Phân tích và xử lý số liệu bằng SPSS

Nội dung text: Phân tích và xử lý số liệu bằng SPSS

1. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam TO CHC C S D’ LIEU I. KIEM TRA VA M A’ HOA BANG CAU HOI 1. Ki !m tra ay la b c kiem tra chat l ng bang cau hoi nham bao ẩam khong co bang cau hoi nao thieu thong tin can thiet theo yeu cau thiet ke ban ẩau, b c nay can thiet ẩ c th c hie n tr c khi tien hanh ma! hoa va nha p d ! lie u vao may t%nh. Ng i kiem tra phai bao ẩam t%nh toan ven va t%nh ch%nh (ac cua t ng bang cau hoi ) t ng cau tra l i. Thong th ng kiem tra bang cau hoi la kiem tra nh !ng ẩa+c t%nh sau cua bang cau hoi- • T%nh logic cua cac cau tra l i • T%nh ẩay ẩu cua mo t cau tra l i va cua mo t bang cau hoi • T%nh h p ly va (ac th c cua cac cau tra l i Th nhat la t%nh logic cua cac cau tra l i trong mo t bang cau hoi. oi khi trong bang cau hoi, do yeu cau nghien c u se! co nh !ng ẩ ng da.n, nh !ng ẩieu kie n ẩe. ng i tra l i hoa+c co the tra l i tat ca cac cau hoi hoa+c co the bo /ua mo t vai cau hoi nao ẩo. 0iem tra t%nh logic cua bang cau hoi cho phep nha nghien c u loai bo nh !ng cau tra l i th a, cu!ng nh k1p th i bo sung nh !ng phan thieu trong bang cau hoi. T%nh logic cua cau tra l i con phu thuo c vao s  ket noi va lien he la.n nhau gi !a cac cau hoi trong mo t bang cau hoi, da.n ẩen ẩoi khi mo t cau tra l i la co y ngh2a neu ẩ ng rieng mo t m3nh no nh ng lai vo ngh2a neu ket h p so sanh v i cac cau tra l i tr c hoa+c sau no Th hai la kiem tra t%nh ẩay ẩu cua mo t cau tra l i va cua mo t bang cau hoi. 4o t cau hoi trong nghien c u th1 tr ng ẩoi khi bao gom nhieu cau hoi nho, nh ẩoi v i cau hoi ve m c ẩo ẩong y ẩoi v i nh !ng cau noi the hie n nh !ng ẩa+c t%nh ve mo t san pham cu the nao ẩo se! bao gom nhieu ẩa+c t%nh khac nhau, neu nh thieu mo t ẩanh gia ve mo t ẩa+c t%nh nao ẩo th3 co the (em nh cau tra l i ẩo khong hoan ch5nh. 6ay ẩoi v i cac cau hoi m , ẩoi khi ng i tra l i do khong hieu ro! cau hoi, hay do s  thieu nhie t t3nh cua ng i tra l i, cau tra l i cua ho th ng rat chung chung va vo ngh2a ẩoi v i muc tieu nghien c u. 0h%a canh khac la t%nh ẩay ẩu cua bang cau hoi, mo t bang cau hoi ch5 co gia tr1 neu nh tat ca nh !ng cau hoi theo yeu cau ẩeu ẩ c tra l i ẩay ẩu. 4o.i cau hoi trong bang cau hoi ẩeu co mo t gia tr1 nhat ẩ1nh, do ẩo thieu mo t cau tra l i cho mo t cau hoi nao ẩo se! lam mat ẩi gia tr1 cua bang cau hoi Cuoi cung la kiem tra t%nh h p ly va (ac th c cua cau tra l i. 4o t cau tra l i ẩay ẩu ch a ha8n la cau tra l i co gia tr1, do t%nh chan th c va h p ly cua cau tra l i cu!ng /uyet ẩ1nh ẩen gia tr1 cua cau tra l i va cua bang cau hoi, ẩa+c bie t la cac cau hoi cham ẩiem, cau hoi m  va cac cau hoi mang t%nh logic
2. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam 9ua tr3nh kiem tra, ra soat lai bang cau hoi la nham muc ẩ%ch kiem tra, phat hie n, s a ch !a va thong bao k1p th i cho ng i thu tha p d ! lie u tranh nh !ng sai sot tiep theo. 4o t so sai sot th ng ga+p nh sau- • Nh !ng cuo c phong van gia tao • Nh !ng cau tra l i khong ẩay ẩu • Nh !ng cau tra l i thieu nhat /uan • Nh !ng cau tra l i khong th%ch h p • Nh !ng cau tra l i khong ẩoc ẩ c e (  ly cac lo.i trong kiem tra va hie u ẩ%nh, ta co the l a chon cach (  ly nh sau tuy thuo c vao m c ẩo sai sot cu the- • Tra ve cho bo pha n thu tha p d ! lie u ẩe lam sang to van ẩe • Suy lua n t  cac cau tra l i khac • Loai bo toan bo bang cau hoi 2. M a' ho*a La /ua tr3nh chuyen d1ch cau tra l i th c cua ng i tra l i vao t ng nhom, t ng ma.u ẩai die n v i cac gia tr1 t ng ng nham lam cho /ua tr3nh tom ta n cac cau tra l i t  tr c, ng i tra l i ch5 vie c l a chon cau tra l i nao phu h p nhat, do ẩo vie c ma! hoa cho cac cau hoi nay th ng ẩ c tien hanh t  tr c,  giai ẩoan thiet ke bang cau hoi • 4a! hoa- Trong bang cau hoi ngoai nh !ng cau hoi ẩong neu  tren, con nh !ng cau hoi m , la nh !ng cau hoi ma nha nghien c u ẩe cac khoang trong trong bang cau hoi cho ng i tra l i t  do ẩ a ra cau tra l i theo suy ngh2 va die.n gia!i cua ch%nh ho. oi v i cac cau hoi m , do cac cau tra l i khong ẩ c lie t ke tr c, nen kho (ac ẩ1nh ẩ c cac cau tra l i th c cua ng i tra l i. Cac bang cau hoi nha n ve th ng co nh !ng cau tra l i rat khac nhau, rat ẩa dang. Do ẩo cong vie c ma! hoa nh !ng cau tra l i nay la can thiet cho /ua tr3nh kiem tra, nha p lie u, tom ta<t va phan t%ch sau nay 4uc ẩ%ch cua ma! hoa la tao nha!n cho cac cau tra l i, th  ng la bang cac con so. 4a! hoa con giup gia!m thieu so l ng cac cau tra l i bang cach nhom cac cau tra l i thanh nh !ng nhom co nh !ng ẩa+c ẩiem giong nhau, nh nh !ng nhom ve mau sa<c, ve chat l ng, ? khi ẩanh gia ve mo t nha!n hie u san pham cu the nao ẩo. Tien tr3nh ma! hoa co the ẩ c tien hanh nh sau-
3. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam • au tien, (ac ẩ1nh loai cau tra l i cho nh !ng cau hoi t ng ng. Nh !ng cau tra l i nay co the thu tha p t  mo t ma.u cac bang cau hoi ẩa! hoan tat, th ng la 2AB tren tong so bang cau hoi • Tiep theo la (ay d ng mo t danh sach lie t ke cac cau tra l i, cac cau tra l i ẩ c lie t ke c ban d a tren nh !ng cau tra l i (ac ẩ1nh  tren, va co tien hanh nhom cac cau tra l i theo nh !ng nhom ẩa+c tr ng. Nh !ng cau tra l i ẩ c nhom lai theo nh !ng yeu to nh s  giong nhau ve ẩa+c t%nh, tan suat (uat hie n, ? • Cuoi cung, nh !ng nhom cau tra l i nay ẩ c gan cho mo t nha!n hie u, mo t gia tr1, th ng la mo t con so cu the II. NHAP D’ LIEU, -.NH BIE/N VA CAC THAO TAC TREN BIE/N 1. Nha0p d3' li 0u 4o t cau truc d ! lie u ẩien h3nh trong SCSS se! bao gom- • Co0t Cac co t trong man h3nh data SCSS se! /uan ly cac bien DvariablesE. 4o.i co t trong man h3nh bang t%nh SCSS se! ẩai die n cho mo t cau tra l i trong bang cau hoi. Tuy nhien chu y ẩen hai loai bien, bien mo t tra l i va bien nhieu tra l i Loa&i bieãn mot tra l'i- 4o.i co t trong bang t%nh se! la bien ẩai die n cho ket /ua duy nhat cua cau hoi mo t tra l i. Loa&i bieãn nhieu tra l'i- oi hoi phai s ! dung nhieu co t ẩe /uan ly cho cac ket /ua khac nhau co the co cho cau hoi nhieu tra l i. Chai bao ẩam khai bao ẩu so co t Dso bienE nham ch a ẩ ng ẩu cac cau tra l i co the (ay (a. • Do6ng Cac dong trong man h3nh bang t%nh SCSS se! /uan ly cac bang cau hoi, hay mo t /uan sat DobservationE. 4o.i dong se! ẩ c (em nh mo t bang cau hoi, so l ng ma.u nghien c u phai bang v i so l ng dong ch a thong tin. D ! lie u ẩ c nha p ngay trong man h3nh bang t%nh cua SCSS nh h3nh 1-
4. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam 63nh 1- 4an h3nh data Chung ta co the tien hanh nha p d ! lie u nh sau- • 0hai bao ten bien ch a ẩ ng thong tin can nha p vao thanh ben tren mo.i co t Dten ma+c ẩ1nh cua cac co t nay trong SCSS la var0001, var000(E. Chan nay se! ẩ c ẩe ca p chi tiet trong phan ẩ1nh bien. • Chon o can nha p d ! lie u, la phan giao nhau gi !a co t va hang. OI can nha p se! co khung vien chung /uanh bao cho ng i nha p biet ẩo la o ẩang hoat ẩo ng, ten bien va so hie u hang ẩ c hie n  goc trai cua c a so. • Go! gia tr1 can nha p vao khung ẩa! chon, gia tr1 nay ẩ c hie n trong thanh s !a ẩoi Dcell editorE nam  tren c a so. Chu y khi nha p d ! lie u phai bao ẩam ẩung v i kieu bien ẩa! ẩ c ẩ1nh ngh2a. Thong th ng cac kieu bien ẩ c khai bao la dang chuoi Ddai toi ẩa 8 ky t E hoa+c dang so, nham bao ẩam t%nh t ng th%ch cho vie c phan t%ch sau nay. 2. -9nh bi ;n Bi ;n (variabl )- La ta p h p nh !ng tra l i cho 1 cau hoi. Co hai loai bien nh sau- Theo yeu cau bang cau hoi: • Lien mo t tra l i- Lien danh cho cau hoi co mo t tra l i
5. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam • Lien nhieu tra l i- Cac bien danh cho nhieu cau tra l i tra l i ẩong th i trong cau hoi nhieu tra l i M% du nh trong bang cau hoi co hai cau hoi sau- Cau hoi 1- 6a!y cho biet ban  nhom tuoi nao trong so nh !ng nhom tuoi sau- Nhom tuoi code D i 18 1 1N ẩen 30 2 31 ẩen P0 3 P1 ẩen A0 P Tren A0 A Cau hoi 2- Noi ẩen ẩie n thoai di ẩo ng, ban biet ẩ c nh !ng nha!n hie u nao trong danh sach lie t ke d i ẩay Nha!n hie u code Ericson 1 4otorola 2 Nokia 3 Siemens P Canasonic A ? .M.M Co the thay ẩoi v i cau hoi 1, ng i tra l i ch5 co the ẩ a ra mo t cau tra l i duy nhat ve tuoi cua m3nh, do ẩo bien ch a ẩ ng cau tra l i cua cau hoi 1 la bien mo t tra l i. Trong khi (em (et cau hoi 2, ng i tra l i co the neu ra nhieu nha!n hie u ma ho co biet /ua do ẩo phai co nhieu bien ch a ẩung cac tra l i co the co, ta goi bien ẩo la bien nhieu tra l i. Theo )ie*u d , lieu: • Lien ẩ1nh l ng D/uantitative variableE- La bien co the mo ta bang mo t con so va con so ẩo co the dung ẩe t%nh toan thanh nh !ng gia tr1 so hoc nh gia tr1 trung b3nh. • Lien ẩ1nh t%nh D/ualitative variableE- La cac bien the hie n trang thai cua bien nh mau sa<c, gi i t%nh. Loai bien nay co the ẩ c ma! hoa thanh cac con so nh ng lai khong co gia tr1 t%nh toan so hoc. Ta co the (em (et bang mo ta cac loai phong trong mo t khach san sau-
6. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam Loai Gia So l ng 4o ta phong D ongRphongE phong Tivi Tu lanh 4ay ẩieu hoa Loai S A00,000 10 Co Co Co Loai L 300,000 20 0hong Co Co Loai C 1A0,000 A0 0hong Co 0hong Ta co the phan loai bien trong bang tren nh sau- Lien ẩ1nh l ng- Lien gia va so l ng phong Lien ẩ1nh t%nh- Lien Loai phong va cac bien mo ta. Nh3'ng ca;p @o0 @o l3A6ng (l v ls of m asur m nt) Nh ẩa! ẩe ca p  tren, bien dung ẩe ch a cac ket /ua tra l i, nh !ng hie n t ng ẩ c /uan sat sau khi cac ket /ua hay hie n t ng tren ẩa! ẩ c gan thanh nh !ng d ! kie n l ng hoa Dcon soE hay nh !ng ky ma! DcodeE. Mie c gan nh !ng d ! lie u nay ẩoi hoi chung ta phai phat trien mo t dang thang ẩo phu h p v i ẩa+c t%nh cua d ! lie u. Co bon loai thang ẩo- • Thang ẩo ẩ1nh danh Dnominal measurementE • Thang ẩo th t  Dordinal measurementE • Thang ẩo /ua!ng Dinterval measurementE • Thang ẩo ty le Dratio measurementE Thang -o -.nh danh- Trong loai thang ẩo nay cac con so ẩ c s  dung ẩ n thuan nh mo t gia tr1 (ac ẩ1nh mo t loai, hang DcategoryE khac nhau va ch5 ẩ c dung nh mo t cai ten hay nha!n cho loai, hang ẩo. oi v i loai thang ẩo ẩ1nh danh cac gia tr1 so ẩ c s  dung nh la ky so nha n dang va khong co gia tr1 ve ma+t so hoc nh so sanh, co ng, tr , nhan, chia. Thang -o th  t &- Trong thang ẩo nay d ! lie u ẩ c (a<p (ep th t  cac gia tr1 theo mo t tieu chuan nao ẩo. 4o.i gia tr1 co v1 tr% cao h n hoa+c thap h n so v i gia tr1 khac, nh ng khong die.n ta ẩ c cao hay thap h n bao nhieu. Dang cau hoi nhom tuoi cua ng i tra l i D1- nho h n 18 tuoiT 2- 1N-30 tuoi, ? E hay khoang thu nha p D1- d i 1 trie u ẩongRthang, 2- T  1 trie u ẩong ẩen 2 trie u ẩongRthangE la v% du ro nhat ve dang thang ẩo nay. Tom lai thang ẩo th t  bao gom ca thong tin ẩ1nh danh ẩong th i cung cap luon /uan he th t  gi !a cac gia tr1 nh ng khong ẩo ẩ c khoang cach gi !a cac gia tr1 ẩo. Thang -o ua,ng- Giong nh ẩa+c t%nh cua thang ẩo th t , nh ng thang ẩo /ua!ng cho phep ta ẩo ẩ c khoang cach gi !a cac gia tr1. Tuy nhien do thang ẩo /ua!ng khong
7. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam (ac ẩ1nh ẩiem 0, do ẩo ta ch5 co the noi gia tr1 nay l n h n gia tr1 kia bao nhieu ẩ n v1 nh ng khong the ket lua n gia tr1 ẩo l n h n gia tr1 kia bao nhieu lan. M% du n c  nhie t ẩo 80o C chenh le ch ve nhie t ẩo so v i n c  P0o C giong nh m c ẩo chenh le ch ve nhie t ẩo cua n c  A0o C va n c  10o C, nh ng ta khong the noi n c  80o C nong gap ẩoi n c  P0o C Thang -o ty le- ay la thang ẩo co ẩu cac ẩa+c t%nh th t  va khoang cach. Ngoai ra vie c (ac ẩ1nh ty so gi !a cac gia tr1 la co the th c hie n do  thang ẩo nay ẩiem 0 ẩ c (ac ẩ1nh mo t cach co y ngh2a. M% du khi ta thu tha p so lie u ve thu nha p hang thang cua mo t ho  thanh pho 6o ch% 4inh, ta co the so sanh thu nha p cua hai ho S va L co thu nha p lan l t la P trie u va 8 trie u nh ho L co thu nha p gap ẩoi ho S, hay ho S co thu nha p bang phan n a ho L -9nh bi ;n Sau khi ẩa! khao sat ve bien va cac dang gia tr1 trong bien, chung ta can phai co cong ẩoan gan nha!n cho cac bien va gan y ngh2a cho cac gia tr1 cua bien Trong SCSS co hai cong cu ẩ1nh bien ẩ1nh bien rieng bie t va ẩ1nh bien chung. • 1nh bien rieng bie t Ddefine variableE- Thong th ng ẩ c dung ẩe ẩ1nh bien mo t tra l i. • 1nh bien chung DtemplateE- 4o t cach lam nga<n gon, tiet kie m th i gian khi ẩ1nh cac bien co nh !ng ẩa+c t%nh giong nhau Dkieu bien, nha!n cua cac gia tr1 trong bienE nh cac bien trong cau hoi nhieu tra l i • M i phien ban SCSS 10.0A, ta co cach ẩ1nh bien ẩ n gian h n nhieu bang cach s  dung Mariable Miew -9nh bi ;n ri Dng bi 0t e ẩ1nh m i hoa+c thay ẩoi ten, loai va cac ẩa+c t%nh khac cua mo t bien, co hai cach- Double-click len ten bien hien th1 tren ẩau mo.i co t tren man h3nh data cua SCSS 6oa+c chon bat ky o nao trong co t cua bien va chon tren menu- DataRDefine Mariable? 6o p thoai Define Mariable ẩ c m  ra nh h3nh 2-
8. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam 63nh 2- 6o p thoai Define Mariable /hai bao ten bieãn, ten bien nay se! hien th1 tren man h3nh data cua SCSS va b1 han che ve so ky t  hien th1, do ẩo can thiet phai khai bao nga<n gon va de. g i nh , thong th ng nen ẩa+t theo th t  cau hoi trong bang cau hoi nh /1, /3, /Pa, ? Co mo t so /ui c sau ẩay phai tuan theo khi khai bao ten bien - La<t ẩau bang mo t ch  cai va khong ba<t ẩau bang dau chamD.E. Ten bien khong ẩ c /ua 8 ky t  0hong ẩ c ch a khoang tra<ng va cac ky t  ẩa+c bie t nh DXE, DYE, D*E. Cac t  khoa sau ẩay khong ẩ c dung lam ten bien- SLL, NE, E9, TO, LE, LT, LY OR, GT, SND, NOT, GE, W IT6 e ẩ1nh ra loai bien, an thanh type trong h p thoai define variable, ta se! co ho p thoai Define Mariable Type nh h3nh 3- 63nh 3- 6o p thoai Define Mariable Type
9. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam Tuy thuo c vao yeu cau cua d ! lie u, ma ta se! ẩ1nh loai bien cho bien, SCSS ma+c ẩ1nh loai bien la kieu so DnumericE. Tiep theo la gan nha,n cho cac bieãn. Ta tr  lai ho p thoai define variable va nhan vao thanh labels ẩe co ẩ c ho p thoai nh h3nh P- 63nh P- 6o p thoai Define Lables Cong vie c ẩau tien trong ho p thoai define labels la tien hanh gan nha!n cho bien Dvariable labelsE, nha!n cua bien se! ẩ c hie.n th1 v i chieu dai toi ẩa 120 ky t , ẩ c dung ẩe mo ta them y ngh2a cua bien. Thong th ng ta co the dung nh !ng cau hoi trong bang cau hoi ẩe s  dung lam nha!n cua bien, ẩieu nay giup ta de. dang trong vie c ẩoc va hieu so lie u phan t%ch sau nay. Tiep theo la gan nha,n cho cac gia tr. cua bieãn DMalue labelsE va ẩay ch%nh la khai bao y ngh2a cua cac gia tr1 cua bien. 0hai bao nha!n cua bien tren ho p thoai Mariable lable. M i nha!n cua gia tr1 Dvalue lablesE co ba thao tac- • Gan mo t nha!n m i- Nha p gia tr1 vao ho p thoai Malue Nha p mo t nha!n vao ho p thoai Malue Label Sn nut Sdd • S !a ẩoi mo t nha!n- Di ve t sang ẩen nha!n can s a ẩoi Nha p ten nha!n m i, an nut Change ẩe thay ẩoi • Loai bo mo t nha!n- Di ve t sang ẩen nha!n can loai bo
10. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam Sn nut Remove ẩe loai bo 0ia tr. )huyeãt 12issing values3 ẩ c dung ẩe khai bao cac gia tr1 ma ta cho phep chung ẩai die n cho cac tr ng h p trong d ! lie u, khi ẩo cac gia tr1 nay se! khong ẩ c s  dung trong /ua tr3nh (  ly thong ke sau nay. 1nh ngh2a 4issing Malues th ng ẩ c s  dung ẩe biet tai sao thong tin b1 thieu. Cha8ng han ta co the muon phan bie t thong tin b1 thieu do ng i tra l i t  choi tra l i hay do cau hoi khong th%ch h p ẩoi v i ẩoi t ng ẩo Trong ho p thoai tren ta co the nha p ba gia tr1 khuyet khac nhau, mo t khoang gia tr1 khuyet hay mo t khoang va mo t gia tr1 khuyet khac. 1nh ngh2a bang khoang ch5 co the ẩ c dung khi bien lay gia tr1 so Ta khong the ẩ1nh ngh2a gia tr1 khuyet cho cac bien chuo.i dai h n tam ky t . Tat ca cac gia tr1 chuo.i, ke ca trong hay khoang tra<ng, ẩ c (em nh co gia tr1 tr  khi ta ẩ1nh ngh2a ẩo la gia tr1 khuyet. e ẩ1nh ngh2a trong hay khoang tra<ng la gia tr1 khuyet, ta nha p mo t khoang tra<ng Dsingle spaceE vao mo t trong ba vung cua Discrete missing values Ta con co the phan t%ch cac gia tr1 khuyet bang cong cu 4issing Malue Snalysis.
11. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam 4issing Malue Snalysis co ba ch c na_ng ch%nh 1. No la cong cu giup mo ta /uy lua t cua cac gia tr1 khuyet- cac gia tr1 khuyet nam  ẩau, co nhieu gia tr1 khuyet khong, co nh !ng ca+p bien co (u h ng b1 thieu gia tr1  cac bang cau hoi khac nhau khong, cac d ! lie u /ua l n hay /ua nho, hay co phai cac gia tr1 b1 thieu mo t cach ngau nhien 2. ` c l ng trung b3nh, ẩo le ch chuan, hie p ph ng sai, va he so t ng /uan bang cac ph ng phap listwise, pairwise, regression, or E4 De(pectation- ma(imiaationE. Ch ng phap listwise bo /ua cac tr ng h p co gia tr1 khuyet  bat ky bien nao, trong khi pairwise ch5 bo /ua cac tr ng h p co gia tr1 khuyet  ca+p bien ẩang (  ly. Ch ng phap E4 c l ng cac gia tr1 khuyet bang /ua tr3nh la+p. bc mo.i b c la+p co mo t b c E t%nh gia tr1 trung b3nh cua cac tham so va mo t b c 4 t%nh cac c l ng h p ly nhat D ma(imum likelihood estimatesE. Ch ng phap hoi /uy th3 c l ng cac gia tr1 khuyet bang thua t toan hoi /uy. 3. ien cac gia tr1 b1 thieu bang cac gia tr1 c l ng cho b i hoi /uy hay E4 4issing value analysis giup giai /uyet nhieu van ẩe gay ra do thieu d ! lie u. Nh !ng tr ng h p b1 thieu gia tr1 khac mo t cach he thong v i nh !ng tr ng h p co ẩay ẩu gia tr1 co the lam cho ket /ua kho hieu, m ho. Cac d ! lie u b1 thieu co the lam giam ẩo ch%nh (ac cua cac thong ke ẩ c t%nh v3 co %t thong tin h n d  t%nh ban ẩau. 4o t van ẩe n !a la cac gia thiet ẩang sau nhieu thu tuc thong ke d a tren cac
12. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam tr ng h p co ẩu thong tin, va cac gia tr1 khuyet co the lam ph c tap them phan ly thuyet s  dung 2easurement. Tuy thuo c vao dang thang ẩo ẩ c s  dung trong bien ma ta khai bao trong cong cu measurement, chu y khai bao scale ẩ c dung chung cho dang thang ẩo /ua!ng va thang ẩo ty le -9nh bi ;n chung ay la cong cu ẩ1nh bien nhanh cho cung mo t luc nhieu bien co cung chung kieu bien-type hoa+cRva cung chung kieu ma! hoa d ! lie u Dcac gia tr1 cua bien giong nhau-value labelsE. Tr c tien ta ẩanh dau khoi cac bien Dt  co tE ma ta muon ẩ1nh bien chung tren man h3nh Data cua SCSS. T  data tren thanh menu ta nhap template ẩe co ẩ c ho p thoai nh h3nh A 63nh A- 6o p thoai Template Sau khi co ho p thoai template, nhan thanh Definedd trong ho p thoai ẩe khai bao ten bien  ho p thoai Name, phan ten khai bao nay se! ẩ c l u tr ! trong template va ta co the lay ra s  dung trong tr ng h p khai bao cac bien khac co cung dang. Chan define template ẩ c dung khai bao loai, cac gia tr1 cua bien,? cho cac bien ẩang ẩ1nh ngh2a loai bien va gan nha!n cho cac gia tr1 cua bien. Chan apply trong ho p thoai cho ta l a chon nh !ng phan chung cua cac bien. Sau ẩo nhan Sdd ẩe (ac nha n vie c ẩ1nh bien chung nay. Neu phan ẩ1nh nghia! nay ẩa! ẩ c l u trong template sau nay khi muon ẩem no ra ẩe ẩ1nh ngh2a cho cac bien khac co s  giong nhau ve loai hay nha!n gia tr1, ? ta ch5 vie c l a chon ten template ẩa! ẩ c l u trong cong cu template Dma+c ẩ1nh la defaultE E. TaFo la0p th Dm ca*c bi ;n Ta p h p 1 so bien Dtra l iE khac nhau ẩe tao thanh mo t bien m i co y ngh2a cho thong ke hay phan t%ch h n. Ngoai cac bien ẩa! ẩ c khai bao trong /ua tr3nh nha p
13. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam d ! lie u, mo t so bien khac co the ẩ c tao ra do nhu cau ( ! ly va phan t%ch d ! lie u. Co mo t so cong cu th ng ẩ c s  dung nh Recode, Compute, Count R cod Recode into same variables- Recode tren cung mo t bien, t c la ẩ1nh lai nh !ng gia tr1 cua nh !ng bien hie n tai hoa+c rut nga<n b t da!y cac gia tr1 ton tai thanh nh !ng gia tr1 m i tren cung nh !ng bien ẩo. Nhap transformRrecode t  thanh menu ch%nh. bc ẩay ta l a chon into same variable ẩe tien hanh ẩ1nh lai gia tr1 cho bien tren cung mo t bien. Ta co ho p thoai nh h3nh e 63nh e- 6o p thoai recode into same variables Chuyen cac bien can ẩ1nh lai gia tr1 sang ho p thoai variables. Nhan thanh Old and New Malues ẩe ẩ1nh cac gia tr1 cu can thay ẩoi thanh cac gia tr1 m i. Nhan thanh If ẩe (ac ẩ1nh cac ẩieu kie n ẩe th c hie n le nh Recode Tiep theo nhan thanh old and new values t  ho p thoai Recode ta se! co ho p thoai nh h3nh f 63nh f- 6o p thoai Old and New Malues
14. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam Old value dung ẩe khai bao gia tr1 cu! can chuyen ẩoi. Gia tr1 cu! nay co the la mo t g%a tr1 ẩ n le, mo t gia tr1 khuyet, mo t da!y cac gia tr1. New value dung ẩe khai bao gia tr1 m i se! thay the cho gia tr1 cu! t ng ng. Nhan thanh Sdd ẩe l u s  chuyen ẩoi nay. Cac gia tr1 chuyen ẩoi co the s a ch a hoa+c loai bo bang cach di chuyen vet toi ẩen bieu th c the hie n s  chuyen ẩoi trong ho p thoai Old-dNew va nhan thanh Change cho s  thay ẩoi hoa+c Remove ẩe loai bo. Neu vie c ẩ1nh lai gia tr1 cua cac gia tr1 cua bien co mo t so ẩieu kie n kem theo, ta co the dung cong cu if ẩe ẩ1nh ra cac ẩieu kie n cho le nh recode. Nhan thanh if t  ho p thoai ta co ho p thoai con nh h3nh 8 63nh 8- 6o p thoai If cases Trong ho p thoai If Cases, ma+c ẩ1nh la khong co ẩieu kie n nao ca, phep ẩ1nh lai gia tr1 cua bien ẩ c th c hie n cho tat ca cac /uan sat,  ẩay hien th1 la Include all cases. Chon le nh include if case satisfies condition ẩe (ac ẩ1nh ẩieu kie n trong vie c ẩ1nh lai gia tr1 cua bien. Chuyen ten bien can ẩ1nh lai cac gia tr1 vao ho p thoai ben phai. Luc nay phep ẩ1nh lai gia tr1 cua bien noi tren ch5 ẩ c th c hie n ẩoi v i cac /uan sat nao thoa ma!n ẩ c bieu th c ẩieu kie n ẩ c the hie n. Recode into different variables- Trong tr ng h p ẩ1nh lai gia tr1 hie n tai cua mo t bien thanh mo t gia tr1 m i trong mo t bien khac ta se! l a chon transformRrecodeR into different variable va ta co ho p thoai nh h3nh N-
15. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam 63nh N- 6o p thoai Recode into Different Mariables Chuyen cac bien can ẩ1nh lai gia tr1 vao trong ho p variables. 0hai bao ten bien m i va nha!n bien m i trong ho p thoai Output variable. Sau ẩo nhan thanh change ẩe (ac nha n. Cac cong cu If va Old and New Malues cu!ng co y ngh2a va thao tac giong nh tr ng h p ẩ1nh lai gia tr1 cho cung mo t bien. Comput Co ng cu compute ẩ c dung ẩe t%nh cac gia tr1 m i t  cac bien sa>n co trong cau truc d ! lie u. 0et /ua t%nh toan th ng ẩ c ch a ẩ ng trong mo t bien m i, hoa+c la mo t bien khac sa8n co hoa+c bien ch a ẩ ng gia tr1 ẩang t%nh toan. Truy (uat cong cu compute variable t  transform tren thanh menu ta co ho p thoai nh h3nh 10, 6o p thoai Target variable ch a ẩ ng ten bien se! nha n gia tr1 ẩ c t%nh. Ta co the khai bao kieu va gan nha!n cho cac gia tr1 cua bien bang cach nhan vao thanh Type)label. 6o p thoai Numeric E(pression ch a ẩung cac bieu th c so ẩ c dung ẩe t%nh gia tr1 cho bien ẩ%ch Dbien ch a ẩung gia tr1 m iE, bieu th c nay co the dung ten cac bien sa>n co, cac hang, cac toan t  va cac ham so. Chung ta co the go! vao va soan bieu th c t%nh toan t ng t  nh v i mo t va_n ban, va co the s ! dung cac cong cu ẩ c hien th1 trong ho p thoai nh cac phiem DgE, D-E, hunction,? Cong cu If dung ẩe ẩ1nh ra nh !ng ẩieu kie n can thiet kem theo trong t%nh toan neu co, ẩ c s  dung giong nh cong cu If trong ho p thoai Recode
16. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam 63nh 10- 6o p thoai Compute Mariable Count Cong cu nay ẩ c dung ẩe tao ra mo t bien m i ch a tong so lan (uat hie n cua mo t gia tr1 hay cac gia tr1 ẩa! ẩ c ẩ1nh ra danh sach cac bien ẩ c chon. T  menu ta chon TransformRcount ẩe co ẩ c ho p thoai nh h3nh 11 63nh 11- 6o p thoai Count 4o t bien m i se! ẩ c tao ra khi ta th c hie n thu tuc Count goi la bien ẩ%ch DTaget variableE se! ch a ẩ ng gia tr1 co ng don mo.i khi ga+p ẩ c gia tr1 can ẩem trong mo t hoa+c nhieu bien ẩa! ẩ c khai bao tr c trong ho p thoai Numeric variables. Gia tr1 can ẩem se! ẩ c ẩ1nh ro! trong phan Define values. Gia tr1 khai bao ẩe ẩem co the la nh !ng gia tr1 cu the nao ẩo DMalueE, hoa+c nh !ng gia tr1 ro.ng DSystem missingE hoa+c la mo t da!y cac gia tr1 DrangeE. Sau khi khai bao gia tr1 can ẩem ta dung thanh
17. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam Sdd ẩe (ac nha n gia tr1 can ẩem vao trong ho p thoai Malues to count. S  dung Change hoa+c Remove ẩe thay the hoa+c loai bo gia tr1 can ẩem Dgia tr1 ẩa! ẩ c ẩanh dau bang vet ẩenE. 63nh 12- 63nh 12- 6o p thoai define values Cong cu If dung ẩe (ac ẩ1nh cac ẩieu kie n neu co khi th c hie n le nh Count, Dgiong nh cong cu If trong phan Recode ẩa! ẩ oc ẩe ca p  trenE -9nh nghGa nho*m bi ;n nhi Hu traI lA6i (d fin multir spons s ts) Trong cau hoi nhieu tra l i se! bao gom nhieu bien ch a ẩ ng cac tra l i co the co, nh !ng bien nay goi la bien s cap. Do ẩo ẩe ( ! ly, chung ta phai go p cac bien s cap nay thanh mo t bien go p ch a cac bien s cap. Sau ẩo trong cac phan t%ch thong ke lien /uan ẩen cau hoi nhieu tra l i, chung ta se! dung ten bien go p nay thay the cho tat ca cac bien s cap. Lien go p nay ch a ẩung toan bo cac gia tr1 trong cac bien s cap cua mo t cau hoi nhieu tra l i. Chon menu StatisticsR4ultiple ResponseRDefine sets? se! m  ho p thoai Define 4ultiple Response Sets nhu h3nh 13-
18. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam 63nh 13- 6o p thoai Define 4ultiple Response Sets Chon tat ca nh !ng bien s cap lien /uan ẩen mo t cau hoi nhieu tra l i  ho p thoai ben trai chuyen sang ho p thoai Mariables in Set, ch5 ẩ1nh cach ma! hoa cac bien ẩo Ddichotomy hay categoryE, da!y gia tr1 ma! hoa Range ? Through, (ac ẩ1nh ten nhom ẩa tra l i roi an thanh Sdd ẩe ẩ a ten nhom v a (ac ẩ1nh vao ho p 4ult Response Sets. Sau khi mo.i nhom ẩ c ẩ1nh ngh2a, cac bien chon se! ẩ c tra ve ho p thoai thoai ben trai, cho phep ta co the dung nhieu bien t ng t  cho nhieu nhom ẩa tra l i khac nhau Trong khung Mariable Sre Code Ss, chung ta co the chon mo t hay hai muc sau ẩay tuy theo ph ng phap ma! hoa- 4ichotomies- ay la trang thai ma+c ẩ1nh, mo.i bien s cap ch5 co hai gia tr1, va chung ta nha p gia tr1 nao cua bien can ẩem vao ho p Counted Malue Category- 4o.i bien s cap co nhieu h n hai gia tr1, va chung ta nha p cac gia tr1 nho nhat va l n nhat cua da!y gia tr1 ma! hoa vao cac o Range va thourgh Chung ta ẩa+t ten cho nhom ẩa bien Dtoi ẩa f ky t E va nha!n Dtoi ẩa P0 ky t E vao cac ho p Name va Label. L u y la ten cua cac nhom ẩa bien ch5 ẩ c s  dung trong cac thu tuc (  ly bien nhieu tra l i ma thoi. e loai bo va s a ẩoi vie c ẩ1nh ngh2a mo t nhom bien ẩa tra l i nao ẩo ta di chuyen ve t sang ẩen ten nhom ẩo va nhap thanh Remove ẩe loai bo va thanh Change ẩe thay ẩoi. La0p baIng cho bi ;n nhi Hu traI lA6i e tien hanh la p bang cho cac bien nhieu tra l i, ta s  dung cac ten nhom ẩa bien ẩa! ẩ c ẩ1nh ngh2a bang cong cu Define 4ulti Response Sets ẩa! ẩ c ẩe ca p  phan tren sau ẩo vao Statisticsi4ultiple response va chon hre/uencies hoa+c Crosstabs tuy theo nhu cau la p bang mo t chieu hay ẩa chieu.
19. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam Ngoai ra khi chung ta tien hanh la p bang cho ket /ua cuoi cung cua van ẩe nghien c u co the dung cac cong cu trong statistics R custom table ẩe tao ra cac bang bieu, co the la bang mo t chieu, bang nhieu chieu va cac bang bieu mo ta thong ke tuy theo yeu cau cua van ẩe nghien c u Lang bieu the hie n tan so (uat hie n DTables of fre/uenciesE- cho phep chung ta tao ra nh !ng bang bieu the hie n tan so (uat hie n cua mo t hay nhieu bien ẩ n • Dang bang bieu c ban DLasic tablesE- The hie n cac d ! lie u nghien c u theo dang bang cheo Dcross-tabulationE gi !a hai bien hoa+c gi !a mo t bien va mo t nhom cac bien. • Dang bang ẩa bien D4ultiple response tablesE- Giong nh basic tables the hie n tan suat (uat hie n va bang cheo, tuy nhien dang bang bieu nay cho phep ta (ay d ng bang bieu cho cac cau tra l i ẩa bien • Dang bang bieu tong h p DGeneral tablesE- Giong nh bang bieu c ban va ẩa tra! l i. Cac d ! lie u ẩ c the hie n d i dang bang cheo, tuy nhien  dang bang bieu nay cho phep ng i phan t%ch the hie n moi lien he gi !a mo t bien v i nhieu bien khac tren cung mo t bang e la p bang tan so cho bien nhieu tra l i ta ẩ a bien vao ho p thoai sau 63nh 1P- 6o p thoai 4ultiple Response hre/uencies Sau ẩay la ket /ua t%nh toan t  SCSS
20. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam Group $NOI_lE4 DNoi (em bong daE Cct of Cct of Category label Code Count Responses Cases Nha 1 1eP e3.1 Nf.e 9uan caf<e 2 A0 1N.2 2N.8 Lar 3 P 1.A 2.P San va n ẩo ng P P1 1A.8 2P.P 9uan nha u e 1 .P .e Total responses 2e0 100.0 1AP.8 0 missing casesT 1e8 valid cases e la p bang cheo cho bien nhieu tra l i, ta chon mo t bien ẩ n Dkhong nhat thiet phai la bien thanh phan cua bien go pE va mo t bien go p D4ultiple Response setsE ẩ a vao cac hang va co t roi chon cac ket /ua can t%nh trong Options, co the t%nh t5 le phan tra_m theo so ng i ẩieu tra trong ma.u hay so ket /ua tra l i. Ta co the tach bang cheo thanh nhieu l p d a vao cac gia tr1 cua bien LayerDsE 63nh 1A- 6o p thoai 4ultiple Response Crosstabs 0et /ua t%nh toan cho trong bang sau ẩay
21. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam * * * C R O S S T A B U L A T I O N * * * $NOI_XEM (paired group) Noi xem bong da by Q15B_1 A/C thớch xem ụỷ ủaõu nhaỏt? Q15B_1 Count Nha Quan cafe Bar Sõn van Nha van Row pct dong hoa Row Col pct Total Tab pct 1 2 3 4 5 $NOI_XEM À À À À À À 1 80 19 3 59 2 163 Nha 49.1 11.7 1.8 36.2 1.2 63.2 86.0 42.2 50.0 53.2 66.7 31.0 7.4 1.2 22.9 .8 À À À À À À 2 9 18 1 20 1 49 Quan cafộ 18.4 36.7 2.0 40.8 2.0 19.0 9.7 40.0 16.7 18.0 33.3 3.5 7.0 .4 7.8 .4 À À À À À À 3 0 3 1 0 0 4 Bar .0 75.0 25.0 .0 .0 1.6 .0 6.7 16.7 .0 .0 .0 1.2 .4 .0 .0 À À À À À À 4 4 5 1 31 0 41 Sõn van dong 9.8 12.2 2.4 75.6 .0 15.9 4.3 11.1 16.7 27.9 .0 1.6 1.9 .4 12.0 .0 À À À À À À 6 0 0 0 1 0 1 Quan nhau .0 .0 .0 100.0 .0 .4 .0 .0 .0 .9 .0 .0 .0 .0 .4 .0 À À À À À À Column 93 45 6 111 3 258 Total 36.0 17.4 2.3 43.0 1.2 100.0 Percents and totals based on responses 167 valid cases; 1 missing cases
22. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam M O TA C S D’ LIEU I. M O TA D’ LIEU BAJNG BIEU -OK 1. Scatt r Chon mo t bien cho Y-a(is va bien khac cho l-a(is. Cac bien nay phai la so nh ng khong nen  ẩ1nh dang ngay thang Co the chon bien cho vao Set 4arkers. 4o.i gia tr1 cua bien nay se! ẩ c ẩanh dau bang ky hie u khac nhau tren bieu ẩo scatter. Lien nay co the la so hay chuoi ky t  Ta cu!ng co the chon bien so hay chuoi ky t  ẩ a vao Label Cases ẩe ẩa+t nha!n cho cac ẩiem tren bieu ẩo M% du Neu ẩ c chon, cac nha!n gia tr1 Dhay gia tr1 neu khong ẩ1nh ngh2a nha!nE cua bien nay ẩ c dung ẩe gan nha!n cho cac ẩiem Neu khong chon, so tr ng h p co the ẩ c dung ẩe gan nha!n cho cac c c tr1 63nh 1e- 6o p thoai Scatterplot Ta co bon cach ẩe hien th1 ket /ua cua bieu ẩo- simple la cach hien th1 ẩ n gian mo t bien theo bien khac, overlay ẩe hien th1 nhieu bieu ẩo gi !a nhieu ca+p bien
23. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam cung mo t luc, matri( ẩe hien th1 bieu ẩo cua nhieu bien so t ng ca+p v i nhau, 3-D hien th1 bieu ẩo cua ba bien trong khong gian ba chieu. Co the (em cac bieu ẩo minh hoa d i ẩay tr3nh bay lan l t theo th t  tren D i ẩay ta ch5 minh hoa tr ng h p simple scatterplot, cac tr ng h p khac lam t ng t 
24. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam 63nh 1f- 6o p thoai Simple Scatterplot 22 20 18 16 ) s 14 r a e y ( l 12 e v e L 10 l a n Gender o i t 8 a Male c u d 6 Female E .5 1.0 1.5 2.0 2.5 3.0 3.5 Employment Category
25. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam 2. Histogram Lieu ẩo histogram nhom cac gia tr1 cua bien vao cac nhom cach ẩeu nhau vave! bieu ẩo t ng ng v i so tr ng h p trong mo.i nhom. So tr ng h p co the bieu th1 theo phan tra_m, rat tie n cho vie c so sanh cac ta p d ! lie u co k%ch th c khac nhau. So tr ng h p hay phan tra_m cu!ng co the ẩ c t%ch lu!y theo cac nhom Lieu ẩo histogram co the ch5 ra cac c c tr1 va ẩo le ch cua phan phoi. ieu nay cho biet co the dung cac thu tuc co gia ẩ1nh phan phoi chuan ẩe (  ly bien nay khong 63nh 18- 6o p thoai 6istogram 200 100 Std. Dev = 2.88 Mean = 13.5 0 N = 474.00 8.0 10.0 12.0 14.0 16.0 18.0 20.0 22.0 Educational Level (years)
26. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam 3. P-P Plots C-C Clots bieu die.n phan phoi t%ch lu!y cua bien theo phan phoi t%ch lu!y cua phan phoi ẩa! chon ẩe kiem tra. Neu cac ẩiem phan bo (ung /uanh ẩ ng tha8ng, phan phoi cua bien phu h p v i phan phoi ẩa! chon. Cac phan phoi co sa>n ẩe kiem tra la beta, chi-s/uare, e(ponential, gamma, half-normal, Laplace, Logistic, Lognormal, normal, pareto, Studentms t, W eibull, va uniform. Tuy theo phan phoi ẩa! chon ma (ac ẩ1nh ẩo t  do va cac tham so Co the dung C-C Clots ẩoi v i cac so lie u ẩa! ẩ c bien ẩoi. Cac phep bien ẩoi co sa>n la natural log, standardiae values, difference, va seasonally difference. 63nh 1N- 6o p thoai C-C Clots
27. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam Detrended Normal P-P Plot of Educational Level (years) .1 l a m 0.0 r o N m o r f n o i t a i v e D -.1 -.2 0.0 .2 .4 .6 .8 1.0 1.2 Observed Cum Prob Transforms: natural log, difference (1) Normal P-P Plot of Educational Level (years) 1.00 .75 b .50 o r P m u C .25 d e t c e p x E 0.00 0.00 .25 .50 .75 1.00 Observed Cum Prob Transforms: natural log, difference (1)
28. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam E. N-N Plot Cong cu 9-9 Clot ve! ẩo th1 cua cac ẩiem phan v1 D/uantilesE cua phan phoi cua bien theo cac phan v1 cua mo t phan phoi muon kiem tra. Cac ẩo th1 (ac suat th ng ẩ c dung ẩe (ac ẩ1nh (em phan phoi cua bien co phu h p v i phoi muon kiem khong. Neu phu h p cac ẩiem cua ẩo th1 se! phan bo /uanh mo t ẩ ng tha8ng. Cac phan phoi co sa>n ẩe kiem tra la beta, chi-s/uare, e(ponential, gamma, half-normal, Laplace, Logistic, Lognormal, normal, pareto, Studentms t, W eibull, va uniform. Tuy theo phan phoi ẩa! chon ma chung ta (ac ẩ1nh ẩo t  do hay cac tham so can thiet. Co the dung 9-9 Clots ẩoi v i cac so lie u ẩa! ẩ c bien ẩoi. Cac phep bien ẩoi co sa>n la natural log, standardiae values, difference, va seasonally difference. 63nh 20- 6o p thoai 9-9 Clots
29. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam Detrended Normal Q-Q Plot of Educational Level (years) .3 .2 .1 l 0.0 a m r o -.1 N m o r -.2 f n o i t -.3 a i v e D -.4 -4 -3 -2 -1 0 1 2 3 Standardized Observed Value Transforms: natural log, difference (1) Normal Q-Q Plot of Educational Level (years) 3 2 1 e 0 u l a V l -1 a m r o -2 N d e t c -3 e p x E -4 -4 -3 -2 -1 0 1 2 3 Standardized Observed Value Transforms: natural log, difference (1) O. BoP plot Cong cu Lo( plot cho ta chon loai bieu ẩo phu h p- simple hay clustered, va cach mo ta d ! lie u tot nhat /ua cac l a chon trong phan Data in Chart Sre. Yn
30. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam ngh2a cua l a chon nay se! ẩ c tr3nh bay d i ẩay. Lo(plots tr3nh bay median, inter/uartile range, outliers, va e(treme cases cua t ng bien 63nh 21- 6o p thoai Lo(plot a. Neu chon Simple, ta co the co cac bo( plots nh sau- S. Summaries for Groups of Cases 4o t bien lay gia tr1 so se! ẩ c tom ta<t theo ket /ua cua mo t bien khac. 4o.i ho p tr3nh bay median, /uartiles, va e(treme values cua mo t ket /ua. Can co cac (ac ẩ1nh toi thieu sau- • Lien lay gia tr1 so can tom ta<t • Lien tren truc Category a cac l a chon tren vao ho p thoai sau
31. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam 63nh 22- 6o p thoai Define Simple Lo(plot 0et /ua (  ly cua SCSS Employm nt Cat gory Cas Proc ssing Summary Cases Malid 4issing Total Employment Category N Cercent N Cercent N Cercent Educational Level Clerical 3e3 100.0B 0 .0B 3e3 100.0B DyearsE Custodial 2f 100.0B 0 .0B 2f 100.0B 4anager 8P 100.0B 0 .0B 8P 100.0B
32. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam Educational L v l (y ars) 22 20 18 16 ) s r 14 a e y ( l 12 e v e L l 10 a n o i t a 8 c u d E 6 N = 363 27 84 Clerical Custodial Manager Employment Category L. Summaries of Separate Mariables Nhieu bien lay gia tr1 so ẩ c tom ta<t. 4o.i ho p ẩai die n cho mo t bien can tom ta<t. Can co cac (ac ẩ1nh toi thieu sau- • ot nhat hai bien lay gia tr1 so a cac l a chon tren vao ho p thoai sau
33. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam 63nh 23- 6o p thoai Define Simple Lo(plot- Summaries of Separate Mariables 0et /ua (  ly b i SCSS nh sau Cas Proc ssing Summary Cases Malid 4issing Total N Cercent N Cercent N Cercent Educational Level DyearsE PfP 100.0B 0 .0B PfP 100.0B 4onths since 6ire PfP 100.0B 0 .0B PfP 100.0B
34. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam 120 100 80 60 40 20 Manager 0 N = 474 474 Educational Level (y Months since Hire b. Neu chon Scattered, ta co cac bo( plots nh sau- S. Summaries for Groups of Cases 4o t bien lay gia tr1 so se! ẩ c tom ta<t trong cac cum ẩ c (ac ẩ1nh b i mo t bien khac. 4o.i ho p trong cum ẩai die n cho mo t ket /ua cua bien dung ẩe ẩ1nh ngh2a cum. Can co cac (ac ẩ1nh toi thieu sau- • Lien lay gia tr1 so can tom ta<t • Lien tren truc Category DCategory Mariable 1E. • Lien ẩ1nh ngh2a cum DCat Mar 2E a cac l a chon tren vao ho p thoai sau
35. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam 63nh 2P- 6o p thoai Define Clustered Lo(plot- Summaries for Groups of Cases Ta ẩ c ket /ua cho b i SCSS nh sau Employm nt Cat goryRM inority Classification Cas Proc ssing Summary Cases Malid 4issing Total Employment 4inority N Cercent N Cercent N Cercent Category Classification Educational Clerical No 2fe 100.0B 0 .0B 2fe 100.0B Level DyearsE Yes 8f 100.0B 0 .0B 8f 100.0B Custodial No 1P 100.0B 0 .0B 1P 100.0B Yes 13 100.0B 0 .0B 13 100.0B 4anager No 80 100.0B 0 .0B 80 100.0B Yes P 100.0B 0 .0B P 100.0B
36. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam Educational L v l (y ars) 22 20 18 16 ) s r 14 a e y ( l 12 e v e L l 10 a Minority Classificat n o i t a 8 No c u d Yes E 6 N = 276 87 14 13 80 4 Clerical Custodial Manager Employment Category L. Summaries of Separate Mariables Nhieu bien lay gia tr1 so ẩ c tom ta<t. 4o.i ho p trong cum ẩai die n cho mo t bien can tom ta<t. Can co cac (ac ẩ1nh toi thieu sau- • ot nhat hai bien lay gia tr1 so DMar 1, Mar 2, etc.E • 4o t bien tren truc Category DCategory MariableE a cac l a chon tren vao ho p thoai sau
37. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam 63nh 2A- 6o p thoai Define Clustered Lo(plot- Summaries of Separate Mariables 0et /ua tr3nh bay trong SCSS nh sau Employm nt Cat gory Cas Proc ssing Summary Cases Malid 4issing Total Employment Category N Cercent N Cercent N Cercent Crevious E(perience Clerical 3e3 100.0B 0 .0B 3e3 100.0B DmonthsE Custodial 2f 100.0B 0 .0B 2f 100.0B 4anager 8P 100.0B 0 .0B 8P 100.0B 4onths since 6ire Clerical 3e3 100.0B 0 .0B 3e3 100.0B Custodial 2f 100.0B 0 .0B 2f 100.0B 4anager 8P 100.0B 0 .0B 8P 100.0B
38. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam 600 500 No No 400 No No YNeos NYeos No YNeos 300 No No YNeos 200 100 Previous Experience 0 (months) -100 Months since Hire N = 363 363 27 27 84 84 Clerical Custodial Manager Employment Category II. TOM TAST D’ LIEU Tr3nh bay cac d ! lie u nghien c u thanh bang bieu DtabulationE th ng ẩ c s ! dung ẩe tom ta<t va phan t%ch cac ket /ua nghien c u. Co hai cong cu ch%nh s ! dung trong vie c tom ta<t va gan loc d ! lie u nghien c u marketing- • Cong cu fre/uencies • Cong cu descriptives • Cong cu e(plore 1. La0p baIng phaDn bo; taHn sua;t (Tr Uu nci s) Cong cu hre/uencies s  dung cac thong so thong ke ẩe mo ta cho nhieu loai bien, ẩay la mo t b c tot ẩe chung ta ba<t ẩau khao sat d ! lie u. Chung ta co the khao sat d ! lie u thong /ua cac cong cu nh - tan suat (uat hie n, phan tra_m, phan tra_m t%ch lu!y. Ngoai ra no con cung cap cho ta cac phep ẩo l ng thong ke nh ẩo ta p trung Dcentral tendency measurementE, ẩo phan tan DdispersionE, t phan v1 D9uartilesE va cac phan v1 DpercentilesE, phan phoi d ! lie u DdistributionE. La p bang nay ngoai vie c tom ta<t d ! lie u, no con giup ta phat hie n nh !ng sai sot trong d ! lie u nh , nh !ng gia tr1 bat th ng D/ua l n hay /ua nhoE co the lam sai
39. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam le ch ket /ua phan t%ch thong ke, nh !ng gia tr1 ma! hoa bat th ng do sai sot vie c nha p lie u hay ma! hoa e tien hanh la p bang ẩ n ta chon cong cu StatisticRsumariaeRfre/uencies ta co ho p thoai nh h3nh 2e 63nh 2e- 6o p thoai hre/uencies Chuyen bien can mo ta sang ho p thoai variableDsE, SCSS ch5 cho phep cac bien kieu so DnumericE va chuoi nga<n p to ẩa 8 ky t  Dshort stringE. Ta co the l a chon mo t hoa+c nhieu bien can khao sat Cong cu Charts ẩ c dung ẩe ve! ẩo th1 cho d ! lie u, va cong cu hormat ẩ c s  dung ẩ1nh ra kieu hien th1 cua d ! lie u, theo th t  ta_ng dan hoa+c gia!m dan Cong cu Statistics ẩe truy (uat ho p thoai nh h3nh 2f. Trong ho p thoai statistics nay se! bao gom cac cong cu ẩe ẩo l ng cac gia tr1 thong ke cua d ! lie u nh v1 tr% t ng ẩoi cua cac nhom gia tr1, ma t ẩo ta p trung va phan tan cua d ! lie u, nh !ng ẩa+c t%nh ve phan phoi cua d ! lie u DDistributionE
40. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam 63nh 2f- 6o p thoai hre/uencies- Statistics Cac -ie*m phan v. 1percentile values3: c dung ẩe (ac ẩ1nh cac ranh gi i t ng ẩoi cua cac nhom /uan sat t  ma.u /uan sat, ẩieu l u y la d ! lie u can /uan sat ẩa! ẩ c (a<p (ep thep th t  t  thap ẩen cao. Ta co cac ẩiem chia d ! lie u thanh P phan bang nhau goi la t phan v1 D/uartilesE. 6oa+c ta co the chia d ! lie u theo cac phan bang nhau cu the bang cach go! so phan muon chia vao cong cu cuts points for e/ual groups. 6oa+c ta co the (em cac ẩiem phan v1 cu the nao ẩo t  cong cu percentileDsE. S  dung thanh Sdd ẩe (ac nha n so th t  phan v1 can /uan sat, s  dung thanh Remove va Change ẩe loai bo hoa+c thay ẩoi s  (ac nha n ban ẩau -o l3A6ng v9 trV cuIa d3' li 0u (c ntral t nd ncy m asur m nt) • Gia tr1 trung b3nh D4eanE- La gia tr1 trung b3nh so hoc cua mo t bien, ẩ c t%nh bang tong cac gia tr1 /uan sat chia cho so /uan sat. ay la ẩa+c tr ng th ng ẩ c dung cho thang ẩo /ua!ng va ty le . Gia tr1 trung b3nh co ẩa+c ẩiem la ch1u s  tac ẩo ng cua gia tr1 cua mo.i /uan sat, do ẩo ẩay la thang ẩo nhay cam nhat ẩoi v i s  thay ẩoi cua cac gia tr1 /uan sat • Trung v1 D4edianE- La gia tr1 nam gi !a da!y /uan sat Dneu l ng /uan sat la so le!E hoa+c la gia tr1 trung b3nh cua hai /uan sat nam gi !a Dneu so l ng /uan sat la so cha8nE da!y /uan sat ẩ c (a<p (ep theo th t  t  nho ẩen l n. ay la dang cong cu thong ke th ng ẩ c dung ẩe ẩo l ng m c ẩo ta p trung cua dang d ! lie u thang ẩo th t , no co ẩa+c ẩiem la khong b1 anh h ng cua cac gia tr1 ẩau mut cua da!y phan phoi, do ẩo rat th%ch h p ẩe phan t%ch ẩoi v i d ! lie u co s  chenh le ch l n ve gia tr1  hay ẩau mut cua da!y phan phoi • 4ode- La gia tr1 co tan suat (uat hie n l n nhat cua mo t ta p h p cac so ẩo, dang nay th ng ẩ c dung ẩoi v i dang d ! lie u thang bieu danh. Giong nh trung v1, mode khong b1 anh h ng b i gia tr1 ẩau mut cua da!y phan phoi
41. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam -o l3Ang m3*c @o0 phaDn ta*n cuIa d3' li 0u (Disp rsion) • Ch ng sai DMarianceE- Dung ẩe ẩo l ng m c ẩo phan tan cua mo t ta p cac gia tr1 /uan sat (ung /uanh gia tr1 trung b3nh cua ta p /uan sat ẩo • o le ch chuan DStandard deviationE- la mo t cong cu khac dung ẩe ẩo l ng ẩo phan tan cua d ! lie u (ung /uanh gia tr1 trung b3nh cua no. o le ch chuan ch%nh bang ca_n ba c hai cua ph ng sai. M3 ph ong sai la trung b3nh cua cac b3nh ph ng sai le ch cua cac gia tr1 /uan sat t  gia tr1 trung b3nh, do ẩo khao sat ph ng sai th ng cho cac gia tr1 rat l n, kho kha_n cho vie c die.n giai ket /ua. S  dung o le ch chuan se! giup de. dang cho vie c die.n giai do cac ket /ua ẩ a ra sat v i d ! lie u goc h n. • 0hoang bien thien DRangeE- La khoang cach gi !a gia tr1 /uan sat nho nhat va gia tr1 /uan sat l n nhat • Standard Error of 4ean- c dung ẩe ẩo l ng ve s  khac bie t ve gia tr1 trung b3nh cua ma.u nghien c u nay so v i ma.u nghien c u khac trong ẩieu kie n co cung phan phoi. No co the ẩ c dung trong so sanh gia tr1 trung b3nh /uan sat v i mo t gia tr1 ban ẩau nao ẩo Dgia thuyetE va ta co the ket lua n hai gia tr1 nay la khac nhau neu ty so gi !a hie u so cua hai gia tr1 ẩoi v i standard error of mean nam ngoai khoang D-2,g2E -aWc tVnh phaDn pho;i (Distribution)- Co hai ẩai l ng ẩo l ng nh !ng ẩa+c t%nh cua s  phan phoi d ! lie u laT • o le ch DCoefficient of Skewness, CSE cho ta biet dang phan phoi cua cac gia tr1 /uan sat co ẩoi ( ng hay khong. Cong th c t%nh ẩo le ch nh sau 3
    (( i − à) Skewness = nσ3 CS q 0- Cac /uan sat ẩ c phan phoi mo t cach ẩoi ( ng (ung /uanh gia tr1 trung b3nh CS d 0- Cac /uan sat ta p trung chu yeu gan cac gia tr1 nho nhat Dle ch phaiE CS r 0- Cac /uan sat ta p trung chu yeu gan cac gia tr1 l n nhat Dle ch traiE • o nhon DCoefficient of 0urtosis, C0E dung ẩe so sanh m c ẩo phan tan cua ẩ ng cong /uan sat v i dang ẩ ng cong phan phoi chuan. C0 cua phan phoi chuan bang 3. Cong th c t%nh ẩo nhon nh sau 4
    (( i − à) Kurtosis = nσ 4 Tuy nhien trong cac phan mem ng dung he so 0urtosis la ẩo ẩo m c ẩo phan tan cua cac /uan sat (ung /uanh gia tr1 trung b3nh. oi v i phan phoi chuan gia tr1 nay la 0. 6e so 0urtosis d ng ngh2a la cac /uan sat phan tan nhieu h n va
42. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam ẩuoi phan phoi dai h n so v i phan phoi chuan, am ngh2a la cac /uan sat phan tan %t h n va ẩuoi phan phoi nga<n h n. 2. Tho;ng k D moD taI (D scriptiv ) S  dung StatistictsiSummariesiDescriptives ẩe m  ho p thoai mo ta thong ke nh h3nh 28 63nh 28- 6o p thoai descriptives va options ay la mo t dang cong cu khac co the ẩ c dung ẩe tom ta<t d ! lie u va ch5 cho phep thao tac tren dang d ! lie u so. c dung ẩe the hie n (u h ng ta p trung cua d ! lie u Dcentral tendencyE thong /ua gia tr1 trung b3nh cua cac gia tr1 trong bien DmeanE, va mo ta s  phan tan cua d ! lie u thong /ua ph ng sai va ẩo le ch chuan. Chuyen cac bien can tom ta<t vao ho p thoai variables va nhap thanh options ẩe l a
43. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam chon cac thong so thong ke can mo ta, nh gia tr1 trung b3nhpmean, Gia tr1 toi thieu, gia tr1 toi ẩa, ph ng sai va ẩo le ch chuan, ? 3. CoDng cuF EPplor Thu tuc E(plore cho chung ta cac tom ta<t so lie u va cac bieu ẩo cua toan bo so lie u hay cua cac nhom so lie u rien bie t. Ta co the dung thu tuc nay l c /ua so lie u ẩe phat hie n cac gia tr1 bat th ng, (ac ẩ1nh cac c c tr1 DoutliersE, mo ta, kiem tra gia thiet ẩe (ac ẩ1nh (em cac ky! thua t thong ke ẩang dung ẩe phan t%ch so lie u co phu h p khong, co can phai bien ẩoi so lie u neu ky! thua t ẩang dung yeu cau so lie u co phan phoi chuan hay phai dung cac phep kiem phi tham so, thu tuc e(plore cu!ng neu ra cac khac bie t gi !a cac nhom nho Statistics va plots cho ta t%nh cac ẩa+c tr ng va ve! bieu ẩo cua so lie u. Cac thong ke ẩ c t%nh la mean, median, AB trimmed mean, standard error, variance, standard deviation, minimum, ma(imum, range, inter/uartile range, skewness va kurtosis va standard errors cua chung, khoang tin ca y cho trung b3nh Dconfidence interval for the meanE v i ẩo tin ca y tuy y, percentiles, 6uberms 4-estimator, Sndrewms wave estimator, 6ampelms redescending 4-estimator, Tukeyms biweight estimator, A gia tr1 l n nhat va A gia tr1 nho nhat, thong ke 0olmogorov-Smirnov statistic v i m c y ngh2a Lilliefors ẩe kiem tra gia thiet chuan, va thong ke Shapiro-W ilk, bo(plots, stem-and-leaf plots, histograms, normality plots, va spread-versus-level plots v i phep kiem Levene va cac phep bien ẩoi 63nh 2N- 6o p thoai E(plore Neu chon Statistics ta co ho p thoai sau
44. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam 63nh 30- 6o p thoaiE(plore- Statistics M-estimator la mo t c l ng cho khuynh h ng ta p trung co phan bie t trong so cho cac gia tr1 khac nhau. Cac c c tr1 ẩ c gan trong so thap h n cac gia tr1 gan tam. 0hi so lie u co phan phoi ẩoi ( ng trai dai ve hai ph%a hay co cac c c tr1, 4-estimators cho c l ng ve v1 tr% tot h n trung b3nh va trung v1. Ta co bon c l ng la 6uberss 4- estimator, Sndrewss wave estimator, 6ampelss redescending 4-estimator, va Tukeyss biweight estimator M-E stim ators Employment 6uberss 4- Tukeyss 6ampelss 4-Sndrewss Category Estimator Liweight Estimator W ave Current Salary Clerical 2e,fP8.NP 2e,3AA.A0 2e,fef.eA 2e,3PN.30 Custodial 30,ee3.e0 30,A81.11 30,AfN.2N 30,A83.0P 4anager e1,388.N0 AN,820.A2 e1,0AN.e3 AN,f80.3f a The weighting constant is 1.33N. b The weighting constant is P.e8A. c The weighting constants are 1.f00, 3.P00, and 8.A00 d The weighting constant is 1.3P0*pi.
45. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam St m-and-L af Plots Current Salary Stem-and-Leaf Clot for tOLCSTq Clerical hre/uency Stem ) Leaf 2.00 1 . A 1e.00 1 . eeeeefff 1A.00 1 . 88NNNNN 3A.00 2 . 00000011111111111 PP.00 2 . 2222222222222233333333 A3.00 2 . PPPPPPPPPPPPPPPAAAAAAAAAAA AA.00 2 . eeeeeeeeeeeeeffffffffffffff 3A.00 2 . 88888888NNNNNNNNN 30.00 3 . 00000001111111 1N.00 3 . 222333333 1f.00 3 . PPPPAAAA 11.00 3 . eeeff 8.00 3 . 88NN 8.00 P . 000) 3.00 P . 2) 12.00 E(tremes DdqP3NA0E Stem width- 10000 Each leaf- 2 caseDsE ) denotes fractional leaves.
46. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam PHAN TYCH D’ LIEU Co nhieu phep kiem ẩ c s  dung trong SCSS • Neu ta muon so sanh trung b3nh cua ma.u v i mo t gia tr1 co ẩ1nh nao ẩo ta s  dung phep kiem One-sample T test. • 6oa+c neu muon so sanh trung b3nh cua hai nhom, ta s  dung kiem nghie m Independent-samples T test. • e so sanh means cua hai bien ẩ c khao sat t  cung mo t ma.u ta s  dung kiem nghie m Caired-samples T test. • 6oa+c v i tr ng h p ta co nhieu h n hai ma.u ẩo c la p, can kiem nghie m trung b3nh ta co the dung SNOMS mo t yeu to DOne-way SNOMSE. Trong cac tr ng h p tren cac bien ẩ c kiem nghie m trung b3nh ẩoi hoi phai la cac bien ẩ1nh l ng va phan phoi phai la phan phoi chuan hay ma.u nghien c u phai ẩu l n. Tuy nhien v i nh !ng tr ng h p bien /uan sat la bien ẩ1nh l ng nh ng la bien theo thang ẩo th t , hoa+c so l ng ma.u khong ẩu l n hoa+c khong thoa ma!n ẩieu kie n phan phoi chuan ta co the tien hanh kiem nghie m bang cong cu W ilco(on signed rank test Dse! tham khao trong phan Nonparametric test se! ẩ c gi i thie u trong ch ng tr3nh SCSS nang caoE Tr c tien ta se! nghien c u mo t cong cu ẩ n gian trong phan t%ch d ! lie u la 4eans I. M EANS Cong cu M ans dung ẩe t%nh toan cac gia tr1 trung b3nh theo cac nhom nho va ẩ a cac ch5 so thong ke lien /uan cho mo t bien phu thuo c trong pham vi cac nhom cua mo t hay nhieu bien ẩo c la p. Ta co the l a chon cac cong cu kem theo nh phan t%ch SNOMS mo t yeu to, eta, va cac kiem nghie m tuyen t%nh. M% du ta co the ẩo l ng m c ẩo ẩanh gia trung b3nh ve mo t show /uang cao cua ba nhom tieu dung khac nhau, cong nhan, sinh vien va cong ch c. Cong cu nay se! cho ta mo t bang cheo the hie n s  ẩanh gia cua ba nhom ng i nay ve show /uang cao ẩ c (em. Cong cu nay ẩ n gian ch5 truy (uat cac ket /ua thong ke /uan sat ẩ c, cac phep kiem khong ẩ c ẩe ca p trong phan nay e th c hie n cong cu nay ta chon Compar M ans/M ans[. t  menus, ta co ho p thoai nh h3nh 1N. Co the chon mo t hay nhieu bien phu thuo c. Di chuyen ve t ẩen ẩen bien ch a cac gia tr1 ẩ1nh l ng ma ta can /uan sat gia tr1 trung b3nh cua cac gia tr1 ẩ1nh l ng ẩo trong cac nhom (ac ẩ1nh b i bien ẩo c la p. S  dung mui ten chuyen bien ẩa! chon vao ho p thoai d p nd nt list.
47. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam Cac bien phu thuo c trong bang 4eans phai la bien ẩ1nh l ng va cac bien ẩo c la p th ng la cac bien ẩ1nh danh. Cac ẩai l ng thong ke ẩ c s  dung tuy thuo c vao dang d ! lie u. Nh mean va stadard deviation th3 d a tren ly thuyet phan phoi chuan va th%ch h p cho cac bien ẩ1nh l ng v i phan phoi ẩoi ( ng. Cac ẩai l ng khac nh median va range th3 th%ch h p cho cac bien ẩ1nh l ng ma ta khong biet lie u no co thoa ma!n cac ẩieu kie n ve phan phoi chuan hay khong. Ta co the l a chon SNOMS va eta ẩe th c hie n vie c phan t%ch s  bien thien mo t chieu cho mo.i bien ẩo c la p. Eta va eta b3nh ph ng cho phep ẩo l ng cac moi t ng /uan Lien ẩo c la p la bien dung ẩe chia cac gia tr1 cua bien phu thuo c thanh nh !ng nhom nho. Co hai cach ẩe l a chon bien ẩo c la p • L a chon mo t hoa+c nhieu bien ẩo c la p. Luc nay cac ket /ua cu!ng nh cac ẩai l ng thong ke kem theo se! ẩ c the hie n tren cac bang rieng bie t cho mo.i bien ẩo c la p • L a chon bien ẩo c la p theo l p, mo.i bien ẩo c la p trong mo t l p, luc nay cac ket /ua va ẩai l ng thong ke ẩ c the hie n tren chung mo t bang 63nh 31- 6o p thoai 4eans Cong cu Options Dh3nh 32E. Cho phep ta l a chon cac ẩai l ng thong ke can khao sat va SNOMS, Eta, va Eta b3nh ph ng Dse! ẩ c ẩe ca p chi tiet ve y ngh2a  phan sauE
48. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam 63nh 32- 6o p thoai Options II. ONE-SAM PLE T TEST Chep kiem mo t ma.u ẩ c dung ẩe kiem ẩ1nh (em gia tr1 trung b3nh cua mo t bien co khac v i mo t gia tr1 ẩa! cho khong. M% du mo t nha nghien c u co the kiem ẩ1nh (em ch5 so I9 trung b3nh cua mo t nhom sinh vien co bang 100 v i m c y ngh2a AB khong. Ch ng phap kiem nghie m nay dung cho cac bien co thang ẩo /ua!ng hay t5 le . Gia thiet 6 va cong th c t%nh t nh sau On -sampl T t st 6- 4ean q Test Malue 0- 4ean ≠ Test Malue Mean Difference  t = ↔ Sig.(2 tailed) Standard Error of Mean Ta co /uy ta<c ket lua n ve gia thiet 6 nh sau- Sig. nho Dr 0.0A cha8ng hanE ngh2a la bac bo gia thiet 6, ngh2a la trung b3nh khac v i test value. L u y la /uy ta<c ket lua n ẩ n gian nay co the ap dung cho hau het cac phep kiem.
49. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam T  4enus ta chon Compar M an\On -Sampl T T st[ ta se! co ho p thoai 63nh 33- 6o p thoai One-Sample T Test L a chon bien can so sanh bang cach di chuyen ve t ẩen va chuyen ẩen vao ho p thoai T st Variabl (s), nha p gia tr1 can so sanh vao ho p thoai T st Valu . Chon cong cu Options ẩe (ac ẩ1nh ẩo tin ca y cho kiem nghie m, ma+c ẩ1nh la NAB va cach ( ! ly ẩoi v i cac gia tr1 khuyet. 0hi kiem nghie m cac bien ta se! ga+p mo t vai gia tr1 khuyet trong cac bien ẩo va van ẩe  ẩay la ta phai chon cach loai bo cac gia tr1 khuyet ẩo • EPclud cas s analysis by analysis. 4o.i kiem nghie m T s  dung toan bo cac tr ng h p DcasesE ch a ẩ ng gia tr1 co y ngh2a ẩoi v i bien ẩ c kiem nghie m. a+c ẩiem cua l a chon nay la k%ch th c ma.u luon thay ẩoi theo t ng kiem nghie m • EPclud cas s listwis . 4o.i kiem nghie m T s  dung ch5 nh !ng tr ng h p co gia tr1 ẩay ẩu ẩoi v i tat ca cac bien . Trong tr ng h p nay k%ch th c ma.u luon khong ẩoi trong tat ca cac phep kiem 63nh 3P- L a chon cach (  ly gia tr1 khuyet e tien hanh kiem nghie m T mo t ma.u ẩoi hoi d ! lie u phai ẩap ng gia ẩ1nh sau • D ! lie u phai la phan phoi chuan, hoa+c
50. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam • 0%ch th c ma.u phai ẩu l n ẩe ẩ c (em la (ap (5 phan phoi chuan III. INDEPENDENT-SAM PLES T TEST 0iem nghie m nay dung cho hai ma.u ẩo c la p, dang d ! lie u la dang thang ẩo khang cach hoa+c ty le oi v i dang kiem nghie m nay, cac ẩoi t ng can kiem nghie m phai ẩ c (ep mo t cach nga.u nhien vao hai nhom sao cho bat ky mo t khac bie t nao t  ket /ua nghien c u la do s  tac ẩo ng cua ch%nh nhom th  ẩo, ch khong phai do cac yeu to khac. M% du nh ta khong the dung ph ng phap nay ẩe so sanh thu nha p cua nam va n ! b i v3 thu nha p con b1 anh h ng l n b i tr3nh ẩo hoc van va nghe nghie p. 6oa+c ẩe ẩanh gia tac ẩo ng cua mo t ch ng tr3nh /uang cao ta l a chon ra hai nhom khach hang ẩo c la p, nhom ẩa! (em /ua ch ng tr3nh /uang cao va nhom ch a (em /ua ch ng tr3nh /uang cao ẩe ẩanh gia m c ẩo a th%ch cua san pham ẩa! ẩ c /uang cao. bc ẩay ngoai cong cu th  la vie c (em /uang cao hoa+c khong (em, nha nghien c u phai bao ẩam khong ton tai yeu to nao ẩang ke tac ẩo ng ẩen s  ẩanh gia ve san pham, nh gi i t%nh, s  tieu dung, tr3nh ẩo , ? Tom lai ẩe so sanh gia tr1 trung b3nh Dve s  a th%ch, thu nha p, chi tieu, ? E cua hai nhom ẩo c la p ta phai thiet ke th% nghie m sao cho cac phan ng thu ẩ c cua nhom nay khong b1 anh h ng b i nhom kia, va ngoai cac tac nhan can ẩanh gia can phai chu y ẩen cac tac ẩo ng khac co the lam thay ẩoi s  phan ng thu nha n ẩ c gi !a hai nhom Me ma+t /uy tr3nh, tr c khi ẩi vao cac kiem nghie m trung b3nh ta can phai tham khao mo t kiem nghie m khac ma ket /ua cua no la rat /uan trong cho cac kiem nghie m trung b3nh sau nay. 0iem nghie m Levene la phep kiem t%nh ẩong nhat cua ph ng sai. bc ẩay ta kiem nghie m gia thuyet cho rang ph ng sai cua cac ma.u /uan sat la bang nhau. Neu ket /ua kiem nghie m cho ta m c y ngh2a nho h n AB, ta co the khong chap nha n gia! thuyet cho rang ph ong sai hai ma.u bang nhau. Chu y trong mo t so kiem nghie m nh SNOMS, kiem nghie m t, ? ẩoi hoi phai tien hanh phep kiem L v n tr c ẩe (ac ẩ1nh t%nh ẩong nhat cua cac ph ng sai. 0et /ua nay se! anh h ng ẩen vie c l a chon cac kiem nghie m trung b3nh khac D0iem nghie m trung b3nh v i ph ng sai ma.u bang nhau hoa+c kiem nghie m trung b3nh v i ph ng sai ma.u khong bang nhauE Gia thiet 6 va cong th c t%nh t trong cac tr ng h p ph ng sai bang nhau va khac nhau nh sau Ind p nd nt-Sampl s T T st: 6- 4ean Difference q 0 0- 4ean Difference ≠ 0 Levene's Test:
51. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam 6- E/ual Mariance 0- une/ual Mariance • E/ual Mariance- Mean Difference  t = ↔ Sig.(2 tailed) Standard Error of Mean Difference l − l t = 1 2 2 2  ’ (N1 −1)S1 + (N 2 −1)S2 1 1  +  N1 + N 2 − 2  N1 N 2  df q N1gN2 -2 • une/ual Mariance- Mean Difference  t = ↔ Sig.(2 tailed) Standard Error of Mean Difference l − l t = 1 2  2 2 ’  S1 S2   +   N1 N 2  2 2 S1 S2 ω1 = , ω2 = N1 N2 (ω + ω )2 df = 1 2 ω2 ω2 1 + 2 N1 −1 N 2 −1 e th c hie n vie c so sanh nay ta vao Compar m ans\Ind p nd nt sampl t- t st[. Cac d ! lie u can so sanh nam trong cung mo t bien ẩ1nh l ng. e so sanh ta tien hanh nhom cac gia tr1 thanh hai nhom nh  bien grouping. T  4enus ta ẩ c ho p thoai nh h3nh 3A
52. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam 63nh 3A- 6o p thoai Independent sample T-test Di chuyen ve t ẩen vao bien ẩ1nh l ng ma ta can so sanh gia tr1 trung b3nh, chon bang cach nhan nut mu!i ten ẩe chuyen bien ẩ1nh l ng ẩo vao ho p thoai T st variabl (s). Ta co the chon nhieu bien ẩ1nh l ng ẩe so sanh. Di chuyen ve t toi ẩen bien ẩ1nh cac nhom can so sanh v i nhau Dth ng la bien ẩ1nh danhE di chuyen vao ho p thoai Gouping variabl . Cong cu D fin Groups[ cho phep ta ẩ1nh ra hai nhom can so sanh v i nhau, nh h3nh 3e 63nh 3e- 6o p thoai Define Groups Co hai cach ẩ1nh nhom so sanh- • S ! dung gia tr1 cu the- nha p hai gia tr1 ẩai die n cho hai nhom can so sanh trong bien vao o group 1 va6 group 2. M% du ẩe so sanh th i gian t  hoc cua hai nhom sinh vien na_m nhat va sinh vien na_m cuoi, ta dung bien loai sinh vien v i P nhom sinh vien ẩ  c ma! hoa nh sau- sinh vien na_m nhat, sinh vien na_m hai, sinh vien na_m ba, sinh vien na_m cuoi lan l t co ma! la 1,2,3,P. Ta nha p gia tr1 1 vao Group 1 va nha p gia tr1 P vao group 2. Luc ẩo th i gian t  hoc trung b3nh se! ẩ c so sanh gi !a hai nhom sinh vien na_m nhat va sinh vien na_m cuoi.
53. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam • Cach th hai la s  dung Cut point: nha p gia tri phan cach cac gia tr1 trong bien thanh hai nhom. Toan bo cac tr ng h p co gia tr1 nho h n gia tr1 ẩ c nha p vao trong cut point se! ẩ1nh ra mo t nhom, va toan bo cac tr ng h p co gia tr1 l n h n hoa+c bang gia tr1 trong Cut point se! tao ra mo t nhom khac. M% du ta muon so sanh th i gian t  hoc cua sinh vien hai na_m ẩau va sinh vien hai na_m cuoi, ta nha p gia tr1 3 Dla gia tr1 ma! hoa cua nhom sinh vien na_m th baE vao cut point, luc ẩo ta tao ẩ c hai nhom sinh vien bao gom, sinh vien hai na_m ẩau Dsinh vien na_m th nhat va sinh vien na_n th haiE va nhom sinh vien hai na_m cuoi Dsinh vien na_m ba va sinh vien na_m cuoiE va se! tien hanh so sanh so th i gian t  hoc trung b3nh tren hai nhom sinh vien nay. Cong cu Options  ẩay co thao tac va y ngh2a giong cong cu Options ẩa! ẩe ca p trong phan kiem nghie m T mo t ma.u ẩa! ẩe ca p  phan tr c. Cac gia ẩ1nh phai ẩ c thoa ma!n khi dung kiem nghie m T cho hai ma.u ẩo c la p • 0hi dung kiem nghie m t cho hai ma.u co ph ng sai bang nhau Dco the kiem ẩ1nh gia thiet nay bang thong ke L v n E, cac /uan sat phai ẩo c la p, ẩ c lay nga.u nhien t  cac ẩam ẩong co phan phoi chuan v i ph ng sai bang nhau • Dung kiem nghie m t cho hai ma.u co ph ng sai khong bang nhau khi cac /uan sat phai ẩo c la p, ẩ c lay nga.u nhien t  tong the co phan phoi chuan. IV. PAIRED-SAM PLES T TEST ay la dang kiem nghie m dung cho hai bien co lien he v i nhau, d ! lie u dang thang ẩo /ua!ng hoa+c ty le . No t%nh hie u so cua cac gia tr1 cua hai bien cho mo.i tr ng h p va kiem nghie m (em hie u so trung b3nh co khac 0 hay khong L i ẩiem cua vie c s  dung kiem nghie m T cho ma.u ca+p la ta loai tr  ẩ c nh !ng yeu to tac ẩo ng ben ngoai vao nhom th . M% du ẩe khao sat s  a th%ch cua hai loai n c hoa chuan b1 tung ra th1 tr ng, tien hanh th  nghie m tren cung mo t nhom ma.u se! cho nh !ng thong tin (ac th c h n ve s  a th%ch mui v1 hai loai n c hoa nay, t  ẩo co the ta p trung vao s  khac bie t t  nhien cua chung. Neu ta tien hanh so sanh gi !a hai nhom ma.u ẩo c la p v i nhau, ket /ua khac bie t co the do nh !ng tac nhan khac gay ra nh s  khac bie t ve con ng i, ve nha n th c, ve kinh nghie m cu!ng nh cac yeu to ben ngoai khac. Ch ng phap nay th%ch h p cho vie c kiem nghie m san pham, no kiem nghie m gia thuyet cho rang s  khac bie t gi !a hai trung b3nh ma.u la bang khong. Ta t  choi gia thuyet nay khi m c y ngh2a cua ket /ua kiem nghie m DsignificanteE nho h n m c y ngh2a cho tr c Dth ng la ABE. ieu kie n yeu cau cho loai kiem nghie m nay la k%ch c  hai ma.u so sanh phai bang nhau. Cac /uan sat trong mo.i ma.u phai ẩ c th c hie n trong cung nh !ng ẩieu kie n giong nhau. 6ie u so cac gia tr1 cua hai ma.u phai co phan phoi chuan hoa+c c  ma.u
54. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam ẩu l n ẩe (ap (5 phan phoi chuan. Ch ng sai cua hai bien co the bang hoa+c khong bang nhau Dco the kiem nghie m /ua phep kiem ph ng sai LeveneE Gia thiet 6 va cong th c t%nh t nh sau Pair d-Sampl s T T st 6- 4ean Difference q 0 0- 4ean Difference ≠ 0 Mean Difference  t = ↔ Sig.(2 tailed) Standard Error of Mean Difference e th c hie n vie c so sanh nay ta vao Compar m ans\Pair d-sampl s t-t st[. T  4enus ta ẩ c ho p thoai nh h3nh 2A- 63nh 3f- Chep kiem T cho ma.u ca+p Chon hai bien can so sanh bang cach di chuyen ve t ẩen lan l t ẩen hai bien can /uan sat, di chuyen bien can /uan sat vao ho p thoai Pair d Variabl s bang nut mu!i ten. Pair d-sampl s t t st con cho ta ket /ua ve moi t ng /uan gi !a hai bien ẩang /uan sat. Cho biet lie u hai bien nay co t ng /uan v i nhau hay khong, ẩo t ng /uan va chieu t ng /uan Dthe hie n  bang Caired samples correlationE. V. ONE-_ AY ANOVA SNOMS la mo t cong cu thong ke dung ẩe so sanh nhieu gia tr1 trung b3nh v i nhau. M% du nh trong nong nghie p ng i ta muon biet ngu! coc se! phat trien nh
55. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam the nao khi s  dung cac loai phan bon khac nhau. Nha nghien c u muon biet lie u tat ca cac loai phan bon tren co anh h ng nh nhau ẩen s  phat trien cua ngu! coc hay mo t vai loai phan bon se! co tac dung tot h n mo t vai loai khac. e lam ro ẩieu nay ng i ta dung SNOMS kiem nghie m toc ẩo phat trien trung b3nh Dco the la l ng ngu! coc thu hoach, chieu cao cua cay, so l ng trai trung b3nh thu hoach ẩ c, ? E khi dung cac loai phan bon khac nhau, ẩay ch%nh la cac gia tr1 trung b3nh ẩ c s  dung trong thong ke. SNOMS th ng kiem nghie m so l ng ma.u l n h n hai, neu so l ng ma.u bang 2 ta co the dung ph ng phap t ng ẩoi ẩ n gian h n la phep kiem t hai ma.u nh ẩa! ẩe ca p  phan tren. SNOMS ẩ c s  dung ro ng ra!i trong th c te b i v3 ta se! ga+p rat nhieu tr ng h p ẩoi hoi ta phai kiem nghie m nhieu ma.u cung mo t luc. Chu y neu ta kiem t ng ca+p lan l t bang ph ng phap kiem nghie m t hai ma.u, mo.i lan kiem kha na_ng sai la AB Dtuy thuo c vao m c y ngh2a ta mong muonE. Do ẩo khi kiem nghie m tat ca cac ca+p ma.u lan l t ty le sai sot se! ta_ng len theo mo.i lan, trong khi SNOMS cho phep ta kiem nghie m tat ca cac ma.u cung mo t luc v i m c ẩo sai sot la AB e th c hie n kiem nghie m SNOMS, d ! lie u ẩoi hoi phai thoa ma!n mo t so gia thuyet sau- • Cac ma.u kiem nghie m phai ẩo c la p va ẩ c chon nga.u nhien • Cac ma.u s  dung trong kiem nghie m phai co phan phoi chuan hoa+c k%ch th oc ma.u ẩu l n ẩe ẩ c (em nh phan phoi chuan. • Ch ng sai cua cac ma.u phai ẩong nhat Dco the kiem nghie m ẩieu nay bang phep kiem LeveneE Neu nh cac ma.u nghien c u khong thoa ma!n cac ẩieu kie n tren ta co the dung phep kiem phi tham so DnonparametricE nh phep kiem Kruskal-_ allis Gia thiet 6 va cac cong th c t%nh toan trong SNOMS nh sau ONE-W SY SNOMS- 6- à1 q à2 q q ài q q àn 2 2 Sum of S/uares Letween Groups q SSL q
    i Ni8i - N8 df q n -1 2 2 Sum of S/uares W ithin Groups q SSW q
    iv
    w Niwli - Ni8i x df q N p n 2 2 Sum of S/uares Total q SST q
    iw Niwliw - N8 df q N - 1
56. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam ANOVA Sum of Squares df Mean Square F Sig. Current Between 88653535061.984 9 9850392784.665 92.779 .000 salary Groups Within 49262960374.356 464 106170173.221 Groups Total 137916495436.340 473 Neu bac bo 6- à1 q à2 q q ài q q àn, tien hanh tiep Cost hoc ẩe (ac ẩ1nh cac trung b3nh nao khac nhau. Chai lam Levene test tr c ẩe (ac ẩ1nh (em cac ph ng sai trong t ng nhom co ẩong nhat khong. M% du minh hoa- Cac nha che bien va phan pho% coffee  th1 tr ng 6oa 0y ẩang ẩoi ma+t v i t3nh h3nh bat on ve gia cua hat coffee. Trong mo t na_m gia cua hat coffee bien ẩo ng t  $1.P0 R pound D0.3f3 kgE len $2.A0 R pound roi sau ẩo lai tut (uong $2.03 R pound. Ng i ta (ac ẩ1nh s  bat on ve gia coffee nay la do t3nh h3nh hoat ẩo ng cua cac nha che bien va phan phoi coffee va mo t yeu to khac rat /uan trong la van ẩe han han  Lraail, b i v3 Lraail san (uat ra 30B san l ng coffee tren the gi i, do ẩo th1 tr ng coffee rat nhay cam v i nh !ng bien chuyen ve th i tiet Dnguy c han hanE  Lraail. e tao s  on ẩ1nh cho hoat ẩo ng cua m3nh mo t nha phan phoi coffee muon loai bo ma+t hang coffee Lraail ra khoi c cau hang hoa cua m3nh. Tuy nhien tr c khi th c hie n /uyet ẩ1nh nay can phai can nha<c lie u loai bo ma+t hang coffee Lraail co lam giam doanh so cua cong ty hay khong. M3 va y cong ty thue mo t cong ty nghien c u 4arketing tien hanh kiem nghie m s  a th%ch mui v1 coffee cua khach hang tieu dung coffee tren th1 tr ng. Cong ty tien hanh khao sat ba nhom khach hang ẩ c chon nga.u nhien bao gom nhom khach hang chuyen tieu dung coffee Lraail, nhom khach hang chuyen tieu dung coffee Colombia va nhom khach hang chuyen tieu dung coffee Chau Chi Dẩay la 3 loai coffee ẩ c tieu dung chu yeu cua cong tyE. Chu y cong ty loai tr  nh !ng nhom khach hang tieu dung nhieu loai coffee khac nhau ẩe bao ẩam t%nh ẩo c la p cua cac ma.u ẩ c chon, va do nghien c u ve mui v1 nen ẩoi hoi phai chon nh !ng khach hang co gu tieu dung rieng bie t. bc ẩay cong ty muon (ac ẩ1nh (em lie u co s  khac bie t ve m c ẩo a th%ch ẩoi v i ba loai coffee Dse! cho khach hanh th  ba loai coffee va khao sat m c ẩo a th%ch chungE va neu co s  khac nhau th3 s  khac nhau ẩo (ay ra  nh !ng loai nao. D a vao ket /ua phan t%ch SNOMS ta biet lie u m c ẩo a th%ch trung b3nh cua ba nhom khach hang tren co giong nhau khong. Neu khac nhau th3 ta tien hanh tiep cac phep kiem trong Post Hoc ẩe (ac ẩ1nh nh !ng khac bie t cua t ng nhom khach hang ve loai coffee ẩa! th .
57. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam Sau khi dung SNOMS khao sat s  khac bie t gi !a cac ma.u. Neu ta co ẩu c s  ẩe ket lua n la khong co s  khac bie t gi !a cac ma.u, ta co the ket thuc cong vie c Dvie c loai bo coffee braail khong gay anh h ng ẩen doanh so, ng i tieu dung co the chuyen sang coffee comlobia hoa+c chau Chi mo t cach de. dangE. Tuy nhien khi ta loai bo gia thiet ve s  ngang bang gi !a cac m c ẩo a th%ch, ta phai (ac ẩ1nh tiep s  khac bie t (ay ra  ẩau gi !a cac ma.u kiem nghie m. Chung ta can phai (ac ẩ1nh h ng va ẩo l n cua cac khac bie t nay bang cach lan l t so sanh cac ma.u v i nhau Dng i tieu dung coffee braail co the th%ch coffee comlombia h n coffe chau Chi, hoa+c ng i tieu dung coffee braail ẩanh gia coffee braail ngang bang v i coffee colombia, trong khi m c ẩo a th%ch coffee chau Chi th3 thap h n do ẩo ẩe giam thieu s  mat doanh so ban coffee braail khi loai bo ma+t hang nay cong ty nen ta_ng l ng coffee comlombia tieu thu tren th1 tr ngE, cac cong cu thong ke trong Post Hoc cho phep ta th c hie n cong vie c nay. Chan t%ch ph ng sai mo t yeu to la tien tr3nh phan t%ch ph ng sai mo t bien ẩ1nh l ng phu thuo c vao mo t yeu to ẩ n le hay con goi la bien ẩo c la p. Chan t%ch ph ng sai DSNOMSE ẩ c dung ẩe kiem nghie m gia thuyet cho rang tat ca cac gia tr1 trung b3nh ẩeu bang nhau. 0y thua t nay la mo t dang m  ro ng cua kiem nghie m T hai ma.u. e (ac ẩ1nh s  khac bie t gi !a cac gia tr1 trung b3nh chung ta co the muon biet nh !ng gia tr1 trung b3nh nao khac bie t. Co hai cach ẩe so sanh s  khac bie t nay la kiem nghie m priori contrasts va post hoc . Contrasts la kiem nghie m ẩ c ẩ a ra tr c khi th c hie n phep th , va post hoc la kiem nghie m ẩ c th c hie n sau khi phep th  ẩa! ẩ c th c hie n. Chung ta con co the kiem nghie m nh !ng (u h ng /ua cac nhom On -_ ay ANOVA Contrasts Chung ta co the chia tong b3nh ph ng ẩo le ch gi !a cac nhom DSSLE thanh cac thanh phan the hie n (u h ng hay (ac ẩ1nh priori contrasts. Polynomial chia SSL thanh cac thanh phan the hie n (u h ng. Chung ta co the kiem (em bien phu thuo c co thay ẩoi theo cac m c ẩo Dtheo mo t th t  nao ẩoE cua bien yeu to theo mo t (u h ng nao khong. Cha8ng han nh chung ta co the kiem (em tien l ng co thay ẩoi theo (u h ng tuyen t%nh /ua cac loai cong vie c t  thap ẩen cao khongY D gr Co the chon ẩa th c cap 1, 2, 3, P, hay A Co ffici nts Lang thong ke t chung ta ẩa! (ac ẩ1nh priori contrasts ẩe kiem nghie m. Nha p cac he so cho t ng nhom cua bien yeu to va nhap Sdd sau mo.i lan
58. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam nha p. 4o.i gia tr1 m i ẩ c ẩ a vao cuoi danh sach he so. e (ac ẩ1nh them mo t bo contrasts, nhap Ne(t. Dung Ne(t va Crevious ẩe (em cac ta p contrasts. Th t  cua cac he so rat /uan trong v3 no t ng ng v i th t  ta_ng dan cua cac gia tr1 cua bien yeu to. 6e so th nhat t ng ng v i gia tr1 nho nhat va he so cuoi cung t ng ng v i gia tr1 l n nhat. Cha8ng han neu co e gia tr1 trong bien yeu to, cac he so 1, 0, 0, 0, 0.A, va 0.A se! t ng phan nhom th nhat v i nhom th na_m va th sau. Trong hau het cac ng dung, tong cac he so la 0. Neu tong khac 0 /uy tr3nh cu!ng ẩ c th c hie n nh ng se! co thong bao l u y M% du Lanh ran hap thu dau a_n hay m ! theo nh !ng so l ng khac nhau khi ta ran banh 4o t th% nghie m ẩ c th c hie n tren ba loai chat beo gom dau pho ng, dau ngu coc va m ! l n. Dau pho ng va dau ngu coc la chat beo khong bao hoa, m ! l n la chat beo bao hoa. Ngoai vie c (ac ẩ1nh l ng chat beo hap thu co phu thuo c vao loai dau s  dung hay khong, ta con co the tien hanh kiem nghie m priori contrasts ẩe (ac ẩ1nh l ng chat beo hap thu co phu thuo c vao t%nh chat bao hoa hay khong bao hoa cua chat beo ẩ c s  dung hay khong. 0hi ẩa! (ac ẩ1nh ẩ c s  khac bie t gi !a cac gia tr1 trung b3nh, cac kiem nghie m post hoc rang va pairwis multipl comparisons co the (ac ẩ1nh ẩ c nh !ng gia tr1 trung b3nh nao khac bie t. Rang t sts (ac ẩ1nh ẩ c nh !ng nhom gia tr1 trung b3nh ẩong nhat Dkhong ton tai s  khac bie t gi !a cac gia tr1 trung b3nh nayE. 0iem nghie m Pairwis multipl comparisons cho biet s  khac bie t gi !a cac ca+p gia tr1 trung b3nh va ẩ a ra mo t ma tra n ẩanh dau hoa th1 ch5 ra nh !ng ca+p gia tr1 trung b3nh co khac bie t ẩang ke  m c y ngh2a AB 0hi gia thuyet ve s  ẩong nhat cua cac ph ng sai ẩ oc chap nha n Dthong /ua kiem nghie m LeveneE ta co cac ph ng phap kiem nghie m thong ke sau ẩe so sanh cac trung b3nh- • Th l ast significant diff r nc (LSD) la phep kiem t ng ẩ ng v i vie c s  dung phep kiem t lan l t cho toan bo cac ca+p bien. Yeu ẩiem cua ph ng phap nay la no khong ẩieu ch5nh m c y ngh2a cho t ng th%ch v i vie c so sanh nhieu bien cung mo t luc. Do ẩo da.n ẩen ẩo tin ca y khong cao. Cac kiem nghie m sau ẩay loai bo ẩ c yeu ẩiem nay bang cach ẩieu ch5nh m c y ngh2a khi phai ẩong th i so sanh nhieu bien. • Chep kiem Bonf rroni va Tuk yas hon stly significant diff r nc ẩ c s  dung cho hau het cac so sanh ẩa bo i. Sidakas t t st cu!ng ẩ c s  dung t ng t  nh Bonf rroni nh ng no cung cap nh !ng gi i han cha+t che h n DYE. 0hi tien hanh kiem nghie m mo t so l ng l n cac ca+p trung b3nh Tuk yas hon stly significant diff r nc t st se! hie u /ua h n la Bonf rroni t st. Ma ng c lai Bonf rroni th%ch h p h n cho cac kiem nghie m co so l ng ca+p so sanh %t.
59. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam • Hochb rgas GT2 cu!ng giong nh Tuk yas hon stly significant diff r nc t st nh ng thong th ng Tuk yas t st hie u /ua h n. Gabri las pairwis comparisons t st th3 giong nh Hochb rgas GT2 nh ng no th ng ẩ c s  dung khi cac c  ma.u co s  sai bie t l n • Chep kiem Dunn ttas pairwis ẩ c dung ẩe so sanh cac gia tr1 trung b3nh cua cac ma.u v i mo t g%a tr1 trung b3nh cu the ẩ c lay t  trong ta p cac ma.u so sanh. Thong th ng ma+c ẩ1nh ma.u cuoi cung la nhom kiem soat, hoa+c ta co the l a chon nhom ẩau tien la nhom kiem soat, luc ẩo cac gia tr1 trung b3nh cua cac nhom trong bien ẩo c la p se! ẩ c so sanh v i gia tr1 trung b3nh cua nhom ẩau tien hoa+c nhom sau cung cua bien ẩo c la p • Ryan, Einot, Gabri l, and _ lsch (R-E-G-_ ) ẩ a ra hai b c kiem nghie m. au tien tien hanh kiem nghie m toan bo cac gia tr1 trung b3nh (em co bang nhau hay khong. Neu toan bo cac gia tr1 trung b3nh khong bang nhau th3 b c th hai se! kiem nghie m s  khac bie t gi !a cac nhom nho v i nhau ẩe t3m ra nh !ng nhom nao tha t s  khac bie t ve gia tr1 trung b3nh. Tuy nhien vie c kiem nghie m nay khong nen th c hie n ẩoi v i tr ng h p k%ch c  ma.u cua cac nhom khong ngang bang nhau • Thong th ng khi k%ch th c ma.u khong ngang bang gi !a cac nhom. Bonf rroni va Sch ff * la hai ph ng phap kiem nghie m ẩ c l a chon h n la ph ng phap Tuk y • Duncanas multipl rang t st, Stud nt-N wman-K uls (S-N-K), va Tuk yas b cu!ng t ng t  tuy nhien no %t ẩ c s  dung nh cac ph ng phap tren. • 0iem nghie m _ all r-Duncant ẩ c s  dung khi cac k%ch th c ma.u khong bang nhau • Ch ng phap Sch ff * cho phep kiem nghie m cac ket h p tuyen t%nh cua nh !ng gia tr1 trung b3nh, khong ch5 so sanh gi !a cac ca+p. Ch%nh v3 va y ket /ua cua kiem nghie m Sch ff * th ng tha n trong h n cac ph ng phap kiem nghie m khac, no ẩoi hoi mo t s  khac bie t l n gi !a cac gia tr1 trung b3nh ẩe bao ẩam s  khac bie t tha t s  • 0hi gia thiet ve s  ẩong nhat cua cac ph ng sai khong ẩ c chap nha n ta se! s  dung cac phep kiem Tamhan as T2, Dunn ttas T3, Gam s-How ll, Dunn ttas C ẩe so sanh cac ca+p gia tr1 trung b3nh cua cac nhom e th c hie n phep kiem SNOMS ta vao Comapr m ans\On -_ ay ANOVA[ t  thanh menus ẩe truy (uat ra ho p thoai nh h3nh 2e. Di chuyen ve t toi ẩen cac bien ẩ1nh l ng can phan t%ch, chuyen sang ho p thoai D p nd nt List. Chon bien kiem soat, con goi la bien ẩo c la p Dyeu cau phai co ba gia tr1 tr  lenE, ẩ a vao ho p thoai Tactor. Lien kiem soat nay cho phep ta phan cac gia tr1 cua bien phu thuo c thanh
60. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam nhieu nhom ẩe so sanh. SNOMS cho phep ta ẩ a ra ket lua n lie u cac trung b3nh cua cac nhom co bang nhau hay khong. 63nh 38- Chan t%ch SNOMS mo t yeu to Neu m c y ngh2a cua phep kiem SNOMS nho Dth ng so v i ABE, ta tien hanh so sanh tiep cac ca+p gia tr1 trung b3nh cua cac nhom bang cong cu Post Hoc nh trong ho p thoai h3nh 3N va l a chon cac ph ng phap kiem nghie m th%ch h p 63nh 3N- Cong cu Cost 6oc ẩe so sanh cac ca+p trung b3nh trong SNOMS L a chon Options cho ta ho p thoai nh h3nh P0. Ta co the hien th1 cac thong ke mo ta bang cach chon D scriptiv va kiem ẩ1nh t%nh ẩong nhat cua ph ng sai bang thong ke L v n Dket /ua kiem ẩ1nh nay /uyet ẩ1nh s  l a chon ph ng phap
61. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam kiem nghie m trong phan Post HocE. Cong cu M ans Plot dung ẩe hien th1 ẩo th1 ve gia tr1 trung b3nh cua cac nhom. Cong cu M issing Valu s dung ẩe kiem soat cac gia tr1 khuyet nh ẩa! tr3nh bay  cac phan tr c 63nh P0- L a chon Options trong SNOMS Cac gia ẩ1nh phai ẩ c thoa ma!n khi dung phan t%ch SNOMS mo t yeu to • Cac ma.u d ! lie u phai ẩo c la p, ẩ c chon nga.u nhien t  mo t tong the phan phoi chuan • Trong tong the cac ph ng sai cua cac ma.u d ! lie u phai bang nhau Dẩieu nay se! ẩ c kiem nghie m bang thong ke L v n Levene's Test: 6- 6omogeneity of Mariance • E/ual Mariances- Co the dung cac tests nh LSD, Lonferroni, Sidak, Scheffe, Tukey • une/ual Mariances- Co the dung cac tests nh Tamhaness T2, Dunnettss T3, Games-6owell Test of Homogeneity of Variances Current Salary Levene Statistic df1 df2 Sig. 14.473 9 464 .000
62. Phan tch va x  ly soã lieu bang SPSS Tran uang Trung ! "ao hoai Nam -AbI HObC KINH TE/ TP HCM PHAN TYCH VA c LY SO/ LIEU BAJNG SPSS TH.Sd TRAKN NUANG TRUNG KHOA NUAN TR. KINH DOANH -AO HOAI NAM KHOA TH NG M AbI-DU L.CH