻭﺍﻟﻭﺴﻴﻁ ‪ ، Median‬ﻭﻤﻥ ﺒﻴﻥ ﻤﻘﺎﻴﻴﺱ ﺍﻟﺘﺸﺘﺕ‬

‫‪ Dispersion‬ﺍﺨﺘﺭ ﺍﻻﻨﺤﺭﺍﻑ ﺍﻟﻤﻌﻴﺎﺭﻱ ‪ ، Standard deviation‬ﺜﻡ ﺍﺨﺘﺭ ﻤﻘﻴﺎﺴﻲ‬ ‫ﺍﻻﻟﺘﻭﺍﺀ ‪ Skewness‬ﻭﺍﻟﺘﻔﻠﻁﺢ ‪ ، Kurtosis‬ﻜﻤﺎ ﻭﻴﻤﻜﻥ ﺘﻜﻭﻴﻥ ﺠﺩﻭل ﺘﺤﻠﻴل ﺍﻟﺘﺒﺎﻴﻥ‬ ‫ﻟﻠﻤﻘﺎﺭﻨﺔ ﺒﻴﻥ ﻤﺘﻭﺴﻁﺎﺕ ﺍﻟﻁﺒﻘﺎﺕ ﻭﺫﻟﻙ ﻓﻲ ﻨﺎﻓﺫﺓ ﺍﻟﺨﻴﺎﺭﺍﺕ ‪ Options‬ﺍﻟﺘﻲ ﺘﻡ ﻓﺘﺤﻬﺎ‪.‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪163‬‬

‫ﺸﻜل ‪ : 21-4‬ﻨﺎﻓﺫﺓ ﺍﻟﺨﻴﺎﺭﺍﺕ ‪ Options‬ﻀﻤﻥ ﻨﺎﻓﺫﺓ ﺤﺴﺎﺏ ﺍﻟﻤﺘﻭﺴﻁﺎﺕ ‪. Means‬‬

‫• ﺍﻀﻐﻁ ﻋﻠﻰ ﻤﻔﺘﺎﺡ ﺍﻻﺴﺘﻤﺭﺍﺭ ‪ Continue‬ﻓﻲ ﻨﺎﻓﺫﺓ ﺍﻟﺨﻴﺎﺭﺍﺕ ‪ Options‬ﻹﻏﻼﻕ‬ ‫ﺍﻟﻨﺎﻓﺫﺓ ﻭﺍﻟﻌﻭﺩﺓ ﺇﻟﻰ ﻨﺎﻓﺫﺓ ﺤﺴﺎﺏ ﺍﻟﻤﺘﻭﺴﻁﺎﺕ ‪ ، Means‬ﻭﻟﺘﻨﻔﻴﺫ ﺍﻟﻤﻬﻤﺔ ﺇﻀﻐﻁ ﻋﻠﻰ‬

‫ﻤﻔﺘﺎﺡ ﺍﻟﺘﻨﻔﻴﺫ ‪ Ok‬ﻓﻲ ﻨﺎﻓﺫﺓ ﺤﺴﺎﺏ ﺍﻟﻤﺘﻭﺴﻁﺎﺕ ‪ Means‬ﻟﻴﺘﻡ ﺘﻨﻔﻴﺫ ﺍﻷﻤﺭ ﻭﻅﻬﻭﺭ‬

‫ﺍﻟﻨﺘﺎﺌﺞ ﻜﻤﺎ ﻓﻲ ﺍﻟﺸﻜل ‪. 22-4‬‬

‫ﺷﻜﻞ ‪ : 22-4‬ﻧﺘﺎﺋﺞ اﺳﺘﺨﺪام أﻣﺮ ﺣﺴﺎب اﻟﻤﺘﻮﺳﻄﺎت ‪Means‬‬

‫ﻋﻠﻰ ﺑﻴﺎﻧﺎت ﻣﻠﻒ ‪Employee Data‬‬ ‫‪Means‬‬ ‫ﺗﻘﺮﻳﺮ أﻋﺪاد اﻟﻤﺸﺎهﺪات ‪:‬‬ ‫‪Case Processing Summary‬‬ ‫‪Cases‬‬ ‫‪Total‬‬ ‫‪Percent‬‬ ‫‪100.0%‬‬

‫‪Excluded‬‬ ‫‪N‬‬

‫‪474‬‬

‫‪Percent‬‬ ‫‪.0%‬‬

‫‪Included‬‬ ‫‪N‬‬

‫‪0‬‬

‫‪Percent‬‬ ‫‪100.0%‬‬

‫‪N‬‬ ‫‪474‬‬

‫‪Previous Experience‬‬ ‫* )‪(months‬‬ ‫‪Employment Category‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪164‬‬

‫اﻟﻤﻘﺎﻳﻴﺲ اﻹﺣﺼﺎﺋﻴﺔ اﻟﻤﻄﻠﻮﺑﺔ ﻣﺤﺴﻮﺑﺔ ﻟﻜﻞ ﻃﺒﻘﺔ وﻇﻴﻔﻴﺔ ﻓﻲ اﻟﻤﺠﺘﻤﻊ ﺛﻢ اﻟﻤﺘﻮﺳﻂ اﻟﻌﺎم ‪:‬‬ ‫‪Report‬‬ ‫)‪Previous Experience (months‬‬ ‫‪Kurtosis‬‬

‫‪Mean‬‬

‫‪Median‬‬

‫‪N‬‬

‫‪Skewness‬‬

‫‪Std. Deviation‬‬

‫‪2.461‬‬

‫‪1.667‬‬

‫‪95.27‬‬

‫‪50.00‬‬

‫‪85.04‬‬

‫‪76.6%‬‬

‫‪363‬‬

‫‪% of Total N‬‬

‫‪-1.127‬‬

‫‪.014‬‬

‫‪101.43‬‬

‫‪305.00‬‬

‫‪298.11‬‬

‫‪5.7%‬‬

‫‪27‬‬

‫‪.817‬‬

‫‪1.276‬‬

‫‪73.26‬‬

‫‪52.00‬‬

‫‪77.62‬‬

‫‪17.7%‬‬

‫‪84‬‬

‫‪1.696‬‬

‫‪1.510‬‬

‫‪104.59‬‬

‫‪55.00‬‬

‫‪95.86‬‬

‫‪100.0%‬‬

‫‪474‬‬

‫‪Employment Category‬‬ ‫‪Clerical‬‬ ‫‪Custodial‬‬ ‫‪Manager‬‬ ‫‪Total‬‬

‫ﺟﺪول ﺗﺤﻠﻴﻞ اﻟﺘﺒﺎﻳﻦ ﻟﻠﻤﻘﺎرﻧﺔ ﺑﻴﻦ ﻣﺘﻮﺳﻄﺎت اﻟﻄﺒﻘﺎت اﻟﻮﻇﻴﻔﻴﺔ ‪:‬‬ ‫‪ANOVA Table‬‬

‫‪Sig.‬‬ ‫‪.000‬‬

‫‪F‬‬ ‫‪69.192‬‬

‫‪df‬‬

‫‪Mean Square‬‬

‫‪Sum of‬‬ ‫‪Squares‬‬

‫‪587453.437‬‬

‫‪2‬‬

‫‪1174906.9‬‬

‫‪8490.233‬‬

‫‪471‬‬

‫‪3998899.9‬‬

‫‪473‬‬

‫‪5173806.8‬‬

‫)‪Previous Experience Between Groups (Combined‬‬ ‫* )‪(months‬‬ ‫‪Within Groups‬‬ ‫‪Employment Category‬‬ ‫‪Total‬‬

‫ﻣﻘﻴﺎس اﻟﺘﺮاﺑﻂ إﻳﺘﺎ ﻟﻘﻴﺎس اﻟﻌﻼﻗﺔ ﺑﻴﻦ اﻟﻤﺘﻐﻴﺮﻳﻦ اﻟﺨﺒﺮة اﻟﺴﺎﺑﻘﺔ واﻟﻄﺒﻘﺔ اﻟﻮﻇﻴﻔﻴﺔ ‪:‬‬ ‫‪Measures of Association‬‬ ‫‪Eta Squared‬‬ ‫‪.227‬‬

‫‪Eta‬‬ ‫‪.477‬‬

‫‪Previous Experience‬‬ ‫* )‪(months‬‬ ‫‪Employment Category‬‬

‫ﻭﻓﻲ ﻨﺘﺎﺌﺞ ﺘﻨﻔﻴﺫ ﺃﻤﺭ ﺤﺴﺎﺏ ﺍﻟﻤﺘﻭﺴﻁﺎﺕ ‪ Means‬ﻓﻲ ﺍﻟﺸﻜل ‪ 22-4‬ﺍﻟﺴﺎﺒﻕ‬

‫ﺘﻅﻬﺭ ﻗﻴﻡ ﺍﻟﻤﺘﻭﺴﻁﺎﺕ ﻭﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻷﺨﺭﻯ ﻟﻤﺩﺓ ﺍﻟﺨﺒﺭﺓ ﺒﺎﻟﺸﻬﻭﺭ ﺒﻭﻀﻭﺡ ﻟﻜل ﻓﺌﺔ‬ ‫ﻼ ﻓﻲ‬ ‫ﻤﻥ ﺍﻟﻔﺌﺎﺕ ﺍﻟﻭﻅﻴﻔﻴﺔ‪ ،‬ﻭﻟﻜﻥ ﻗﺩ ﻨﺤﺘﺎﺝ ﺇﻟﻰ ﺘﻘﺴﻴﻡ ﺍﻟﻤﺠﺘﻤﻊ ﺇﻟﻰ ﻓﺌﺎﺕ ﺃﻜﺜﺭ ﺘﻔﺼﻴ ﹰ‬

‫ﺒﻌﺽ ﺍﻷﺤﻴﺎﻥ‪ ،‬ﻓﻘﺩ ﻨﺤﺘﺎﺝ ﺇﻟﻰ ﻜل ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻟﺴﺎﺒﻘﺔ ﻟﻤﺩﺓ ﺍﻟﺨﺒﺭﺓ ﺒﺎﻟﺸﻬﻭﺭ ﻟﺠﻤﻴﻊ‬ ‫ﺍﻟﻁﺒﻘﺎﺕ ﺍﻟﻭﻅﻴﻔﻴﺔ ﻭﻟﻜﻥ ﻟﻠﺫﻜﻭﺭ ﻭﺍﻹﻨﺎﺙ ﻜل ﻋﻠﻰ ﺤﺩﺓ ﺜﻡ ﻟﻠﻨﻭﻋﻴﻥ ﻤﻌﹰﺎ ‪ ،‬ﻭﻫﺫﺍ ﻴﻤﻜﻥ‬

‫ﺍﻟﺤﺼﻭل ﻋﻠﻴﻪ ﻋﻥ ﻁﺭﻴﻕ ﺘﻘﺩﻴﻡ ﻤﺘﻐﻴﺭ ﺍﻟﻨﻭﻉ ‪ gender‬ﻟﻴﻤﺜل ﺘﻘﺴﻴﻡ ﻁﺒﻘﻲ ﺠﺩﻴﺩ‬

‫‪ Layering‬ﻭﺇﺩﺨﺎﻟﻪ ﻓﻲ ﻨﺎﻓﺫﺓ ﺤﺴﺎﺏ ﺍﻟﻤﺘﻭﺴﻁﺎﺕ ‪) Means‬ﺸﻜل ‪ (20-4‬ﻭﺫﻟﻙ‬ ‫ﺒﺈﺩﺨﺎل ﺍﺴﻡ ﺍﻟﻤﺘﻐﻴﺭ ‪ gender‬ﻟﻴﻤﺜل ﺍﻟﺘﻘﺴﻴﻡ ﺍﻟﻁﺒﻘﻲ ‪ Layer‬ﺍﻷﻭل ﺜﻡ ﺍﻟﻀﻐﻁ ﻋﻠﻰ‬ ‫ﻤﺭﺒﻊ "ﺍﻟﺘﺎﻟﻲ" ‪ Next‬ﻹﻀﺎﻓﺔ ﺍﻟﺘﻘﺴﻴﻡ ﺍﻟﻁﺒﻘﻲ ﺍﻵﺨﺭ ﺍﻟﻤﺒﻨﻲ ﻋﻠﻰ ﺃﺴﺎﺱ ﺍﻟﻤﺘﻐﻴﺭ‬

‫‪ jobcat‬ﻓﻲ ﻤﺭﺒﻊ ﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺘﺼﻨﻴﻑ ﺘﺤﺕ ﺍﺴﻡ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﺴﺘﻘﻠﺔ ‪Independent‬‬

‫‪ ، List‬ﻭﺒﺘﻨﻔﻴﺫ ﺍﻷﻤﺭ ﺴﻴﻅﻬﺭ ﺠﺩﻭل ﺒﻨﺘﺎﺌﺞ ﺍﻟﺘﻘﺴﻴﻡ ﺍﻟﻤﺯﺩﻭﺝ ﻜﻤﺎ ﻓﻲ ﺍﻟﺸﻜل ‪.23-4‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪165‬‬

‫ﺷﻜﻞ ‪ : 23-4‬ﻧﺘﺎﺋﺞ اﺳﺘﺨﺪام أﻣﺮ ﺣﺴﺎب اﻟﻤﺘﻮﺳﻄﺎت ‪ Means‬ﻋﻠﻰ ﺑﻴﺎﻧﺎت ﻣﻠﻒ‬ ‫‪ Employee Data‬ﺑﻌﺪ إﺿﺎﻓﺔ ﻣﺘﻐﻴﺮ ﺗﻘﺴﻴﻢ اﻟﺠﺪﻳﺪ ‪. gender‬‬ ‫‪Report‬‬ ‫)‪Previous Experience (months‬‬ ‫‪Std. Deviation‬‬

‫‪Mean‬‬

‫‪% of Total N‬‬

‫‪95.55‬‬

‫‪78.04‬‬

‫‪43.5%‬‬

‫‪N‬‬ ‫‪206‬‬

‫‪Employment Category‬‬ ‫‪Clerical‬‬

‫‪Gender‬‬ ‫‪Female‬‬

‫‪Custodial‬‬ ‫‪Manager‬‬

‫‪84.85‬‬

‫‪56.40‬‬

‫‪2.1%‬‬

‫‪10‬‬

‫‪95.01‬‬

‫‪77.04‬‬

‫‪45.6%‬‬

‫‪216‬‬

‫‪Total‬‬

‫‪94.43‬‬

‫‪94.22‬‬

‫‪33.1%‬‬

‫‪157‬‬

‫‪Clerical‬‬

‫‪101.43‬‬

‫‪298.11‬‬

‫‪5.7%‬‬

‫‪27‬‬

‫‪Male‬‬

‫‪Custodial‬‬ ‫‪Manager‬‬

‫‪71.73‬‬

‫‪80.49‬‬

‫‪15.6%‬‬

‫‪74‬‬

‫‪109.69‬‬

‫‪111.62‬‬

‫‪54.4%‬‬

‫‪258‬‬

‫‪Total‬‬

‫‪95.27‬‬

‫‪85.04‬‬

‫‪76.6%‬‬

‫‪363‬‬

‫‪Clerical‬‬

‫‪101.43‬‬

‫‪298.11‬‬

‫‪5.7%‬‬

‫‪27‬‬

‫‪73.26‬‬

‫‪77.62‬‬

‫‪17.7%‬‬

‫‪84‬‬

‫‪104.59‬‬

‫‪95.86‬‬

‫‪100.0%‬‬

‫‪474‬‬

‫‪Total‬‬

‫‪Custodial‬‬ ‫‪Manager‬‬ ‫‪Total‬‬

‫ﺃﻤﺭ ﺍﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪: Explore‬‬ ‫ﻭﻴﺄﺘﻲ ﺃﻤﺭ ﺍﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪ Explore‬ﻟﺘﻨﻔﻴﺫ ﺤﺴﺎﺏ ﻤﺠﻤﻭﻋﺔ ﻜﺒﻴﺭﺓ ﻤﻥ‬

‫ﺍﻹﺤﺼﺎﺀﺍﺕ ‪ Statistics‬ﺒﺎﻹﻀﺎﻓﺔ ﺇﻟﻰ ﻤﺠﻤﻭﻋﺔ ﻜﺒﻴﺭﺓ ﻤﻥ ﺍﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﺒﻴﺎﻨﻴﺔ ﻟﺒﻴﺎﻨﺎﺕ‬

‫ﻅﺎﻫﺭﺓ ﻤﻌﻴﻨﺔ‪ ،‬ﻭﻴﻤﻜﻥ ﺘﻁﺒﻴﻘﻪ ﻋﻠﻰ ﻤﺠﻤﻭﻋﺔ ﻜﺒﻴﺭﺓ ﻤﻥ ﺍﻟﻅﻭﺍﻫﺭ ﻓﻲ ﺃﻤﺭ ﻭﺍﺤﺩ ﻭﻟﻜﻥ‬

‫ﺴﻴﻅل ﺍﻟﺘﻨﻔﻴﺫ ﻋﻠﻰ ﻜل ﻅﺎﻫﺭﺓ ﺒﻤﻔﺭﺩﻫﺎ‪ ،‬ﻓﻬﺫﺍ ﺍﻷﻤﺭ ﻴﻘﺩﻡ ﻤﺠﻤﻭﻋﺔ ﻜﺒﻴﺭﺓ ﻤﻥ ﺍﻟﻨﺘﺎﺌﺞ‬ ‫ﺍﻟﺘﻲ ﺘﻘﺩﻤﻬﺎ ﺃﻭﺍﻤﺭ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ ﺍﻟﺴﺎﺒﻕ ﺫﻜﺭﻫﺎ‪ ،‬ﻜﻤﺎ ﻴﻤﻜﻥ ﺍﺴﺘﺨﺩﺍﻤﻪ ﻟﻠﻤﺘﻐﻴﺭﺍﺕ‬

‫ﺍﻟﻜﻤﻴﺔ ﺴﻭﺍﺀ ﻜﺎﻥ ﺍﻟﻬﺩﻑ ﻫﻭ ﺍﺴﺘﻜﺸﺎﻓﻬﺎ ﺒﺸﻜل ﺸﻤﻭﻟﻲ ﺃﻭ ﺒﺸﻜل ﺠﺯﺌﻲ ﺃﻱ ﻷﺠﺯﺍﺀ‬

‫ﺍﻟﻤﺠﺘﻤﻊ ﺍﻟﺫﻱ ﺘﻡ ﺘﻘﺴﻴﻤﻪ ﺇﻟﻰ ﻁﺒﻘﺎﺕ ﺤﺴﺏ ﺍﻟﻔﺌﺎﺕ ﺍﻟﻤﺨﺘﻠﻔﺔ ﻟﻅﺎﻫﺭﺓ ﺃﺨﺭﻯ ﻤﺜل‬

‫ﺍﻟﻨﻭﻉ ﺃﻭ ﺍﻟﻁﺒﻘﺔ ﺍﻟﻭﻅﻴﻔﻴﺔ ﺃﻭ ﻏﻴﺭﻩ‪ ،‬ﻓﻬﺫﺍ ﺍﻷﻤﺭ ﻴﻤﻜﻨﻪ ﺤﺴﺎﺏ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ‬

‫ﻭﺘﻜﻭﻴﻥ ﺍﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﺒﻴﺎﻨﻴﺔ ﻟﺒﻴﺎﻨﺎﺕ ﺍﻟﻅﺎﻫﺭﺓ ﻓﻲ ﻤﺠﻤﻠﻬﺎ ﺃﻭ ﻟﻜل ﻁﺒﻘﺔ ﻋﻠﻰ ﺤﺩﺓ‪،‬‬

‫ﻭﻤﻥ ﺍﻟﻤﻔﻴﺩ ﺃﻥ ﻨﺒﺩﺃ ﺩﺍﺌﻤﹰﺎ ﺒﺎﺴﺘﻜﺸﺎﻑ ﺍﻟﻅﺎﻫﺭﺓ ﻓﻲ ﻤﺠﻤﻠﻬﺎ ﻗﺒل ﺍﻟﺒﺩﺀ ﻓﻲ ﺍﻟﺘﺤﻠﻴل‪،‬‬ ‫ﻓﻴﻌﻁﻲ ﺃﻤﺭ ﺍﻻﺴﺘﻜﺸﺎﻑ ‪ Explore‬ﺜﻼﺙ ﺃﻨﻭﺍﻉ ﻤﻥ ﺍﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﺒﻴﺎﻨﻴﺔ ﺍﻟﻤﻔﻴﺩﺓ ﻓﻲ‬ ‫ﺇﻋﻁﺎﺀ ﺼﻭﺭﺓ ﺸﻤﻭﻟﻴﺔ ﻋﻥ ﺍﻟﻅﺎﻫﺭﺓ ﻭﻫﻲ‪:‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪166‬‬

‫‪ .1‬ﺍﻟﻤﺩﺭﺝ ﺍﻟﺘﻜﺭﺍﺭﻱ ‪ Histogram‬؛‬ ‫‪ .2‬ﺍﻟﺠﺫﻉ ﻭﺍﻟﻔﺭﻭﻉ ‪ Stem-and-leaf displays‬؛‬

‫‪ .3‬ﺼﻨﺩﻭﻕ ﺍﻻﻨﺘﺸﺎﺭ ‪. Boxplots‬‬

‫ﻭﻋﺎﺩﺓ ﻴﻜﻭﻥ ﺍﻷﺴﺎﺱ ﻓﻲ ﺠﻤﻴﻊ ﻫﺫﻩ ﺍﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﺒﻴﺎﻨﻴﺔ ﻫﻭ ﺠﺩﻭل ﺍﻟﺘﻭﺯﻴﻊ‬

‫ﺍﻟﺘﻜﺭﺍﺭﻱ ‪ frequency distribution‬ﻭﺍﻟﺫﻱ ﻴﻤﻜﻥ ﺘﻜﻭﻴﻨﻪ ﻟﻠﻅﺎﻫﺭﺓ ﺴﻭﺍﺀ ﻜﺎﻨﺕ ﻜﻤﻴﺔ‬

‫ﺃﻭ ﻭﺼﻔﻴﺔ‪ ،‬ﺇﻻ ﺃﻨﻪ ﻓﻲ ﺤﺎﻟﺔ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻜﻤﻴﺔ ﻴﻘﻭﻡ ﻫﺫﺍ ﺍﻷﻤﺭ ﺒﺘﻘﺴﻴﻡ ﻤﺩﻯ ﺍﻟﻅﺎﻫﺭﺓ‬

‫ﺇﻟﻰ ﻓﺘﺭﺍﺕ ﻭﺇﻋﻁﺎﺀ ﻋﺩﺩ ﺍﻟﻤﻔﺭﺩﺍﺕ ﺍﻟﺘﻲ ﺘﻨﺘﻤﻲ ﺇﻟﻰ ﻜل ﻓﺘﺭﺓ ‪.‬‬

‫ﻭﻴﻌﺘﺒﺭ ﺸﻜل ﺍﻷﻋﻤﺩﺓ ﺍﻟﺒﺴﻴﻁﺔ ‪ Bar chart‬ﻤﻨﺎﺴﺒﹰﺎ ﻟﺤﺎﻟﺔ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻨﻭﻋﻴﺔ‬

‫‪ qualitative variables‬ﺃﻭ ﺍﻟﻜﻤﻴﺔ ﺍﻟﻤﺘﻘﻁﻌﺔ ‪ discrete‬ﻓﻘﻁ ‪ ،‬ﻭﻓﻲ ﻫﺫﺍ ﺍﻟﺸﻜل ﺘﻜﻭﻥ‬ ‫ﺍﻷﻋﻤﺩﺓ ﻤﺘﺒﺎﻋﺩﺓ ﻟﺘﻭﻀﺢ ﻁﺒﻴﻌﺔ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻤﻨﻔﺼﻠﺔ ‪ ، discrete‬ﺇﻻ ﺃﻥ ﺸﻜل‬

‫ﺍﻟﻤﺩﺭﺝ ﺍﻟﺘﻜﺭﺍﺭﻱ ‪ histogram‬ﻴﻌﺘﺒﺭ ﻤﻨﺎﺴﺒﹰﺎ ﻟﺘﻤﺜﻴل ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﻜﻤﻴﺔ ﺍﻟﻤﺘﺼﻠﺔ‬

‫‪ continuous variables‬ﻨﻅﺭﹰﺍ ﻟﻌﺩﻡ ﻭﺠﻭﺩ ﻤﺴﺎﻓﺎﺕ ﺒﻴﻥ ﺍﻷﻋﻤﺩﺓ ﻭﻴﺒﺩﺃ ﻜل ﻋﻤﻭﺩ‬ ‫ﻋﻠﻰ ﺍﻟﻤﺤﻭﺭ ﺍﻷﻓﻘﻲ ﻤﻥ ﺤﻴﺙ ﻴﻨﺘﻬﻲ ﺍﻟﻌﻤﻭﺩ ﺍﻟﺴﺎﺒﻕ‪.‬‬

‫ﻭﻟﺘﻭﻀﻴﺢ ﺃﻤﺭ ﺍﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪ Explore‬ﺴﻨﺴﺘﺨﺩﻡ ﺒﻴﺎﻨﺎﺕ ﻨﻔﺱ ﺍﻟﻤﺜﺎل‬

‫ﺍﻟﺫﻱ ﺘﻡ ﺍﺴﺘﺨﺩﺍﻤﻪ ﻓﻲ ﺍﻷﻭﺍﻤﺭ ﺍﻟﺴﺎﺒﻘﺔ ‪ ،‬ﻓﺈﺫﺍ ﺃﺨﺫﻨﺎ ﺍﻟﻤﺘﻐﻴﺭ ﺍﻟﻜﻤﻲ ﺍﻟﻤﺘﺼل ‪prevexp‬‬

‫ﺍﻟﺫﻱ ﻴﻤﺜل ﺍﻟﺨﺒﺭﺓ ﺍﻟﺴﺎﺒﻘﺔ ﺒﺎﻟﺸﻬﺭ )‪ Previous experience (months‬ﻓﻲ ﻤﻠﻑ‬

‫‪ Employee data‬ﺍﻟﻤﺸﺎﺭ ﺇﻟﻴﻪ ﺴﺎﺒﻘﹰﺎ ‪ ،‬ﻓﺈﻨﻪ ﻟﺘﻨﻔﻴﺫ ﺃﻤﺭ ﺍﻻﺴﺘﻜﺸﺎﻑ ‪ Explore‬ﺴﻭﻑ‬ ‫ﻨﺘﺒﻊ ﻤﺎ ﻴﻠﻲ ‪:‬‬

‫• ﺍﻀﻐﻁ ﺒﺎﻟﻔﺎﺭﺓ ﻋﻠﻰ ﺃﻤﺭ ﺍﻻﺴﺘﻜﺸﺎﻑ ‪ Explore‬ﻓﻲ ﻗﺎﺌﻤﺔ ﺍﻹﺤﺼﺎﺀﺍﺕ ﺍﻟﻭﺼﻔﻴﺔ‬ ‫‪) Descriptive Statistics‬ﻗﺎﺌﻤﺔ ﺘﻠﺨﻴﺹ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪ Summarize‬ﻓﻲ ﺇﺼﺩﺍﺭ ‪(8.0‬‬ ‫ﻓﺘﻔﺘﺢ ﻨﺎﻓﺫﺓ ﺍﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪ Explore‬ﻜﻤﺎ ﻓﻲ ﺍﻟﺸﻜل ‪. 24-4‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪167‬‬

‫ﺸﻜل ‪ : 24-4‬ﻨﺎﻓﺫﺓ ﺍﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪ Explore‬ﻓﻲ ﻗﺎﺌﻤﺔ ﺍﻹﺤﺼﺎﺀﺍﺕ ﺍﻟﻭﺼﻔﻴﺔ‬ ‫‪) Descriptive Statistics‬ﻗﺎﺌﻤﺔ ﺘﻠﺨﻴﺹ ‪ Summarize‬ﻓﻲ ﺇﺼﺩﺍﺭ ‪.(8.0‬‬

‫• ﻓﻲ ﻨﺎﻓﺫﺓ ﺤﻭﺍﺭ ﺍﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪ Explore‬ﺃﺩﺨل ﺍﺴﻡ ﺍﻟﻤﺘﻐﻴﺭ ‪ prevexp‬ﻓﻲ‬ ‫ﻤﺭﺒﻊ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺘﺎﺒﻌﺔ ‪ Dependent List‬ﻓﻲ ﺍﻟﺠﺯﺀ ﺍﻷﻴﻤﻥ ﻤﻥ ﺍﻟﻨﺎﻓﺫﺓ ﺒﺈﺯﺍﺤﺘﻪ‬

‫ﺒﺎﻟﺴﻬﻡ ﻤﻥ ﺍﻟﺠﺯﺀ ﺍﻷﻴﺴﺭ ﺇﻟﻰ ﺍﻟﺠﺯﺀ ﺍﻷﻴﻤﻥ ﺍﻟﻌﻠﻭﻱ ﻤﻥ ﺍﻟﻨﺎﻓﺫﺓ‪ ،‬ﺜﻡ ﺃﺩﺨل ﺍﺴﻡ ﻤﺘﻐﻴﺭ‬

‫ﺍﻟﺘﺼﻨﻴﻑ ‪ jobcat‬ﻓﻲ ﻤﺭﺒﻊ ﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺘﺼﻨﻴﻑ ﺘﺤﺕ ﺍﺴﻡ ﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺘﺼﻨﻴﻑ‬ ‫‪ Factor List‬ﻜﻤﺎ ﻓﻲ ﺍﻟﺸﻜل ‪ ،24-4‬ﻭﻴﺠﺩﺭ ﺒﺎﻟﺫﻜﺭ ﺃﻨﻪ ﻴﻤﻜﻥ ﺍﺨﺘﻴﺎﺭ ﺃﻱ ﻋﺩﺩ ﻤﻥ‬

‫ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﻟﺘﻨﻔﻴﺫ ﻫﺫﺍ ﺍﻷﻤﺭ ﻋﻠﻴﻬﻡ ﺒﻨﻔﺱ ﺍﻟﻁﺭﻴﻘﺔ‪ ،‬ﻭﺒﺎﻹﻤﻜﺎﻥ ﺃﻴﻀﹰﺎ ﻋﺩﻡ ﺍﺨﺘﻴﺎﺭ ﺃﻱ‬ ‫ﻤﺘﻐﻴﺭ ﺘﺼﻨﻴﻑ ﻻﺴﺘﻜﺸﺎﻑ ﺍﻟﻤﺘﻐﻴﺭ ﺍﻟﺘﺎﺒﻊ ﺒﻨﻅﺭﺓ ﺸﻤﻭﻟﻴﺔ ‪.‬‬

‫• ﻓﻲ ﻨﺎﻓﺫﺓ ﺍﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪ Explore‬ﺍﻟﺴﺎﺒﻘﺔ ﻭﻓﻲ ﺃﺴﻔﻠﻬﺎ ﺍﺨﺘﺭ ﻤﺭﺒﻊ ﺍﻟﺤﻭﺍﺭ‬

‫‪ Statistics‬ﺍﻟﺨﺎﺹ ﺒﺎﺨﺘﻴﺎﺭ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ﺍﻟﻤﻁﻠﻭﺏ ﺤﺴﺎﺒﻬﺎ ﻟﻴﺘﻡ ﻓﺘﺢ ﻨﺎﻓﺫﺓ‬

‫ﺍﺨﺘﻴﺎﺭ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ‪ Statistics‬ﻜﻤﺎ ﻓﻲ ﺍﻟﺸﻜل ‪ ، 25-4‬ﻭﺘﻅﻬﺭ ﺘﻠﻙ ﺍﻟﻨﺎﻓﺫﺓ‬

‫ﻓﻲ ﺍﻟﺸﻜل ﻭﻗﺩ ﺘﻡ ﻓﻴﻬﺎ ﺍﺨﺘﻴﺎﺭ ﺠﻤﻴﻊ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ﺍﻟﻭﺼﻔﻴﺔ ‪Descriptives‬‬

‫ﻭ‪ 95%‬ﻓﺘﺭﺓ ﺜﻘﺔ ﻟﻜل ﻤﻥ ﻫﺫﻩ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺒﺎﻹﻀﺎﻓﺔ ﺇﻟﻰ ﺍﻟﻘﻴﻡ ﺍﻟﺸﺎﺫﺓ ‪ Outliers‬ﻓﻲ‬ ‫ﺍﻟﺒﻴﺎﻨﺎﺕ‪ ،‬ﺃﻤﺎ ﺍﻟﺨﻴﺎﺭﺍﺕ ﺍﻷﺨﺭﻯ ﻓﻬﻲ ﺍﻟﻤﺌﻴﻨﻴﺎﺕ ﻭﺘﻘﺩﻴﺭﺍﺕ ﻟﺘﻠﻙ ﺍﻟﻤﻘﺎﻴﻴﺱ ﻭﻫﻲ‬

‫ﺃﺴﻠﻭﺏ ﻤﺘﻘﺩﻡ ﻓﻲ ﺘﻘﺩﻴﺭ ﺍﻟﻤﻌﺎﻟﻡ ﺍﻹﺤﺼﺎﺌﻴﺔ ‪.‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪168‬‬

‫ﺸﻜل ‪ : 25-4‬ﻨﺎﻓﺫﺓ ﺍﺨﺘﻴﺎﺭ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ‪ Statistics‬ﻀﻤﻥ‬ ‫ﻨﺎﻓﺫﺓ ﺍﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪. Explore‬‬

‫• ﻓﻲ ﻨﻔﺱ ﻨﺎﻓﺫﺓ ﺍﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪ Explore‬ﺍﻟﺴﺎﺒﻘﺔ ﻭﻓﻲ ﺃﺴﻔﻠﻬﺎ ﺍﺨﺘﺭ ﻤﺭﺒﻊ‬

‫ﺍﻟﺤﻭﺍﺭ ﺍﻟﺨﺎﺹ ﺒﺎﻷﺸﻜﺎل ﺍﻟﺒﻴﺎﻨﻴﺔ ‪ Plots‬ﻟﻴﺘﻡ ﻓﺘﺢ ﻨﺎﻓﺫﺓ ﺍﻷﺸﻜﺎل ﺍﻟﺒﻴﺎﻨﻴﺔ ‪ Plots‬ﻜﻤﺎ‬

‫ﻓﻲ ﺍﻟﺸﻜل ‪ ، 26-4‬ﻭﻤﻥ ﻫﺫﻩ ﺍﻟﻨﺎﻓﺫﺓ ﺍﺨﺘﺭ ﺸﻜل ﺼﻨﺩﻭﻕ ﺍﻻﻨﺘﺸﺎﺭ ‪ Boxplots‬ﻭﻤﻨﻪ‬ ‫ﺍﺨﺘﺭ ﻤﺴﺘﻭﻴﺎﺕ ﺍﻟﻌﻭﺍﻤل ﻤﻌﹰﺎ ‪ Factor levels together‬ﺜﻡ ﻤﻥ ﺍﻟﺭﺴﻭﻤﺎﺕ ﺍﻟﻭﺼﻔﻴﺔ‬

‫‪ Descriptive‬ﺍﺨﺘﺭ ﺸﻜﻠﻲ ﺍﻟﺠﺫﻉ ﻭﺍﻟﻔﺭﻭﻉ ‪ Stem-and-leaf‬ﻭﺍﻟﻤﺩﺭﺝ ﺍﻟﺘﻜﺭﺍﺭﻱ‬

‫‪ ، Histogram‬ﺜﻡ ﺒﺎﻹﻤﻜﺎﻥ ﺍﺨﺘﻴﺎﺭ ﺭﺴﻡ ﺍﺨﺘﺒﺎﺭ ﺍﻟﺘﻭﺯﻴﻊ ﺍﻟﻁﺒﻴﻌﻲ ﻟﻠﺒﻴﺎﻨﺎﺕ‬ ‫‪ Normality plots with tests‬ﻭﻜﺫﻟﻙ ﺃﻱ ﻤﻥ ﺸﻜل ﺍﻻﻨﺘﺸﺎﺭ ﻤﻘﺎﺒل ﺍﻟﻤﺴﺘﻭﻴﺎﺕ‬ ‫‪. Spread vs. Level with Levene Test‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪169‬‬

‫ﺸﻜل ‪ : 26-4‬ﻨﺎﻓﺫﺓ ﺍﺨﺘﻴﺎﺭ ﺍﻷﺸﻜﺎل ﺍﻟﺒﻴﺎﻨﻴﺔ ‪ Plots‬ﻀﻤﻥ‬ ‫ﻨﺎﻓﺫﺓ ﺍﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪. Explore‬‬

‫• ﻴﻤﻜﻨﻙ ﺍﻵﻥ ﻓﻲ ﺍﻟﻨﻬﺎﻴﺔ ﺘﺤﺩﻴﺩ ﻜﻴﻔﻴﺔ ﺍﻟﺘﻌﺎﻤل ﻤﻊ ﺍﻟﻘﻴﻡ ﺍﻟﻤﻔﻘﻭﺩﺓ ﻭﺫﻟﻙ ﺒﺎﺨﺘﻴﺎﺭ‬ ‫ﻤﺭﺒﻊ ﺍﻟﺤﻭﺍﺭ ﺍﻟﺨﺎﺹ ﺒﺎﻟﻘﻴﻡ ﺍﻟﻤﻔﻘﻭﺩﺓ ‪ Options‬ﻟﻴﺘﻡ ﻓﺘﺢ ﻨﺎﻓﺫﺓ ﺍﻟﺨﻴﺎﺭﺍﺕ ‪Options‬‬

‫ﻜﻤﺎ ﻓﻲ ﺍﻟﺸﻜل ‪ 27-4‬ﻭﺫﻟﻙ ﻓﻲ ﻨﻔﺱ ﻨﺎﻓﺫﺓ ﺍﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪ Explore‬ﺍﻟﺴﺎﺒﻘﺔ‬

‫ﻭﻓﻲ ﺃﺴﻔﻠﻬﺎ‪ ،‬ﻭﻴﻤﻜﻥ ﺘﺤﺩﻴﺩ ﺇﻟﻐﺎﺀ ﺍﻟﻘﻴﻡ ﺍﻟﻤﻔﻘﻭﺩﺓ ﻟﻠﻤﺸﺎﻫﺩﺍﺕ ﺍﻟﺘﻲ ﺘﺤﺘﻭﻱ ﻋﻠﻰ ﻤﺘﻐﻴﺭ‬ ‫ﻭﺍﺤﺩ ﻋﻠﻰ ﺍﻷﻗل ﻟﻪ ﻗﻴﻤﺔ ﻤﻔﻘﻭﺩﺓ ﻤﻥ ﺒﻴﻥ ﺍﻟﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺘﺎﺒﻌﺔ ‪ Independent list‬ﺃﻭ‬

‫ﻗﺎﺌﻤﺔ ﺍﻟﻌﻭﺍﻤل ‪ Factor list‬ﺃﻱ ﺍﻟﺨﻴﺎﺭ ﺍﻷﻭل ‪ ، Exclude cases listwise‬ﺃﻭ ﺍﻟﺨﻴﺎﺭ‬ ‫ﺍﻟﺜﺎﻨﻲ ﺍﻟﻤﺘﻌﻠﻕ ﺒﺤﺫﻑ ﺍﻟﻘﻴﻤﺔ ﺍﻟﻤﻔﻘﻭﺩﺓ ﺇﺫﺍ ﻜﺎﻥ ﺍﻟﻤﺘﻐﻴﺭ ﺍﻟﺫﻱ ﺴﻴﺘﻡ ﺍﻟﺘﻌﺎﻤل ﻓﻘﻁ ﻤﻌﻪ ﺫﻭ‬

‫ﻗﻴﻤﺔ ﻤﻔﻘﻭﺩﺓ ‪ ، Exclude cases pairwise‬ﺃﻭ ﺍﻟﺨﻴﺎﺭ ﺍﻟﺜﺎﻟﺙ ﺍﻟﻤﺘﻌﻠﻕ ﺒﺎﻹﺒﻼﻍ ﻋﻥ‬ ‫ﺍﻟﻘﻴﻡ ﺍﻟﻤﻔﻘﻭﺩﺓ ‪. Report values‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪170‬‬

‫ﺸﻜل ‪ : 27-4‬ﻨﺎﻓﺫﺓ ﺍﻟﺨﻴﺎﺭﺍﺕ ‪ Options‬ﺍﻟﻤﺘﻌﻠﻘﺔ ﺒﺎﻟﻘﻴﻡ ﺍﻟﻤﻔﻘﻭﺩﺓ‬ ‫ﻀﻤﻥ ﻨﺎﻓﺫﺓ ﺍﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪. Explore‬‬

‫• ﻓﻲ ﺍﻟﻨﻬﺎﻴﺔ ﺒﺎﻟﺘﺄﻜﺩ ﻤﻥ ﺍﺨﺘﻴﺎﺭ ﺃﺤﺩ ﺍﻟﺨﻴﺎﺭﺍﺕ ﺍﻟﺜﻼﺙ ﻓﻲ ﺃﺴﻔل ﻨﺎﻓﺫﺓ ﺍﺴﺘﻜﺸﺎﻑ‬ ‫ﺍﻟﺒﻴﺎﻨﺎﺕ ‪ Explore‬ﺍﻟﺴﺎﺒﻘﺔ ﻭﺍﻟﻤﺘﻌﻠﻘﺔ ﺒﺈﻅﻬﺎﺭ ﻨﺘﺎﺌﺞ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ‪Statistics‬‬

‫ﺃﻭ ﺍﻷﺸﻜﺎل ﺍﻟﺒﻴﺎﻨﻴﺔ ‪ Plots‬ﺃﻭ ﻜﻠﻴﻬﻤﺎ ‪ Both‬ﻴﺘﻡ ﺍﻟﻀﻐﻁ ﻋﻠﻰ ﻤﻔﺘﺎﺡ ﺍﻟﺘﻨﻔﻴﺫ ‪ Ok‬ﻓﻲ‬

‫ﺘﻠﻙ ﺍﻟﺸﺎﺸﺔ ﻟﺘﻨﻔﻴﺫ ﺃﻤﺭ ﺍﻻﺴﺘﻜﺸﺎﻑ ﻭﻅﻬﻭﺭ ﺍﻟﻨﺘﺎﺌﺞ ﺍﻟﻤﻁﻠﻭﺒﺔ ﻜﻤﺎ ﻓﻲ ﺸﻜل ‪. 28-4‬‬

‫( ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬4)

171

Explore ‫ ﻨﺘﺎﺌﺞ ﺘﻨﻔﻴﺫ ﺃﻤﺭ ﺍﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬: 28-4 ‫ﺸﻜل‬ Explore Employment Category Case Processing Summary Cases Valid Previous Experience (months)

Employment Category Clerical

N

Missing Percent

N

Total

Percent

N

Percent

363

100.0%

0

.0%

363

100.0%

Custodial

27

100.0%

0

.0%

27

100.0%

Manager

84

100.0%

0

.0%

84

100.0%

Descriptives Employment Category Previous Experience (months)

Clerical

Statistic Mean 95% Confidence Interval for Mean

Lower Bound Upper Bound

5% Trimmed Mean

94.87

50.00

Variance

9077.258

Std. Deviation

95.27

Minimum

0

Maximum

476

Range

476

Interquartile Range

102.00

Skewness

1.667

Kurtosis

2.461

.255

298.11

19.52

Mean 95% Confidence Interval for Mean

Lower Bound Upper Bound

5% Trimmed Mean

257.99 338.23

305.00

Variance

10287.333

Std. Deviation

101.43

Minimum

144

Maximum

460

Range

316

Interquartile Range

178.00

Skewness

.014

Kurtosis Mean 95% Confidence Interval for Mean 5% Trimmed Mean Median Variance Std. Deviation

Lower Bound Upper Bound

.872

77.62

7.99

61.72 93.52 70.88 52.00 5367.010 73.26 3

Maximum

285

Interquartile Range Skewness Kurtosis

.448

-1.127

Minimum Range

.128

297.81

Median

Manager

5.00

75.20

73.98

Median

Custodial

Std. Error

85.04

282 106.50 1.276

.263

.817

.520

‫( ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬4)

172

Extreme Values Employment Category Previous Experience (months)

Clerical

Case Number Highest

Lowest

Custodial

Highest

Lowest

Manager

Highest

Lowest

Value

1

295

476

2

54

444

3

144

412

4

241

390

5

320

385

1

396

0

2

313

0

3

265

0

4

135

0

5

393

.

1

255

460

2

152

451

3

174

438

4

96

432

5

285

429

1

98

144

2

326

144

3

353

155

4

414

155

5

386

174

1

134

285

2

341

272

3

137

264

4

307

264

5

205

258

1

231

3

2

130

6

3

66

7

4

120

7

5

89

8

a

a. Only a partial list of cases with the value 0 are shown in the table of lower extremes.

Test of Homogeneity of Variance Levene Statistic Previous Experience (months)

df1

df2

Sig.

Based on Mean

2.544

2

471

.080

Based on Median

1.678

2

471

.188

Based on Median and with adjusted df

1.678

2

448.081

.188

Based on trimmed mean

2.231

2

471

.109

‫( ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬4)

173

Previous Experience (months) Histograms Histogram For JOBCAT= Clerical 100

80

60

Frequency

40

20

Std. Dev = 95.27 Mean = 85.0 N = 363.00

0

0 5.

47 .0 0 45 .0 5 42 .0 0 40 .0 5 37 .0 0 35 .0 5 32 .0 0 30 0 5.

27 .0 0 25 .0 5 22 .0 0 20 .0 5 17 .0 0 15 .0 5 12 .0 0 10 0 .

75 0 .

50 0 .

25 0 0.

Previous Experience (months)

Histogram For JOBCAT= Custodial 5

4

3

Frequency

2

Std. Dev = 101.43

1

Mean = 298.1 N = 27.00

0 150.0

200.0

175.0

250.0

225.0

300.0

275.0

350.0

325.0

400.0

375.0

450.0

425.0

Previous Experience (months)

Histogram For JOBCAT= Manager 30

Frequency

20

10 Std. Dev = 73.26 Mean = 77.6 N = 84.00

0 0.0

40.0 20.0

80.0

60.0

120.0

100.0

160.0

140.0

200.0

180.0

Previous Experience (months)

240.0

220.0

280.0

260.0

‫( ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬4)

174

Stem-and-Leaf Plots Previous Experience (months) Stem-and-Leaf Plot for JOBCAT= Clerical Frequency

Stem &

98.00 0 51.00 0 56.00 0 30.00 0 22.00 0 15.00 1 16.00 1 9.00 1 10.00 1 12.00 1 7.00 2 6.00 2 4.00 2 3.00 2 24.00 Extremes Stem width: Each leaf:

. . . . . . . . . . . . . .

Leaf 000000000000000000000001111111111 22222222233333333 444444444455555555 6666677777 8888999 00011 222333 455 6677 8899 01 22& 4& 6 (>=271)

100 3 case(s)

& denotes fractional leaves.

Previous Experience (months) Stem-and-Leaf Plot for JOBCAT= Custodial Frequency 2.00 4.00 4.00 2.00 7.00 2.00 4.00 2.00 Stem width: Each leaf:

Stem & 1 1 2 2 3 3 4 4

. . . . . . . .

Leaf 44 5579 0444 88 0000114 88 0233 56

100 1 case(s)

‫( ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬4)

175

Previous Experience (months) Stem-and-Leaf Plot for JOBCAT= Manager Frequency Stem &

Leaf

41.00 0 . 00000001111111111111122222223333333444444 20.00 0 . 55556666666777778899 9.00 1 . 222223344 7.00 1 . 5557799 2.00 2 . 02 4.00 2 . 5667 1.00 Extremes (>=285) Stem width: Each leaf:

100 1 case(s)

600

500

295 54 144 241 20 3 349 372 230 171 147 229 410 453 136 302 362 340 22

Previous Experience (months)

400

300

191 339 378 268 367

134

200

100

0 -100 N=

363

27

84

Clerical

Custodial

Manager

Employment Category

Spread vs. Level Plot of PREVEXP By JOBCAT 5.2 5.1 5.0 4.9

Spread

4.8 4.7 4.6 3.5

4.0

4.5

Level * Plot of LN of Spread vs LN of Level Slope = .300 Power for transformation = .700

5.0

5.5

6.0

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪176‬‬

‫ﻤﻼﺤﻅﺎﺕ ﻋﻠﻰ ﺍﻟﻨﺘﺎﺌﺞ ﺍﻟﺴﺎﺒﻘﺔ ‪:‬‬ ‫ﺒﺎﻟﻨﻅﺭ ﺇﻟﻰ ﺃﺸﻜﺎل ﺍﻟﺠﺫﻉ ﻭﺍﻟﻔﺭﻭﻉ ‪ stem-and-leaf display‬ﻨﻼﺤﻅ ﺃﻥ‬

‫ﺍﻟﻌﻤﻭﺩ ﺍﻷﺴﺎﺴﻲ ﺩﺍﺌﻤﹰﺎ ﻴﺸﻜل ﺍﻟﺠﺫﻉ ﻭﻴﻤﺜل ﺍﻟﺭﻗﻡ ﺍﻷﺴﺎﺴﻲ ﺍﻟﻤﺸﺘﺭﻙ ﻭﺍﻟﻤﺘﻜﺭﺭ ﻓﻲ‬ ‫ﺍﻟﻤﺸﺎﻫﺩﺍﺕ ﻭﻜل ﺭﻗﻡ ﻓﻲ ﺍﻟﻔﺭﻭﻉ ﻴﻤﺜل ﻗﻴﻤﺔ ﺃﻭ ﻤﺸﺎﻫﺩﺓ‪ ،‬ﻭﻜل ﺠﺫﻉ ﻓﻲ ﺍﻟﺸﻜل ﻴﻤﺜل‬

‫ﺍﻟﺤﺩ ﺍﻷﺩﻨﻰ ﻟﻔﺌﺔ‪ ،‬ﻭﺇﺫﺍ ﻜﺎﻥ ﻫﻨﺎﻙ ﻋﺩﺩ ﻜﺒﻴﺭ ﻤﻥ ﺍﻟﻔﺭﻭﻉ ﻓﻲ ﺠﺫﻉ ﻭﺍﺤﺩ )ﺃﻱ ﻋﺩﺩ‬

‫ﻜﺒﻴﺭ ﻤﻥ ﺍﻟﻤﻔﺭﺩﺍﺕ ﻓﻲ ﻓﺌﺔ ﻭﺍﺤﺩﺓ( ﻓﺈﻥ ﻨﻅﺎﻡ ‪ SPSS‬ﺴﻴﻘﻭﻡ ﺒﻘﺴﻤﺔ ﺍﻟﻔﺭﻉ ﺇﻟﻰ ﻓﺭﻋﻴﻥ‬ ‫ﻤﺴﺘﺨﺩﻤﹰﺎ ﺍﻟﺭﻤﺯ )*( ﻟﻴﻤﺜل ﺍﻟﻔﺭﻭﻉ ﻤﻥ ‪ 0‬ﺇﻟﻰ ‪ 4‬ﻭﺍﻟﺭﻤﺯ )‪ (.‬ﻟﻴﻤﺜل ﺍﻟﻔﺭﻭﻉ ﻤﻥ ‪5‬‬

‫ﺇﻟﻰ ‪ ،9‬ﻭﻓﻲ ﺒﻌﺽ ﺍﻟﺤﺎﻻﺕ ﻴﻀﻁﺭ ﺇﻟﻰ ﻗﺴﻤﺔ ﺍﻟﻔﺭﻉ ﺇﻟﻰ ‪ 5‬ﻓﺭﻭﻉ ﻤﺴﺘﺨﺩﻤﹰﺎ ﺍﻟﺭﻤﺯ‬

‫)*( ﻟﻠﻔﺭﻭﻉ ‪ 0‬ﻭ ‪ 1‬ﻭﺍﻟﺭﻤﺯ )‪ (t‬ﻟﻠﻔﺭﻭﻉ ‪ 2‬ﻭ ‪ 3‬ﻭﺍﻟﺭﻤﺯ )‪ (f‬ﻟﻠﻔﺭﻭﻉ ‪ 4‬ﻭ ‪ 5‬ﻭﺍﻟﺭﻤﺯ‬ ‫)‪ (s‬ﻟﻠﻔﺭﻭﻉ ‪ 6‬ﻭ ‪ 7‬ﻭﺍﻟﺭﻤﺯ )‪ (.‬ﻟﻠﻔﺭﻭﻉ ‪ 8‬ﻭ ‪ ، 9‬ﺃﻤﺎ ﺍﻟﻌﻤﻭﺩ ﺒﺎﻟﻌﻨﻭﺍﻥ ﺘﻜﺭﺍﺭﺍﺕ‬

‫‪ Frequency‬ﻓﺈﻨﻪ ﻴﻭﻀﺢ ﻋﺩﺩ ﺍﻟﻤﻔﺭﺩﺍﺕ ﻓﻲ ﻜل ﻓﺌﺔ‪ ،‬ﻤﻥ ﻫﻨﺎ ﻴﺘﻀﺢ ﺃﻥ ﻫﺫﺍ ﺍﻟﻨﻭﻉ‬ ‫ﻤﻥ ﺍﻷﺸﻜﺎل ﻴﻜﻭﻥ ﺴﻬل ﺍﻟﺘﻔﺴﻴﺭ ﻭﻤﻔﻴﺩ ﻓﻲ ﺘﻤﺜﻴل ﺍﻟﺒﻴﺎﻨﺎﺕ ﻋﻨﺩﻤﺎ ﻴﻜﻭﻥ ﻋﺩﺩ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫ﻗﻠﻴل‪ ،‬ﺃﻤﺎ ﻋﻨﺩﻤﺎ ﻴﻜﻭﻥ ﻋﺩﺩ ﺍﻟﻘﻴﻡ ﻜﺒﻴﺭ ﻓﺈﻨﻪ ﻴﻔﻀل ﺍﺴﺘﺨﺩﺍﻡ ﺍﻟﻤﺩﺭﺝ ﺍﻟﺘﻜﺭﺍﺭﻱ‬ ‫‪. Histogram‬‬

‫ﻭﺒﺎﻟﻨﻅﺭ ﺇﻟﻰ ﺃﺸﻜﺎل ﺼﻨﺩﻭﻕ ﺍﻻﻨﺘﺸﺎﺭ ‪ Boxplots‬ﻴﻤﻜﻨﻨﺎ ﺃﻥ ﻨﻼﺤﻅ ﺃﻥ‬

‫ﺍﻟﺼﻨﺩﻭﻕ ﻨﻔﺴﻪ ﻓﻲ ﺍﻟﺸﻜل ﻴﻤﺜل ﺍﻟﺠﺯﺀ ﻤﻥ ﺍﻟﺘﻭﺯﻴﻊ ﺍﻟﺘﻜﺭﺍﺭﻱ ﺍﻟﺫﻱ ﻴﻘﻊ ﺒﻴﻥ ﺍﻟﺭﺒﻴﻊ‬

‫ﺍﻷﻭل )ﺍﻟﻘﻴﻤﺔ ﺍﻟﺘﻲ ﻴﻘﻊ ﺩﻭﻨﻬﺎ ‪ 25%‬ﻤﻥ ﺍﻟﺒﻴﺎﻨﺎﺕ( ﻭﺍﻟﺭﺒﻴﻊ ﺍﻟﺜﺎﻟﺙ )ﺍﻟﻘﻴﻤﺔ ﺍﻟﺘﻲ ﻴﻘﻊ‬

‫ﺩﻭﻨﻬﺎ ‪ 75%‬ﻤﻥ ﺍﻟﺒﻴﺎﻨﺎﺕ(‪ ،‬ﻭﺃﻥ ﺍﻟﺨﻁ ﺍﻟﺜﻘﻴل ﺍﻷﻓﻘﻲ ﺒﺩﺍﺨل ﺍﻟﺼﻨﺩﻭﻕ ﻴﻤﺜل ﺍﻟﺭﺒﻴﻊ‬

‫ﺍﻟﺜﺎﻨﻲ ﺃﻱ ﺍﻟﻭﺴﻁ )ﺍﻟﻘﻴﻤﺔ ﺍﻟﺘﻲ ﻴﻘﻊ ﺩﻭﻨﻬﺎ ‪ 50%‬ﻤﻥ ﺍﻟﺒﻴﺎﻨﺎﺕ(‪ ،‬ﻭﺃﻥ ﺍﻟﺨﻁﺎﻥ ﺍﻷﻓﻘﻴﺎﻥ‬

‫ﺇﻟﻰ ﺃﻋﻠﻰ ﻭﺇﻟﻰ ﺃﺴﻔل ﺍﻟﺼﻨﺩﻭﻕ ﺘﻤﺜﻼﻥ ﺍﻟﻘﻴﻤﺘﻴﻥ ﺍﻟﻌﻅﻤﻰ ﻭﺍﻟﺼﻐﺭﻯ ﻋﻠﻰ ﺍﻟﺘﺭﺘﻴﺏ‬

‫ﻓﻲ ﺍﻟﺒﻴﺎﻨﺎﺕ ﻤﺴﺘﺒﻌﺩﹰﺍ ﻤﻨﻬﺎ ﺍﻟﻘﻴﻡ ﺍﻟﻤﺼﻨﻔﺔ ﻋﻠﻰ ﺃﻨﻬﺎ ﻗﻴﻤﹰﺎ ﺸﺎﺫﺓ ‪. outliers‬‬

‫ﻭﺘﻌﺭﻑ ﺍﻟﻘﻴﻡ ﺍﻟﺸﺎﺫﺓ ‪) outliers‬ﻭﺍﻟﺘﻲ ﻴﺭﻤﺯ ﻟﻬﺎ ﻓﻲ ﺸﻜل ﺼﻨﺩﻭﻕ ﺍﻻﻨﺘﺸﺎﺭ‬

‫ﺒﺎﻟﺭﻤﺯ ‪ ( o‬ﻋﻠﻰ ﺃﻨﻬﺎ ﺍﻟﻘﻴﻡ ﺍﻟﺘﻲ ﺘﺒﻌﺩ ﻋﻥ ﻁﺭﻑ ﺍﻟﺼﻨﺩﻭﻕ ﺍﻟﻘﺭﻴﺏ ﻤﻨﻬﺎ ﺒﻤﻘﺩﺍﺭ ﻁﻭل‬

‫ﺍﻟﺼﻨﺩﻭﻕ ﻤﺭﺓ ﻭﻨﺼﻑ )ﺃﻱ ﺒﻤﻘﺩﺍﺭ ‪ 150%‬ﻤﻥ ﻁﻭل ﺍﻟﺼﻨﺩﻭﻕ(‪ ،‬ﻭﺘﻌﺭﻑ ﺃﻴﻀﹰﺎ‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪177‬‬

‫ﺍﻟﻘﻴﻡ ﺍﻟﻤﺘﻁﺭﻓﺔ ﻓﻲ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪) extreme values‬ﻭﺍﻟﺘﻲ ﻴﺭﻤﺯ ﻟﻬﺎ ﻓﻲ ﺸﻜل ﺼﻨﺩﻭﻕ‬ ‫ﺍﻻﻨﺘﺸﺎﺭ ﺒﺎﻟﺭﻤﺯ * (ﻋﻠﻰ ﺃﻨﻬﺎ ﺍﻟﻘﻴﻡ ﺍﻟﺘﻲ ﺘﺒﻌﺩ ﻋﻥ ﻁﺭﻑ ﺍﻟﺼﻨﺩﻭﻕ ﺍﻟﻘﺭﻴﺏ ﻤﻨﻬﺎ‬

‫ﺒﻤﻘﺩﺍﺭ ﺜﻼﺜﺔ ﺃﻀﻌﺎﻑ ﻁﻭل ﺍﻟﺼﻨﺩﻭﻕ )ﺃﻱ ﺒﻤﻘﺩﺍﺭ ‪ 300%‬ﻤﻥ ﻁﻭل ﺍﻟﺼﻨﺩﻭﻕ(‪،‬‬ ‫ﻭﺘﺒﺩﻭ ﻓﻲ ﺍﻟﺸﻜل ﺒﺠﺎﻨﺏ ﺭﻤﻭﺯ ﺍﻟﻘﻴﻡ ﺍﻟﺸﺎﺫﺓ ﻭﺍﻟﻘﻴﻡ ﺍﻟﻤﺘﻁﺭﻓﺔ ﺒﻌﺽ ﺍﻷﺭﻗﺎﻡ ﺍﻟﺘﻲ ﺘﺸﻴﺭ‬

‫ﺇﻟﻰ ﺃﺭﻗﺎﻡ ﺍﻷﺴﻁﺭ ﺍﻟﺘﻲ ﺘﺤﺘﻭﻱ ﻋﻠﻰ ﺍﻟﻤﻔﺭﺩﺍﺕ ﻓﻲ ﺍﻟﺒﻴﺎﻨﺎﺕ‪ ،‬ﻭﻫﻲ ﺃﺭﻗﺎﻡ ﺍﻟﻤﻔﺭﺩﺍﺕ‬ ‫ﺍﻟﺘﻠﻘﺎﺌﻴﺔ ﺍﻟﺘﻲ ﻴﻀﻌﻬﺎ ﻤﺤﺭﺭ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪ Data Editor‬ﻭﻟﻴﺴﺕ ﺃﺭﻗﺎﻡ ﺍﻟﻤﻔﺭﺩﺍﺕ ﺍﻟﺘﻲ‬

‫ﻴﻀﻌﻬﺎ ﺍﻟﻤﺴﺘﺨﺩﻡ‪ ،‬ﻭﻴﻤﻜﻥ ﺘﻐﻴﻴﺭ ﺘﻠﻙ ﺍﻷﺭﻗﺎﻡ ﻋﻥ ﻁﺭﻴﻕ ﺘﺤﺩﻴﺩ ﺭﻗﻡ ﺃﻭ ﺭﻤﺯ ﺃﻭ‬ ‫ﺇﺸﺎﺭﺓ ﺃﺨﺭﻯ ﻴﺨﺘﺎﺭﻫﺎ ﺍﻟﻤﺴﺘﺨﺩﻡ ﻤﻥ ﺨﻼل ﻨﺎﻓﺫﺓ ﺍﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ ‪) Explore‬ﺸﻜل‬ ‫‪ (24-4‬ﺒﺘﺤﺩﻴﺩ ﻤﺘﻐﻴﺭ ﺠﺩﻴﺩ ﻴﻤﻴﺯ ﺍﻟﻘﻴﻡ ﺍﻟﻤﺨﺘﻠﻔﺔ ﻓﻲ ﺍﻟﺒﻴﺎﻨﺎﺕ ﻓﻲ ﻤﺭﺒﻊ ﺍﻟﺤﻭﺍﺭ‬

‫ﺍﻟﺨﺎﺹ ﺒﺘﻌﺭﻴﻑ ﺩﻟﻴل ﻟﻠﻤﻔﺭﺩﺍﺕ ‪ Label Cases by‬ﻋﻥ ﻁﺭﻴﻕ ﻤﺘﻐﻴﺭ ﻴﺘﻡ ﺘﺤﺩﻴﺩﻩ ﻓﻲ‬ ‫ﺍﻟﻤﺭﺒﻊ ﺍﻟﻤﻘﺎﺒل ﻓﻲ ﺃﺴﻔل ﺍﻟﻨﺎﻓﺫﺓ ﺍﻟﻤﺫﻜﻭﺭﺓ‪ ،‬ﻤﻥ ﻫﻨﺎ ﻴﺘﻀﺢ ﺃﻥ ﺃﺸﻜﺎل ﺼﻨﺎﺩﻴﻕ‬ ‫ﺍﻻﻨﺘﺸﺎﺭ ﺫﺍﺕ ﻓﺎﺌﺩﺓ ﻋﻅﻴﻤﺔ ﺨﺎﺼﺔ ﻓﻲ ﺍﻟﺘﻌﺭﻑ ﻋﻠﻰ ﺍﻟﻘﻴﻡ ﺍﻟﺸﺎﺫﺓ ﻭﺍﻟﻘﻴﻡ ﺍﻟﻤﺘﻁﺭﻓﺔ ﻓﻲ‬

‫ﺍﻟﺒﻴﺎﻨﺎﺕ‪.‬‬

‫‪ .5 .4‬اﻟﻤﻠﺨﺼﺎت واﻟﺘﻘﺎرﻳﺮ ‪Summaries and Reports :‬‬ ‫ﻫﻨﺎﻙ ﻤﺠﻤﻭﻋﺔ ﻤﻥ ﺍﻷﻭﺍﻤﺭ ﺘﺄﺘﻲ ﻀﻤﻥ ﻗﺎﺌﻤﺔ ﻤﻨﻔﺭﺩﺓ ﺒﺎﺴﻡ ﻗﺎﺌﻤﺔ ﺃﻭﺍﻤﺭ‬

‫ﺍﻟﺘﻘﺎﺭﻴﺭ ‪ Reports‬ﻓﻲ ﺍﻹﺼﺩﺍﺭ ‪ 11.0‬ﺒﻴﻨﻤﺎ ﻓﻲ ﺇﺼﺩﺍﺭ ‪ 8.0‬ﺘﺄﺘﻰ ﻀﻤﻥ ﻗﺎﺌﻤﺔ‬ ‫ﺃﻭﺍﻤﺭ ﺘﻠﺨﻴﺹ ‪ Summarize‬ﻜﻤﺎ ﻴﻅﻬﺭ ﻓﻲ ﺍﻟﺸﻜﻠﻴﻥ ‪ 1-4‬ﻭ ‪ 2-4‬ﺃﻋﻼﻩ‪ ،‬ﻭﻓﻲ‬ ‫ﺍﻟﺤﺎﻟﺘﻴﻥ ﻫﻨﺎﻙ ‪ 4‬ﺃﻭﺍﻤﺭ ﺘﻬﺘﻡ ﺒﺈﻋﺩﺍﺩ ﺍﻟﻤﻠﺨﺼﺎﺕ ﻭﺍﻟﺘﻘﺎﺭﻴﺭ‬

‫‪Summaries and‬‬

‫‪ Reports‬ﺴﻨﻬﺘﻡ ﺒﻬﺎ ﻫﻨﺎ‪ ،‬ﻭﻫﺫﻩ ﺍﻷﻭﺍﻤﺭ ﻫﻲ ‪:‬‬ ‫• ﻤﻠﺨﺹ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ‬

‫• ﺘﻘﺭﻴﺭ ﻤﻠﺨﺹ ﻓﻲ ﺍﻟﺼﻔﻭﻑ‬ ‫•‬

‫‪Case Summaries‬‬ ‫‪Report Summaries In Rows‬‬

‫ﺘﻘﺭﻴﺭ ﻤﻠﺨﺹ ﻓﻲ ﺍﻷﻋﻤﺩﺓ ‪Report Summaries In Columns‬‬

‫• ﺘﻘﺎﺭﻴﺭ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻔﻭﺭﻱ‬

‫)‪Layered Reports (OLAP Cubes‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪178‬‬

‫ﻭﻴﺠﺩﺭ ﺒﺎﻟﺫﻜﺭ ﺃﻥ ﻫﺫﻩ ﺍﻷﻭﺍﻤﺭ ﻻ ﺘﻘﺩﻡ ﺃﻱ ﻤﻌﻠﻭﻤﺎﺕ ﺇﻀﺎﻓﻴﺔ ﻋﻥ ﺍﻟﻤﻌﻠﻭﻤﺎﺕ‬ ‫ﺍﻟﺘﻲ ﺘﻘﺩﻤﻬﺎ ﻤﺠﻤﻭﻋﺔ ﺍﻷﻭﺍﻤﺭ ﺍﻟﺘﻲ ﺘﻡ ﺘﻘﺩﻴﻤﻬﺎ ﻓﻲ ﺍﻟﻘﺴﻡ ﺍﻟﺴﺎﺒﻕ ﻤﻥ ﻫﺫﻩ ﺍﻟﻭﺤﺩﺓ‬ ‫ﺍﻟﺨﺎﺼﺔ ﺒﺎﺴﺘﻜﺸﺎﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‪ ،‬ﻟﺫﺍ ﻓﺈﻨﻨﺎ ﺴﻨﺘﻌﺭﺽ ﻟﻬﺫﻩ ﺍﻷﻭﺍﻤﺭ ﺒﺎﺨﺘﺼﺎﺭ‪.‬‬ ‫ﺃﻤﺭ ﺘﻠﺨﻴﺹ ﺍﻟﻤﻔﺭﺩﺍﺕ ‪: Case Summaries‬‬ ‫ﻭﻫﺫﺍ ﺍﻷﻤﺭ ﻴﻘﻭﻡ ﺒﺤﺴﺎﺏ ﺃﺤﺩ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ﺃﻭ ﻤﺠﻤﻭﻋﺔ ﻤﻨﻬﺎ‬

‫ﻟﻠﻤﺠﻤﻭﻋﺎﺕ ﺍﻟﺠﺯﺌﻴﺔ ﻤﻥ ﺒﻴﺎﻨﺎﺕ ﻤﺘﻐﻴﺭ ﻤﻘﺴﻡ ﺤﺴﺏ ﻓﺌﺎﺕ ﻤﺘﻐﻴﺭ ﺘﺼﻨﻴﻑ ﻭﺍﺤﺩ ﺁﺨﺭ‬

‫ﺃﻭ ﺃﻜﺜﺭ‪ ،‬ﻭﻴﻘﻭﻡ ﺒﻌﺭﺽ ﻨﺘﺎﺌﺞ ﺍﻟﺤﺴﺎﺏ ﻟﻜل ﻤﺴﺘﻭﻯ ﻤﻥ ﻤﺴﺘﻭﻴﺎﺕ ﺍﻟﻤﺘﻐﻴﺭ ﻭﻜﺫﻟﻙ‬ ‫ﺒﺸﻜل ﺇﺠﻤﺎﻟﻲ ﻓﻲ ﺠﺩﻭل ﻤﺭﻜﺏ‪ ،‬ﻭﻴﻤﻜﻥ ﺍﺨﺘﻴﺎﺭ ﻁﺭﻴﻘﺔ ﺘﺭﺘﻴﺏ ﻋﺭﺽ ﺍﻹﺤﺼﺎﺀﺍﺕ‬

‫ﺍﻟﻤﺨﺘﻠﻔﺔ‪ ،‬ﻭﻴﻤﻜﻥ ﻋﺭﺽ ﺃﻭ ﺇﻴﻘﺎﻑ ﻋﺭﺽ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ ﻨﻔﺴﻬﺎ ﺃﻭ ﺠﺯﺀ ﻤﻨﻬﺎ ﻓﻲ‬ ‫ﺍﻟﺘﻘﺭﻴﺭ‪ ،‬ﻓﻌﻨﺩﻤﺎ ﻴﻜﻭﻥ ﻋﺩﺩ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ ﻜﺒﻴﺭ ﻴﻤﻜﻨﻙ ﻋﺭﺽ ﺠﺯﺀ ﻓﻘﻁ ﻤﻨﻬﺎ‪ ،‬ﻭﺃﻫﻤﻴﺔ‬

‫ﻫﺫﺍ ﺍﻷﻤﺭ ﺃﻨﻪ ﻴﻤﻜﻥ ﻤﻥ ﺨﻼﻟﻪ ﺤﺴﺎﺏ ﺠﻤﻴﻊ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ﺍﻟﺘﻲ ﺘﺴﺘﻁﻴﻊ‬ ‫ﺍﻷﻭﺍﻤﺭ ﺍﻷﺨﺭﻯ ﺤﺴﺎﺒﻬﺎ ﻭﻋﺭﻀﻬﺎ ﻓﻲ ﺸﻜل ﺠﺩﻭل ﻤﺭﻜﺏ ﺤﺴﺏ ﻓﺌﺎﺕ ﻤﺘﻐﻴﺭﺍﺕ‬

‫ﻻ ﻋﻠﻰ ﺫﻟﻙ ﺘﻡ ﺘﻁﺒﻴﻘﻪ ﻋﻠﻰ ﻨﻔﺱ‬ ‫ﺘﺼﻨﻴﻑ ﺃﺨﺭﻯ‪ ،‬ﻭﺸﻜل ﺭﻗﻡ ‪ 29-4‬ﻴﻭﻀﺢ ﻤﺜﺎ ﹰ‬ ‫ﺒﻴﺎﻨﺎﺕ ﻤﻠﻑ ‪ employee data‬ﺍﻟﺘﻲ ﺍﺴﺘﺨﺩﻤﺕ ﻓﻲ ﺍﻷﻭﺍﻤﺭ ﺍﻟﺴﺎﺒﻘﺔ‪.‬‬

‫ﺸﻜل ‪ : 29-4‬ﺍﺴﺘﺨﺭﺍﺝ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ﻟﻤﺩﺓ ﺍﻟﺨﺩﻤﺔ ﻓﻲ ﻤﻠﻑ ‪employee data‬‬ ‫ﺒﺎﺴﺘﺨﺩﺍﻡ ﺃﻤﺭ ﺘﻠﺨﻴﺹ ﺍﻟﻤﻔﺭﺩﺍﺕ ‪Case Summarize‬‬ ‫‪Example on Case Summaries‬‬ ‫)‪Previous Experience (months‬‬ ‫‪Geometric Harmonic‬‬ ‫‪Mean‬‬ ‫‪Mean Std. DeviationSkewness Kurtosis‬‬ ‫‪a‬‬

‫‪.00 Missing‬‬

‫‪2.461‬‬

‫‪1.667‬‬

‫‪95.27‬‬

‫‪-1.127‬‬

‫‪.014‬‬

‫‪101.43‬‬

‫‪.817‬‬

‫‪1.276‬‬

‫‪73.26‬‬

‫‪26.43‬‬

‫‪1.696‬‬

‫‪1.510‬‬

‫‪104.59‬‬

‫‪a‬‬

‫‪Median‬‬

‫‪N‬‬

‫‪Mean‬‬

‫‪50.00‬‬

‫‪85.04‬‬

‫‪363‬‬

‫‪261.30‬‬

‫‪280.08‬‬

‫‪305.00‬‬

‫‪298.11‬‬

‫‪27‬‬

‫‪47.47‬‬

‫‪52.00‬‬

‫‪77.62‬‬

‫‪84‬‬

‫‪55.00‬‬

‫‪95.86‬‬

‫‪474‬‬

‫‪.00 Missing‬‬

‫‪Employment Catego‬‬ ‫‪Clerical‬‬ ‫‪Custodial‬‬ ‫‪Manager‬‬ ‫‪Total‬‬

‫‪a. The data contains both negative and positive values, and possibly zero values.‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪179‬‬

‫ﺃﻤﺭ ﺘﻘﺭﻴﺭ ﻤﻠﺨﺹ ﻓﻲ ﺍﻟﺼﻔﻭﻑ ‪: Report Summaries in Rows‬‬ ‫ﻴﻘﻭﻡ ﺃﻤﺭ ﺘﻘﺭﻴﺭ ﻤﻠﺨﺹ ﻓﻲ ﺍﻟﺼﻔﻭﻑ ‪Report Summaries in Rows‬‬

‫ﺒﺎﺴﺘﺨﺭﺍﺝ ﺘﻘﺭﻴﺭ ﻋﻥ ﺠﻤﻴﻊ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ﻭﺘﻠﺨﻴﺼﻬﺎ ﻓﻲ ﺼﻔﻭﻑ‪ ،‬ﻜﻤﺎ ﻴﻤﻜﻥ‬ ‫ﻤﻥ ﺨﻼل ﻫﺫﺍ ﺍﻷﻤﺭ ﻋﺭﺽ ﺃﻭ ﺇﻴﻘﺎﻑ ﻋﺭﺽ ﺍﻟﻤﺸﺎﻫﺩﺍﺕ ﻨﻔﺴﻬﺎ ﺃﻭ ﺠﺯﺀ ﻤﻨﻬﺎ ﻓﻲ‬

‫ﻼ ﻤﻥ ﺨﻼل ﻫﺫﺍ ﺍﻷﻤﺭ‬ ‫ﺍﻟﺘﻘﺭﻴﺭ ﺇﻀﺎﻓﺔ ﺇﻟﻰ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ﺃﻭ ﺒﺩﻭﻨﻬﺎ‪ ،‬ﻓﻴﻤﻜﻥ ﻤﺜ ﹰ‬

‫ﻋﺭﺽ ﺠﻤﻴﻊ ﺒﻴﺎﻨﺎﺕ ﺍﻟﻤﻔﺭﺩﺍﺕ ﻓﻲ ﻤﻠﻑ ﻤﻌﻴﻥ ﻤﻘﺴﻤﺔ ﺤﺴﺏ ﻓﺌﺎﺕ ﻤﺘﻐﻴﺭﺍﺕ ﺘﺼﻨﻴﻑ‬ ‫ﻤﺤﺩﺩﺓ ﺒﻤﺎ ﻓﻲ ﺫﻟﻙ ﺃﻱ ﻋﺩﺩ ﻤﻥ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ﻟﻅﻭﺍﻫﺭ ﻤﻌﻴﻨﺔ ‪ ،‬ﻭﻴﺠﺏ ﺃﻥ‬

‫ﺘﻜﻭﻥ ﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺘﺼﻨﻴﻑ ﻓﻲ ﻫﺫﻩ ﺍﻟﺤﺎﻟﺔ ﻤﺘﻐﻴﺭﺍﺕ ﻭﺼﻔﻴﺔ ﺃﻭ ﻜﻤﻴﺔ ﻤﺘﻘﻁﻌﺔ ﺫﺍﺕ ﻋﺩﺩ‬ ‫ﻤﺤﺩﻭﺩ ﻭﺼﻐﻴﺭ ﻤﻥ ﺍﻟﻘﻴﻡ‪ ،‬ﻭﻴﻤﻜﻥ ﺍﻟﺘﺤﻜﻡ ﻓﻲ ﺨﻭﺍﺹ ﺍﻟﺘﻘﺭﻴﺭ ﺍﻟﺫﻱ ﻴﺘﻡ ﺍﺴﺘﺨﺭﺍﺠﻪ ﺒﻤﺎ‬

‫ﻓﻲ ﺫﻟﻙ ﺇﻤﻜﺎﻨﻴﺔ ﺍﻟﺤﺼﻭل ﻋﻠﻰ ﻤﻘﺎﻴﻴﺱ ﺇﺤﺼﺎﺌﻴﺔ ﻋﻥ ﺍﻟﻤﺠﻤﻭﻋﺎﺕ ﺍﻟﺠﺯﺌﻴﺔ‬

‫ﻭﻤﺠﻤﻭﻋﺔ ﺍﻟﺒﻴﺎﻨﺎﺕ ﺍﻟﻜﻠﻴﺔ ﻭﻜﺫﻟﻙ ﻁﺭﻴﻘﺔ ﻋﺭﺽ ﺍﻟﻘﻴﻡ ﺍﻟﻤﻔﻘﻭﺩﺓ ﻭﺍﻟﻌﻨﺎﻭﻴﻥ ﺒﺎﻹﻀﺎﻓﺔ‬

‫ﺇﻟﻰ ﺍﻟﻌﻨﺎﻭﻴﻥ ﺍﻟﺠﺎﻨﺒﻴﺔ ﻭﺃﺭﻗﺎﻡ ﺍﻟﺼﻔﺤﺎﺕ ﻓﻲ ﺍﻟﺘﻘﺭﻴﺭ ‪ ،‬ﻭﺸﻜل ﺭﻗﻡ ‪ 30-4‬ﻴﻭﻀﺢ‬ ‫ﻻ ﻋﻠﻰ ﺫﻟﻙ ﺘﻡ ﺘﻁﺒﻴﻘﻪ ﻋﻠﻰ ﻨﻔﺱ ﺒﻴﺎﻨﺎﺕ ﻤﻠﻑ ‪ employee data‬ﺍﻟﺘﻲ ﺍﺴﺘﺨﺩﻤﺕ‬ ‫ﻤﺜﺎ ﹰ‬

‫ﻓﻲ ﺍﻷﻭﺍﻤﺭ ﺍﻟﺴﺎﺒﻘﺔ‪ ،‬ﻭﻴﻘﻭﻡ ﺍﻷﻤﺭ ﺒﺘﺭﺘﻴﺏ ﺍﻟﺒﻴﺎﻨﺎﺕ ﻓﻲ ﺍﻟﻤﻠﻑ ﻓﻲ ﺍﻷﺠﺯﺍﺀ ﺍﻟﻤﺨﺘﻠﻔﺔ‬ ‫ﺤﺴﺏ ﺍﻟﻔﺌﺎﺕ ﺍﻟﻤﺨﺘﻠﻔﺔ ﻓﻲ ﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺘﺼﻨﻴﻑ ‪ ،‬ﻭﺇﺫﺍ ﻜﺎﻥ ﺍﻟﻤﻠﻑ ﻤﺭﺘﺒﹰﺎ ﻤﻥ ﻋﻤﻠﻴﺎﺕ‬

‫ﺴﺎﺒﻘﺔ ﻓﻴﻤﻜﻥ ﺤﻔﻅ ﺍﻟﺘﺭﺘﻴﺏ ﻟﺘﻭﻓﻴﺭ ﻭﻗﺕ ﺍﺴﺘﺨﺭﺍﺝ ﺍﻟﺘﻘﺭﻴﺭ‪.‬‬

‫( ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬4)

180

employee data ‫ ﺍﺴﺘﺨﺭﺍﺝ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ﻟﻤﺩﺓ ﺍﻟﺨﺩﻤﺔ ﻓﻲ ﻤﻠﻑ‬: 30-4 ‫ﺸﻜل‬ Report Summaries in Rows ‫ﺒﺎﺴﺘﺨﺩﺍﻡ ﺃﻤﺭ ﺘﻘﺭﻴﺭ ﻤﻠﺨﺹ ﻓﻲ ﺍﻟﺼﻔﻭﻑ‬ Page ‫ﻧﺘﺎﺋﺞ‬ ‫اﻷﻣﺮ‬ ‫ﻣﻠﺨﺺ‬ ‫اﻟﺴﻄﻮر‬ _________

Previous Experience (months) __________

Clerical Mean N StdDev Kurtosis Skewness

85 363 95 2.46 1.67

Custodial Mean N StdDev Kurtosis Skewness

298 27 101 -1.13 .01

Manager Mean N StdDev Kurtosis Skewness

78 84 73 .82 1.28

Grand Total Mean Minimum Maximum N StdDev Kurtosis Skewness

96 0 476 474 105 1.70 1.51

1

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪181‬‬

‫ﺃﻤﺭ ﺘﻘﺭﻴﺭ ﻤﻠﺨﺹ ﻓﻲ ﺍﻷﻋﻤﺩﺓ ‪: Report Summaries in Columns‬‬ ‫ﻴﻘﻭﻡ ﺃﻤﺭ ﺘﻘﺭﻴﺭ ﻤﻠﺨﺹ ﻓﻲ ﺍﻷﻋﻤﺩﺓ ‪Report Summaries in Columns‬‬

‫ﺃﻴﻀﹰﺎ ﺒﺎﺴﺘﺨﺭﺍﺝ ﺘﻘﺭﻴﺭ ﻋﻥ ﺠﻤﻴﻊ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ﺇﻻ ﺃﻨﻪ ﻴﻠﺨﺼﻬﺎ ﻓﻲ ﺃﻋﻤﺩﺓ‬ ‫ﻤﺴﺘﻘﻠﺔ‪ ،‬ﻓﻴﻤﻜﻥ ﻤﻥ ﺨﻼل ﻫﺫﺍ ﺍﻷﻤﺭ ﻋﺭﺽ ﺃﻱ ﻋﺩﺩ ﻤﻥ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ‬

‫ﻟﻅﻭﺍﻫﺭ ﻤﻌﻴﻨﺔ ﻓﻲ ﺃﻋﻤﺩﺓ ﻤﺨﺘﻠﻔﺔ ﻭﺫﻟﻙ ﺤﺴﺏ ﺍﻷﻗﺴﺎﻡ ﺍﻟﻤﻌﺭﻓﺔ ﺒﻔﺌﺎﺕ ﻤﺘﻐﻴﺭﺍﺕ‬

‫ﺘﺼﻨﻴﻑ ﻤﻌﻴﻨﺔ‪ ،‬ﻭﻴﺠﺏ ﺃﻥ ﺘﻜﻭﻥ ﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺘﺼﻨﻴﻑ ﻓﻲ ﻫﺫﻩ ﺍﻟﺤﺎﻟﺔ ﻤﺘﻐﻴﺭﺍﺕ ﻭﺼﻔﻴﺔ‬

‫ﺃﻭ ﻜﻤﻴﺔ ﻤﺘﻘﻁﻌﺔ ﺫﺍﺕ ﻋﺩﺩ ﻤﺤﺩﻭﺩ ﻭﺼﻐﻴﺭ ﻤﻥ ﺍﻟﻘﻴﻡ‪ ،‬ﻭﻴﻤﻜﻥ ﺍﻟﺘﺤﻜﻡ ﻓﻲ ﺨﻭﺍﺹ‬ ‫ﺍﻟﺘﻘﺭﻴﺭ ﺍﻟﺫﻱ ﻴﺘﻡ ﺍﺴﺘﺨﺭﺍﺠﻪ ﺒﻤﺎ ﻓﻲ ﺫﻟﻙ ﺇﻤﻜﺎﻨﻴﺔ ﺍﻟﺤﺼﻭل ﻋﻠﻰ ﻤﻘﺎﻴﻴﺱ ﺇﺤﺼﺎﺌﻴﺔ‬

‫ﻋﻥ ﺍﻟﻤﺠﻤﻭﻋﺎﺕ ﺍﻟﺠﺯﺌﻴﺔ ﻭﻤﺠﻤﻭﻋﺔ ﺍﻟﺒﻴﺎﻨﺎﺕ ﺍﻟﻜﻠﻴﺔ ﻭﻜﺫﻟﻙ ﻁﺭﻴﻘﺔ ﻋﺭﺽ ﺍﻟﻘﻴﻡ‬ ‫ﺍﻟﻤﻔﻘﻭﺩﺓ ﻭﺍﻟﻌﻨﺎﻭﻴﻥ ﺒﺎﻹﻀﺎﻓﺔ ﺇﻟﻰ ﺍﻟﻌﻨﺎﻭﻴﻥ ﺍﻟﺠﺎﻨﺒﻴﺔ ﻭﺃﺭﻗﺎﻡ ﺼﻔﺤﺎﺕ ﺍﻟﺘﻘﺭﻴﺭ ‪ ،‬ﻭﺸﻜل‬

‫ﻻ ﻋﻠﻰ ﺫﻟﻙ ﺘﻡ ﺘﻁﺒﻴﻘﻪ ﻋﻠﻰ ﻨﻔﺱ ﺒﻴﺎﻨﺎﺕ ﻤﻠﻑ ‪employee data‬‬ ‫‪ 31-4‬ﻴﻭﻀﺢ ﻤﺜﺎ ﹰ‬

‫ﺍﻟﺘﻲ ﺍﺴﺘﺨﺩﻤﺕ ﻓﻲ ﺍﻷﻭﺍﻤﺭ ﺍﻟﺴﺎﺒﻘﺔ‪ ،‬ﻭﻴﻘﻭﻡ ﺍﻷﻤﺭ ﺒﺘﺭﺘﻴﺏ ﺍﻟﺒﻴﺎﻨﺎﺕ ﻓﻲ ﺍﻟﻤﻠﻑ ﻓﻲ‬

‫ﺍﻷﺠﺯﺍﺀ ﺍﻟﻤﺨﺘﻠﻔﺔ ﺤﺴﺏ ﺍﻟﻔﺌﺎﺕ ﺍﻟﻤﺨﺘﻠﻔﺔ ﻓﻲ ﻤﺘﻐﻴﺭﺍﺕ ﺍﻟﺘﺼﻨﻴﻑ ‪ ،‬ﻭﺇﺫﺍ ﻜﺎﻥ ﺍﻟﻤﻠﻑ‬

‫ﻤﺭﺘﺒﹰﺎ ﻤﻥ ﻋﻤﻠﻴﺎﺕ ﺴﺎﺒﻘﺔ ﻓﻴﻤﻜﻥ ﺤﻔﻅ ﺍﻟﺘﺭﺘﻴﺏ ﻟﺘﻭﻓﻴﺭ ﻭﻗﺕ ﺍﺴﺘﺨﺭﺍﺝ ﺍﻟﺘﻘﺭﻴﺭ‪.‬‬

‫ﺸﻜل ‪ : 31-4‬ﺍﺴﺘﺨﺭﺍﺝ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ﻟﻤﺩﺓ ﺍﻟﺨﺩﻤﺔ ﻓﻲ ﻤﻠﻑ ‪employee data‬‬ ‫ﺒﺎﺴﺘﺨﺩﺍﻡ ﺃﻤﺭ ﺘﻘﺭﻴﺭ ﻤﻠﺨﺹ ﻓﻲ ﺍﻷﻋﻤﺩﺓ ‪Report Summaries in Columns‬‬ ‫‪1‬‬

‫‪Page‬‬

‫‪Previous‬‬ ‫‪Experience‬‬ ‫)‪(months‬‬ ‫‪Mean‬‬ ‫__________‬

‫‪Employment‬‬ ‫‪Category‬‬ ‫__________‬

‫‪85‬‬

‫‪Clerical‬‬

‫‪298‬‬

‫‪Custodial‬‬

‫‪78‬‬

‫‪Manager‬‬

‫‪96‬‬

‫‪Grand Total‬‬

‫)‪ (4‬ﻋﻤﻠﻴﺎﺕ ﻭﺼﻑ ﺍﻟﺒﻴﺎﻨﺎﺕ‬

‫‪182‬‬

‫ﺃﻤﺭ ﺘﻘﺎﺭﻴﺭ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻔﻭﺭﻱ ‪: OLAP Cubes‬‬ ‫ﻴﻘﻭﻡ ﺃﻤﺭ ﺘﻘﺎﺭﻴﺭ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻔﻭﺭﻱ ‪ OLAP Cubes‬ﻭﻫﻭ ﺍﺨﺘﺼﺎﺭﺍ ﻟﻠﺘﻌﺒﻴﺭ‬

‫‪ Online Analytical Processing‬ﺒﺤﺴﺎﺏ ﺍﻟﻤﺠﺎﻤﻴﻊ ﻭﺍﻟﻤﺘﻭﺴﻁﺎﺕ ﻭﺍﻟﻤﻘﺎﻴﻴﺱ‬ ‫ﺍﻷﺨﺭﻯ ﻟﻤﺘﻐﻴﺭﺍﺕ ﻜﻤﻴﺔ ﻤﺘﺼﻠﺔ ﻭﻤﻭﺯﻋﺔ ﺤﺴﺏ ﻓﺌﺎﺕ ﻤﺘﻐﻴﺭﺍﺕ ﺘﺼﻨﻴﻑ ﺃﺨﺭﻯ‬

‫ﻼ ﻟﻜل ﻤﺠﻤﻭﻋﺔ ﻤﻥ ﻤﺠﻤﻭﻋﺎﺕ ﺫﻟﻙ‬ ‫ﻻ ﻤﺴﺘﻘ ﹰ‬ ‫)ﻤﺘﻐﻴﺭ ﻭﺍﺤﺩ ﺃﻭ ﺍﻜﺜﺭ( ﻭﺇﻋﻁﺎﺀ ﺠﺩﻭ ﹰ‬ ‫ﺍﻟﻤﺘﻐﻴﺭ ﺍﻟﺫﻱ ﺘﻡ ﺘﺼﻨﻴﻔﻪ ﻭﺫﻟﻙ ﺒﺴﻬﻭﻟﺔ ﻭﻴﺴﺭ‪ ،‬ﻭﺘﻅﻬﺭ ﺍﻟﺠﺩﺍﻭل ﺍﻟﻤﺨﺘﻠﻔﺔ ﻋﻠﻰ ﺸﻜل‬ ‫ﻻ ﻭﺍﺤﺩﹰﺍ ﻭﺒﺎﻗﻲ ﺍﻟﺠﺩﺍﻭل ﺘﻅﻬﺭ ﻓﻘﻁ ﺒﺎﻟﻨﻘﺭ ﻋﻠﻰ‬ ‫ﻤﻜﻌﺒﺎﺕ ﻓﻭﻕ ﺒﻌﻀﻬﺎ‪ ،‬ﻓﻴﻅﻬﺭ ﺠﺩﻭ ﹰ‬

‫ﺍﻟﺠﺩﻭل ﺒﺎﻟﻔﺄﺭﺓ‪ ،‬ﻜﻤﺎ ﺃﻥ ﻫﺫﺍ ﺍﻷﻤﺭ ﻴﺘﻴﺢ ﺤﺴﺎﺏ ﻗﺎﺌﻤﺔ ﻁﻭﻴﻠﺔ ﻤﻥ ﺍﻟﻤﻘﺎﻴﻴﺱ‬ ‫ﺍﻹﺤﺼﺎﺌﻴﺔ‪.‬‬

‫ﻭﺍﻟﻨﺘﺎﺌﺞ ﺍﻟﺘﺎﻟﻴﺔ )ﺸﻜل ‪ (32-4‬ﺘﻡ ﺍﺴﺘﺨﺭﺍﺠﻬﺎ ﺒﺘﻁﺒﻴﻕ ﺃﻤﺭ ﺘﻘﺎﺭﻴﺭ ﺍﻟﺘﺤﻠﻴل‬

‫ﺍﻟﻔﻭﺭﻱ ‪ OLAP Cubes‬ﻋﻠﻰ ﺒﻴﺎﻨﺎﺕ ﻤﻠﻑ ‪ employee data‬ﺍﻟﺴﺎﺒﻕ ﺍﺴﺘﺨﺩﺍﻤﻪ‪.‬‬

‫ﺸﻜل ‪ : 32-4‬ﺍﺴﺘﺨﺭﺍﺝ ﺍﻟﻤﻘﺎﻴﻴﺱ ﺍﻹﺤﺼﺎﺌﻴﺔ ﻟﻤﺩﺓ ﺍﻟﺨﺩﻤﺔ ﻓﻲ ﻤﻠﻑ ‪employee data‬‬ ‫ﺒﺎﺴﺘﺨﺩﺍﻡ ﺃﻤﺭ ﺘﻘﺎﺭﻴﺭ ﺍﻟﺘﺤﻠﻴل ﺍﻟﻔﻭﺭﻱ ‪OLAP Cubes‬‬ ‫‪OLAP Cubes‬‬ ‫‪Employment Category: Clerical‬‬ ‫‪Kurtosis‬‬ ‫‪2.461‬‬

‫‪Skewness‬‬

‫‪Std. Deviation‬‬

‫‪1.667‬‬

‫‪95.27‬‬

‫‪Skewness‬‬

‫‪Std. Deviation‬‬

‫‪.014‬‬

‫‪101.43‬‬

‫‪Skewness‬‬

‫‪Std. Deviation‬‬

‫‪1.276‬‬

‫‪73.26‬‬

‫‪Mean‬‬ ‫‪85.04‬‬

‫‪% of Total N‬‬ ‫‪76.6%‬‬

‫‪N‬‬ ‫‪Previous Experience‬‬ ‫)‪(months‬‬

‫‪363‬‬

‫‪OLAP Cubes‬‬ ‫‪Employment Category: Custodial‬‬ ‫‪Kurtosis‬‬ ‫‪-1.127‬‬

‫‪Mean‬‬ ‫‪298.11‬‬

‫‪% of Total N‬‬ ‫‪5.7%‬‬

‫‪N‬‬ ‫‪Previous Experience‬‬ ‫)‪(months‬‬

‫‪27‬‬

‫‪OLAP Cubes‬‬ ‫‪Employment Category: Manager‬‬ ‫‪Kurtosis‬‬ ‫‪.817‬‬

‫‪Mean‬‬ ‫‪77.62‬‬

‫‪% of Total N‬‬ ‫‪17.7%‬‬

‫‪N‬‬ ‫‪84‬‬

‫‪Previous Experience‬‬ ‫)‪(months‬‬