]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:
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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
140
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
141
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
142
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
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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
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.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
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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
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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
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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