Download - Nota kursus-asas-spss-28-29-april-2014-plpp
28-29 April 2014
© Muhammad Amirul Abdullah 2014 1
Muhammad Amirul bin Abdullah
E-mel: [email protected]
Blog: cikguamirul.wordpress.com
1
TARIKH
/ MASA8.30 - 10.30 pagi
10.30 -
11.00 pagi11.00 pg - 1.00 tgh
1.00 tgh - 2.30
ptg2.30 - 4.30 ptg
4.30 -
5.00
ptg
28 April
(Isnin)
1.0 Asas Penyelidikan
2.0 Mengenali SPSS
2.1 Membuka perisian
2.2 Menyediakan
template
2.3 Memasukkan data
MINUM PAGI
3.0 Modifying the Data
File
4.0 Screening & Cleaning
Data
5.0 Manipulating the
Data REHAT &
MAKAN
TENGAHARI
6.0 Descriptive
Statistics
7.0 Realibility
MINUM
PETANG
29 April
(Selasa)
8.0 T-Tests
9.0 One-Way ANOVA
10.0 Two-Way ANOVA
11.0 Correlation
12.0 Multiple
Regression
13.0 Kesimpulan
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“SUKA PENYELIDIKAN SUKA STATISTIK”
Statistical Package for the Social Sciences
Predictive Analytics SoftWare (2009) – ver.18
IBM SPSS (2010) – ver.21
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• Kenapa STATISTIK?
Alat untuk membuat keputusan:
i. Objektif kajian
ii. Hipotesis Kajian
Make informed decisions
Collect Analyze Present Interpret
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PERUBAHAN
AMALAN
ANALISIS STATISTIK
Tu
juan
Perisia
n
Jen
isd
ata
Kem
ah
iran
Reko
d
Maklu
man
Pem
an
tau
an
Kajian
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Melibatkan pengumpulan maklumat (data), menganalisisnya,
dan membuat keputusan yang bermakna.
Sains membuat kesimpulan dari data.
Koleksi prosedur mengumpul data bagi membuat
keputusan
Bidang pengajian yang melibatkan proses
pengumpulan, analisis, persembahan, dan tafsir
untuk membuat keputusan.
Satu cabang matematik yang menganalisis nombor untuk
membuat keputusan.
MAKSUD
STATISTIK
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Secara umumnya statistik ialah satu teknik matematik
untuk memproses, menyusun, menganalisis dan
menyimpulkan data yang berbentuk kuantitatif. Data-
data yang diperolehi dari individu disatukan untuk
membuat sesuatu kesimpulan seberapa umum yang
boleh.
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KegunaanStatistik
Menerangkandata
MeringkaskanData
Memberimakna
kepada data
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Statistik deskriptif digunakan
untuk mengumpul data,
menyusunnya dan
mempersembahkan data itu
supaya data yang banyak dapat
disimpulkan dengan
menggunakan indeks seperti
kekerapan, peratusan, min,
mod, median, varians dan
sisihan piawai.
Statistik inferensi digunakan
untuk membuat sesuatu
anggaran tentang indeks
populasi dengan
menggunakan satu indeks
statistik daripada sampel
yang representatif. Dengan
menggunakan indeks statistik
daripada sampel kita boleh
membuat satu kesimpulan
tentang sifat sesuatu
populasi. Statistik yang
digunakan bergantung kepada
paras ukuran data. Contoh;
Ujian-t, ANOVA, Korelasi,
Regresi dan lain-lain.
STATISTIK DALAM PENYELIDIKAN SAINS SOSIAL
STATISTIK DESKRIPTIF STATISTIK INFERENSI
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Meringkas dan mempersembah data dengan menggunakan
nombor.
Data berasal daripada (sama ada) pemboleh ubah kuantitatif
(ketinggian, kecerdasan, berat) atau dari pemboleh ubah
kategori (jantina, judul buku, jenis sekolah)
Data yang dikumpul diproses dan disusun dalam bentuk yang
mudah dibaca dengan menggunakan pelbagai cara seperti
graf, jadual, dan carta.
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Markah ujian yang dikumpul
Bagaimana hendak memudahkan bacaan dan
memberi makna kepada markah yang dikumpul?
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Membuat inferens mengenai sesuatu populasi
dengan berdasarkan data yang dikumpul dari
satu kumpulan yang lebih kecil (sampel)
Sampel yang dipilih mempunyai ciri-ciri yang
sama dengan populasi (representative)
Menggunakan kaedah statistik yang
mengambil kira faktor ralat dan perbezaan
sampel dengan populasi
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STATISTIK INFERENSI
Populasi Sampel
Dapatan
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Markah ujian yang dikumpul
Apakah interpretasi yang boleh dibuat
berdasarkan kepada markah ujian sampel?
Pelajar Lelaki lebih cemerlang?
Tiada Perbezaan kecemerlangan berdasarkan jantina?
Apakah ujian statistik yang perlu digunakan?
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5 JENIS:
Independent Variable
Dependent Variable
Mediated Variable
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Moderator Variable
Extraneous Variable
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Demografi:
i. Jantina
ii. SES
iii. Lokasi
Kaedah Mengajar:
i. X
ii. Y
PENCAPAIAN PELAJAR
Kualiti Kelengkapan
Bengkel/Makmal
Independent Variables (IV) Dependent Variable (DV)18
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Demografi:
i. Jantina
ii. SES
iii. Lokasi
Kaedah Mengajar:
i. X
ii. Y
MOTIVASI
Kualiti Kelengkapan
Bengkel/Makmal
Independent Variables
(IV)
Dependent Variable
(DV)
PENCAPAIAN PELAJAR
INTERVENING VARIABLE
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Nisbah
(Ratio)
Sela (Interval)
Ordinal
Nominal• Aras paling rendah
• Angka tiada magnitud. Bertujuan untuk
pengkelasan, pengenalan
• Jantina, nombor KP, jenis sekolah, kod buku
• Pengkelasan mengikut pemeringkatan (tinggi rendah)
• Nombor menunjukkan kuantiti; jarak/selang tidak sekata
• Kedudukan dalam kelas, penarafan, kemahiran bertutur,
ranking, SES, Pendapat (Setuju, Tak Pasti, Tidak Setuju)
• Nombor menunjukkan kuantiti/ magnitud
• Jarak sekata antara nombor.
• Suhu, markah ujian, IQ
• Nilai sifar arbitrari (tiada sifar mutlak)
• Nombor menunjukkan kuantiti
• Jarak sekata antara nombor.
• Berat, tinggi, pendapatan
• Nilai sifar menunjukkan tiada
SKALA…No Oil In River
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Jenis UjianStatistik
Sela & Nisbah
Parametrik
BukanParametrik
Nominal &
OrdinalBukan
Parametrik
PEMILIHAN UJIAN STATISTIK
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TUJUAN
JENIS DATA
Parametrik Bukan Parametrik
Menerangkan satu kumpulan Mean, SDMedian, interquartile
range
Perbandingan satu kumpulan
menggunakan satu nilai
One-sample
T- testWilcoxon test
Membandingkan dua kumpulan
berbeza
Independent
T- testMann-Whitney test
Membanding dua kumpulan
berpasanganPaired T-test Wilcoxon test
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TUJUAN
JENIS DATA
Parametrik Bukan Parametrik
Membandingkan tiga atau
lebih kumpulan berbezaOne-way ANOVA Kruskal-Wallis test
Korelasi dua variabel Pearson correlationSpearman
correlation
Meramal nilai dari variabel
lain yang diukur (Predict value
from another measured
variable)
Simple linear regression
or
Nonlinear regression
Nonparametric
regression**
Meramal nilai dari beberapa
variabel lain yang diukur
(Predict value from several
measured or binomial variables)
Multiple linear regression* or
Multiple nonlinear regression**
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IV
-Data diskret
(Nominal / Ordinal)
DV
-Data continuous
(Interval / ratio)
Jenis Ujian
1
(cth. Lelaki/perempuan)
1
(cth. Pencapaian)
Ujian-t
1
(cth. Melayu/Cina/India)
1 Anova Satu
Hala
2
(cth. Bangsa & Jantina)
1 ANOVA Dua
Hala
1 @ > 2 @ > MANOVA
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• Apa yang ingin dikaji?
• Kumpulan mana?
• Berapa pembolehubah?
• Deskriptif? Inferensi?
• Pernyataan persoalan kajian:
“Adakah terdapat perbezaan yang signikan skor
gaya pembelajaran pelajar di kolej A berdasarkan
jantina?”
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• Hipotesis = andaian kajian yg akan diuji
• Jenis Hipotesis = HA & Ho
• Ho
• = Min Sampel Tidak Berbeza Dari Min Populasi
• Pengiraan dan perbandingan t obtain dan t kritikal
• T obtain > t kritikal (alfa=0.05), maka wujud perbezaan yg signifikan kedua-dua min skor sampel dan min populasi
• Why Null?
“It is difficult to prove something to be TRUE, but is much easier to prove something to be NOT TRUE.”
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SOALAN KAJIAN PERLU/TIDAK PENGUJIAN HIPOTESIS
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1• Nyatakan Ho & Ha
2• Setkan darjah keyakinan/alfa (kajian sains
sosial=.05)
3• Laporkan ujian statistik & kesignifikanan
4• Membuat keputusan (terima / gagal)
5• Kesimpulan
Ho BETUL Ho SALAH
TOLAK Ho RALAT JENIS I KEPUTUSAN TEPAT
GAGAL TOLAK Ho KEPUTUSAN TEPAT RALAT JENIS II
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Tolak Ho = Terdapat perbezaan/hubungan yg
signifikan
Gagal tolak Ho = Tidak terdapat perbezaan/hubungan
yg signifikan
Ralat Jenis 1 (alfa) = Tolak Ho yg betul
Ralat Jenis 2 (beta) = Terima Ho yg salah
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Ho: Tidak terdapat perbezaan yg signifikan antara
penggunaan koswer Sains dengan kaedah
konvensional terhadap pemahaman konsep sains
pelajar sekolah rendah.
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Jika memang sebenarnya tiada kesan yang
signifikan antara penggunaan koswer berbanding
kaedah konvensional (Ho memang betul) tetapi
anda TOLAK kerana kajian anda menunjukkan ada
perbezaan maka implikasinya melibatkan
pelaburan jutaan ringgit bagi membangunkan
koswer dan membeli peralatan ICT sedang
aplikasinya tidak berkesan. Kerugian kepada
kewangan negara!.
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Jika memang sebenarnya ada kesan yang
signifikan antara penggunaan koswer berbanding
kaedah konvensional (Ho perlu ditolak) tetapi anda
TERIMA kerana kajian anda menunjukkan tiada
perbezaan maka implikasinya tiada polisi
dijalankan bagi membangunkan koswer dan
membeli peralatan ICT sedang ianya berkesan
kepada pelajar. Kerugian ini lebih serius (secara
relatif) dan berterusan kerana melibatkan satu
pendekatan berkesan yang dapat membantu pelajar
dalam pembelajaran tetapi tidak dilaksanakan.31
• Type 1 Error (T1E): Tolak Ho yang BETUL (patutnya tolak
yang salah ler...)
• Type 2 Error (T2E): Terima Ho yang SALAH (patutnya terima
yang betul...)
• Mana lebih serius - jika ada orang minat kat anak dara kita,
katakan nama dia Ho...dan kita tak kenal orang tu…
• Maka tolak Ho walaupun dia BETUL tak apa (boleh cari lain..)
dari kita terima Ho yang SALAH…naya anak dara kita…
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1. Starting SPSS [From Start Button/Short Cut Desktop]
2. Opening an Existing Data File
File: survey5ED.sav
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3. Starting a New Data File
FileNewData
4. Defining the Variables
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5. Procedure for Defining Your Variables (Name; Type;
Width; Decimals; Label; Value; Missing; Align; Measure)
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1. To enter data, you need to have the Data View active. Click on
the Data View tab at the bottom left-hand side of the screen of
the Data Editor window. A spreadsheet should appear with your
newly defined variable names listed across the top.
2. Click on the first cell of the data set (first column, first row).
3. Type in the number (if this variable is ID, this should be 1).
4. Press the right arrow key on your keyboard; this will move the
cursor into the second cell, ready to enter your second piece of
information for case number 1.
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5. Move across the row, entering all the information for case 1,
making sure that the values are entered in the correct columns.
6. To move back to the start, press the Home key on your
keyboard (on some computers you may need to hold the Ctrl key
or the Fn key down and then press the Home key). Press the down
arrow to move to the second row, and enter the data for case 2.
7. If you make a mistake and wish to change a value, click in the
cell that contains the error. Type in the correct value and then
press the right arrow key.
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i. Memulakan SPSS
ii. Menyediakan Template Data
iii. Memasukkan Data
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1. To delete a case
2. To insert a case between existing cases
3. To delete a variable
4. To insert a variable between existing variables
5. To move an existing variable
6. To split the data file
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Checking for error
Finding the error in the data file
Correcting the error in
the data file
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File: error5ED.sav
1. From the main menu at the top of the screen, click on Analyze,
then click on Descriptive Statistics, then Frequencies.
2. Choose the variables that you wish to check (e.g. sex, marital,
educ.).
3. Click on the arrow button to move these into the Variable box.
4. Click on the Statistics button. Tick Minimum and Maximum in
the Dispersion section.
5. Click on Continue and then on OK
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1. From the menu at the top of the screen, click on Analyze, then
click on Descriptive statistics, then Descriptives.
2. Click on the variables that you wish to check. Click on the arrow
button to move them into the Variables box (e.g. age).
3. Click on the Options button. You can ask for a range of
statistics. The main ones at this stage are mean, standard
deviation, minimum and maximum. Click on the statistics you wish
to generate.
4. Click on Continue, and then on OK
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Method 1
1. Click on the Data menu and choose Sort Cases.
2. In the dialogue box that pops up, click on the variable that you
know has an error (e.g. sex) and then on the arrow to move it into
the Sort By box. Click on either ascending or descending
(depending on whether you want the higher values at the top or
the bottom). For sex, we want to find the person with the value of
3, so we would choose descending.
3. Click on OK.
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Method 2
1. Make sure that the Data Editor window is open and on the screen with
the data showing.
2. Click on the variable name in which the error has occurred (e.g. sex).
3. Click once to highlight the column.
4. Click on Edit from the menu across the top of the screen. Click on Find.
5. In the Find box, type in the incorrect value that you are looking for (e.g. 3).
6. Click on Find Next. SPSS will scan through the file and will stop at the first
occurrence of the value that you specified. Take note of the ID number of
this case (from the first column). You will need this to check your records
or questionnaires to find out what the value should be.
7. Click on Find Next again if you need to continue searching for other
cases with the same incorrect value. In this example, we know from the
Frequencies output that there is only one incorrect value of 3.
8. Click on Close when you have finished searching.
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Rujuk ID soal selidik Semak Semula Borang
Soal Selidik Buat Pembetulan
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• Descriptive statistics
• Using graphs to describe and explore the data
1. Frekuensi
2. Deskriptif
3. Crosstab
4. Graf/Carta/Histogram
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• Menyediakan jadual, graf, carta, dan crosstab.
• Selamat MENCUBA….
• Jangan lupa paste Output pada MS Word…
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Procedure for assessing normality using Explore
1. From the menu at the top of the screen click on Analyze, then select Descriptive Statistics, then Explore.
2. Click on the variable(s) you are interested in (e.g. Total perceived stress: tpstress). Click on the arrow button to move them into the Dependent List box.
3. In the Label Cases by: box, put your ID variable.
4. In the Display section, make sure that Both is selected.
5. Click on the Statistics button and click on Descriptives and Outliers. Click on Continue.
6. Click on the Plots button. Under Descriptive, click on Histogram. Click on Normality plots with tests. Click on Continue.
7. Click on the Options button. In the Missing Values section, click on Exclude cases pairwise. Click on Continue and then OK
Skewness & kurtosis
Test of NormalityKolmogorov-Smirnov…non-sig.=normal
Big samples=Central Limit Theorem
File: survey5ED.sav
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Data file: staffsurvey5ED.sav.
1. Follow the procedures covered in this chapter to generate appropriate descriptive
statistics to answer the following questions.
(a) What percentage of the staff in this organisation are permanent
employees? (Use the variable employstatus.)
(b) What is the average length of service for staff in the organisation?
(Use the variable service.)
(c) What percentage of respondents would recommend the organisation to
others as a good place to work? (Use the variable recommend.)
2. Assess the distribution of scores on the Total Staff Satisfaction Scale (totsatis) for
employees who are permanent versus casual (employstatus).
(a) Are there any outliers on this scale that you would be concerned about?
(b) Are scores normally distributed for each group?
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HISTOGRAM
Procedure for creating a histogram
1. From the menu click on Graphs, then select Legacy Dialogs. ChooseHistogram.
2. Click on your variable of interest and move it into the Variable box.This should be a continuous variable (e.g. Total perceived stress:tpstress).
3. If you would like to generate separate histograms for differentgroups (e.g. male/female), you could put an additional variable (e.g.sex) in the Panel by: section. Choose Rows if you would like the twographs on top of one another, or Column if you want them side by side.In this example, I will put the sex variable in the Column box.
4. Click on OK
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File: survey5ED.sav
BAR GRAPH:
Procedure for creating a bar graph
1. From the menu at the top of the screen, click on Graphs, then select LegacyDialogs. Choose Bar. Click on Clustered.
2. In the Data in chart are section, click on Summaries for groups of cases.Click on Define.
3. In the Bars represent box, click on Other statistic (e.g. mean).
4. Click on the continuous variable you are interested in (e.g. Total perceived
stress: tpstress). This should appear in the box listed as Mean (Total perceivedstress). This indicates that the mean on the Perceived Stress Scale for thedifferent groups will be displayed.
5. Click on your first categorical variable (e.g. agegp3). Click on the arrowbutton to move it into the Category axis box. This variable will appear acrossthe bottom of your bar graph (X axis).
6. Click on another categorical variable (e.g. sex) and move it into the DefineClusters by: box. This variable will be represented in the legend.
7. If you would like to display error bars on your graph, click on the Optionsbutton and click on Display error bars. Choose what you want the bars torepresent (e.g. confidence intervals).
8. Click on Continue and then OK
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LINE GRAPH:
Procedure for creating a line graph
1. From the menu at the top of the screen, select Graphs, then Legacy Dialogs,then Line.
2. Click on Multiple. In the Data in Chart Are section, click on Summaries forgroups of cases. Click on Define.
3. In the Lines represent box, click on Other statistic. Click on the continuousvariable you are interested in (e.g. Total perceived stress: tpstress). Click on thearrow button. The variable should appear in the box listed as Mean (Totalperceived stress). This indicates that the mean on the Perceived Stress Scale forthe different groups will be displayed.
4. Click on your first categorical variable (e.g. agegp3). Click on the arrowbutton to move it into the Category Axis box. This variable will appear acrossthe bottom of your line graph (X axis).
5. Click on another categorical variable (e.g. sex) and move it into the DefineLines by: box. This variable will be represented in the legend.
6. If you would like to add error bars to your graph, you can click on theOptions button. Click on the Display error bars box and choose what youwould like the error bars to represent (e.g. confi dence intervals).
7. Click on OK
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Procedure for creating a boxplot
1. From the menu at the top of the screen, click on Graphs, then
select Legacy Dialogs and then Boxplot.
2. Click on Simple. In the Data in Chart Are section, click on
Summaries for groups of cases. Click on the Define button.
3. Click on your continuous variable (e.g. Total Positive Affect:
tposaff). Click on the arrow button to move it into the Variable
box.
4. Click on your categorical variable (e.g. sex). Click on the arrow
button to move it into the Category axis box.
5. Click on ID and move it into the Label cases box. This will allow
you to identify the ID numbers of any cases with extreme values.
6. Click on OK
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File: staffsurvey5ED.sav.
1. Generate a histogram to explore the distribution of scores on the
Staff Satisfaction Scale (totsatis).
2. Generate a bar graph to assess the staff satisfaction levels for
permanent versus casual staff employed for less than or equal to 2
years, 3 to 5 years and 6 or more years. The variables you will need
are totsatis, employstatus and servicegp3.
3. Generate a boxplot to explore the distribution of scores on the Staff
Satisfaction Scale (totsatis) for the different age groups (age).
4. Generate a line graph to compare staff satisfaction for the different
age groups (use the agerecode variable) for permanent and casual
staff.
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CALCULATING TOTAL SCALE SCORE
COLLAPSING A CONTINUOUS VARIABLE INTO GROUP
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File: survey5ED.sav.
1. From the menu at the top of the screen, click on Transform, then click on
Recode Into Different Variables.
2. Select the items you want to reverse (op2, op4, op6). Move these into the
Input Variable - Output Variable box.
3. Click on the first variable (op2) and type a new name in the Output
Variable section on the right-hand side of the screen and then click the Change
button. I have used Rop2 in the existing data file. If you wish to create your own
(rather than overwrite the ones already in the data file), use another name (e.g.
revop2). Repeat for each of the other variables you wish to reverse (op4 and
op6).
4. Click on the Old and new values button.
In the Old value section, type 1 in the Value box.
In the New value section, type 5 in the Value box (this will change all
scores that were originally scored as 1 to a 5).
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5. Click on Add. This will place the instruction (1 → 5) in the boxlabelled Old > New.
6. Repeat the same procedure for the remaining scores. For example:
Old value—type in 2 New value—type in 4 Add
Old value—type in 3 New value—type in 3 Add
Old value—type in 4 New value—type in 2 Add
Old value—type in 5 New value—type in 1 Add
Always double-check the item numbers that you specify for recodingand the old and new values that you enter. Not all scales use a five-point scale; some have four possible responses, some six and someseven. Check that you have reversed all the possible values for yourparticular scale.
7. Click on Continue and then OK
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Procedure for calculating total scale scores
1. From the menu at the top of the screen, click on Transform, then click
on Compute Variable.
2. In the Target Variable box, type in the new name you wish to give to
the total scale scores. (It is useful to use a T prefix to indicate total
scores, as this makes them easier to find in the list of variables when you
are doing your analyses.)
Important: make sure you do not accidentally use a variable name that
has already been used in the data set. If you do, you will lose all the
original data—potential disaster—so check your codebook.
3. Click on the Type and Label button. Click in the Label box and type
in a description of the scale (e.g. total optimism). Click on Continue.
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4. From the list of variables on the left-hand side, click on the first itemin the scale (op1).
5. Click on the arrow button to move it into the Numeric Expressionbox.
6. Click on + on the calculator.
7. Repeat the process until all scale items appear in the box. In thisexample we would select the unreversed items first (op3, op5) and thenthe reversed items (obtained in the previous procedure), which arelocated at the bottom of the list of variables (Rop2, Rop4, Rop6).
8. The complete numeric expression should read as follows:
op1+op3+op5+Rop2+Rop4+Rop6.
9. Double-check that all items are correct and that there are + signs inthe right places. Click OK
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Procedure for collapsing a continuous variable into groups
1. From the menu at the top of the screen, click on Transform and chooseVisual Binning.
2. Select the continuous variable that you want to use (e.g. age). Transfer it intothe Variables to Bin box. Click on the Continue button.
3. In the Visual Binning screen, a histogram showing the distribution of agescores should appear.
4. In the section at the top labelled Binned Variable, type the name for thenew categorical variable that you will create (e.g. Agegp3). You can alsochange the suggested label that is shown (e.g. age in 3 groups).
5. Click on the button labelled Make Cutpoints. In the dialogue box thatappears, click on the option Equal Percentiles Based on Scanned Cases. In thebox Number of Cutpoints, specify a number one less than the number ofgroups that you want (e.g. if you want three groups, type in 2 for cutpoints). Inthe Width (%) section below, you will then see 33.33 appear. This means thatSPSS will try to put 33.3 per cent of the sample in each group. Click on theApply button.
6. Click on the Make Labels button back in the main dialogue box. This willautomatically generate value labels for each of the new groups created.
7. Click on OK
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Procedure for recoding a categorical variable
1. From the menu at the top of the screen, click on Transform, then on Recodeinto Different Variables. (Make sure you select ‘different variables’, as thisretains the original variable for other analyses.)
2. Select the variable you wish to recode (e.g. educ). In the Name box, type aname for the new variable that will be created (e.g. educrec). Type in anextended label if you wish in the Label section. Click on the button labelledChange.
3. Click on the button labelled Old and New Values.
4. In the section Old Value, you will see a box labelled Value. Type in the firstcode or value of your current variable (e.g. 1). In the New Value section, typein the new value that will be used (or, if the same one is to be used, type thatin). In this case I will recode to the same value, so I will type 1 in both the OldValue and New Value sections. Click on the Add button.
5. For the second value, I would type 2 in the Old Value but in the New Value Iwould type 1. This will recode all the values of both 1 and 2 from the originalcoding into one group in the new variable to be created with a value of 1.
6. For the third value of the original variable, I would type 3 in the Old Valueand 2 in the New Value. This is just to keep the values in the new variable insequence. Click on Add. Repeat for all the remaining values of the originalvalues. In the table Old > New, you should see the following codes for thisexample: 1→1; 2→1; 3→2; 4→3; 5→4; 6→5.
7. Click on Continue and then on OK62
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63
• Merujuk kpd sejauhmana sst.alat ukur dpt. Memberikan ukuran
terhadap apa yg patut diukur (Tuckman, 1978; Mohd Majid,
1990; Anastasi&Urbina 1997).
• Darjah ketepatan ujian/alat ukur tersebut mengukur perkara
atau kualiti yg diukur oleh ujian tersebut (Anastasi, 1990 dlm
Mohamad Sahari, 2008).
• Cth.: Alat penimbang sah untuk mengukur berat badan, TETAPI
kurang sah untuk mengukur darjah kesihatan seseorang.
• Sesuatu alat yg berupaya mengukur dengan tepat sst
pembolehubah yg ditetapkan adalah dianggap SAH sbg alat
pengukur bg pembolehubah tersebut.
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• Kesahan Muka (Face validity) – bahasa, ejaan,
kejelasan, kurang saintifik & boleh disemak oleh org
bukan pakar bidang
• Kesahan Kandungan (Content validity) – sejauh mana
alat ukur itu mewakili bidang/kandungan yg diukur.
MESTI disahkan oleh pakar bidang.
• Kesahan Kriteria (Criterion validity)
• Terbahagi kpd 2:
i. Kesahan serentak (concurrent);
ii. Kesahan Jangkaan (Predictive)65
• K.serentak- kesetaraan…skor instrumen yg dibina setara/hampir
setara dgn instrumen org lain. Cth.,Soalan Matematik pada tahun
2007 (Lembaga Pep.) dgn soalan Matematik yg dibina pada 2012
(menguji topik yg sama) – menunjukkan keputusan yg tidak jauh
berbeza apabila diuji utk tempoh masa yg dekat.
• K.Jangkaan – dpt.menjangka keputusan akan datang (3-6 bulan).
Lazim utk ujian penyaringan. Cth. IMSAK di IPG).
• “the ability of a survey instrument to forecast future events,
behaviours, attitudes, or outcome.” (Litwin 1995)
• Kesahan Gagasan (Construct validity)
• Item yg menguji konstruk yang sama, skor ujian adalah ‘correlated’;
tetapi jika mengukur konstruk yang berlainan akan mencatatkan
korelasi yg rendah
• Ringkasnya, item yg mewakili sesuatu konstruk perlu mempunyai ciri
sepunya!
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kestabilan & ketekalan/konsistensi sst alat/instrumen
mengukur sst konsep, pd bila2 masa, dlm apa jua
keadaan.
Memberi skor yg sama walau diukur berulang kali
“…reliability doesn't ensure validity…” (Hair et al.,
1995)
67
• Inter-Rater or Inter-Observer Reliability
Used to assess the degree to which different raters/observers
give consistent estimates of the same phenomenon.
• Test-Retest Reliability
Used to assess the consistency of a measure from one time to
another.
• Parallel-Forms Reliability
Used to assess the consistency of the results of two tests
constructed in the same way from the same content domain.
• Internal Consistency Reliability
Used to assess the consistency of results across items within a
test.
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• Cronbach (1946):
•<0.6 : rendah
•0.6 – 0.8 : diterima
•>0.8 : baik
•DeVellis (2003), >0.7
69
1. From the menu at the top of the screen, click on Analyze, select Scale,
then Reliability Analysis.
2. Click on all of the individual items that make up the scale (e.g. item1,
item2, item3, item4, item5). Move these into the box marked Items.
3. In the Model section, make sure Alpha is selected.
4. In the Scale label box, type in the name of the scale or subscale (Life
Satisfaction).
5. Click on the Statistics button. In the Descriptives for section, select Item,
Scale, and Scale if item deleted. In the Inter-Item section, click on
Correlations. In the Summaries section, click on Correlations.
6. Click on Continue and then OK
File: survey5ED.sav
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File: staffsurvey5ED.sav
Check the reliability of the Staff Satisfaction Survey, which is
made up of the agreement items in the data fi le: Q1a to Q10a.
None of the items of this scale needs to be reversed.
71
• Ujian-T
• ANOVA SEHALA
• ANOVA 2 HALA
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Untuk menguji perbezaan Min antara DUA kategori/kumpulanpembolehubah tak bersandar/bebas.
Model yang digunakan bergantung sama ada sampel yang terlibat itubersandar atau tidak.
Jika sampel tidak bersandar, kita perlu terlebih dahulu menentukansama ada varians kedua-dua sampel itu homogenus atau tidak(heterogenus). Ujian yang boleh digunakan ialah Ujian-F (Levene'sTest).
Sekiranya Ujian-F menunjukkan bahawa varians sampel adalahhomogenus, kita perlu menggunakan formula Ujian-t untuk variansyang disatukan (Pooled Varians estimate/Equal varians assumed)
Sekiranya Ujian-F menunjukkan bahawa varians sampel adalahheterogenus, kita perlu menggunakan formula Ujian-t untuk variansyang berasingan (Separate Varians estimate/Equal varians notassumed)
Jika sampel-sampel yang terlibat itu bersandar maka kita perlumenggunakan formula Ujian-t untuk sampel yang bersandar.
UJIAN-t
73
UJIAN-t
SAMPEL TAK BERSANDAR (Independent Sample t-Test)
SAMPEL BERSANDAR (Paired Sample t-Test)
UJIAN-F (Leven’s Test) Bagi menguji
Kemohogenan Varians
VARIANS HOMOGENUS
VARIANS HETEROGENUS
MENGGUNAKAN UJIAN-t VARIANS YANG DISATUKAN (Pooled Varians Estimate/Equal
Varians Assumed)
MENGGUNAKAN UJNIAN-t VARIANS YANG BERASINGAN (Separate Varians Estimate/Equal
Varians Not Assumed
MENGGUNAKAN UJIAN-t SAMPEL
YANG BERSANDAR
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Example of research question: Is there a significant difference in
the mean self-esteem scores for males and females?
What you need: Two variables:
• one categorical, independent variable (e.g. males/females)
• one continuous, dependent variable (e.g. self-esteem scores).
What it does: An independent-samples t-test will tell you whether
there is a statistically significant difference in the mean scores for
the two groups (i.e. whether males and females differ significantly
in terms of their self-esteem levels). In statistical terms, you are
testing the probability that the two sets of scores (for males and
females) came from the same population.
75
Procedure for independent-samples t-test
1. From the menu at the top of the screen, click on Analyze, then
select Compare means, then Independent Samples T test.
2. Move the dependent (continuous) variable (e.g. total self-
esteem: tslfest) into the Test variable box.
3. Move the independent variable (categorical) variable (e.g. sex)
into the section labelled Grouping variable.
4. Click on Define groups and type in the numbers used in the
data set to code each group. In the current data fi le, 1=males,
2=females; therefore, in the Group 1 box, type 1, and in the
Group 2 box, type 2. If you cannot remember the codes used,
right click on the variable name and then choose Variable
Information from the pop-up box that appears. This will list the
codes and labels.
5. Click on Continue and then OK
File: survey5ED.sav
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Procedure for paired-samples t-test
1. From the menu at the top of the screen, click on
Analyze, then select Compare Means, then Paired
Samples T test.
2. Click on the two variables that you are interested in
comparing for each subject (e.g. fost1: fear of stats
time1, fost2: fear of stats time2) and move them into the
box labelled Paired Variables by clicking on the arrow
button. Click on OK
77
Data file: staffsurvey5ED.sav.
Follow the procedures in the section on independent-
samples t-tests to compare the mean staff satisfaction
scores (totsatis) for permanent and casual staff
(employstatus).
Is there a significant difference in mean satisfaction
scores?
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Tujuan: Mengenal pasti sama ada terdapat perbezaan skor min
yang signifikan pada 1 DV (berskala interval) yang melibatkan 3
atau lebih kumpulan.
• ANOVA Satu Hala akan menunjukkan sama ada terdapat
perbezaan yang signifikan dalam skor min pembolehubah
bersandar (dependent variable) merentasi 3 kumpulan
responden yang dikaji. Ujian Post-hoc pula digunakan untuk
mengenalpasti kumpulan manakah yang berbeza.
Andaian: sama seperti t-test
79
UJIAN ANALISIS VARIANS SATU-HALA (ANOVA SATU-HALA)
Untuk menguji perbezaan Min antara DUA atau lebihkategori/kumpulan pembolehubah tak bersandar/ bebas.
Terdapat dua punca variasi dalam ujian ini iaitu variasi dalamkumpulan yang bebas dari kesan rawatan yang dianggap sebagaivarians ralat dan variasi antara kumpulan yang berlaku keranakesan rawatan yang dianggap sebagai varians daripada kesanrawatan.
Ujian-F digunakan untuk menentukan sama ada min kumpulan-kumpulan itu berbeza secara signifikan atau tidak.
Sekiranya Ujian-F menunjukkan perbezaan yang signifikan antarakumpulan-kumpulan tersebut maka ujian perbandingan berganda(multiple comparison) perlu dilakukan bagi menentukanperbezaan min antara pasangan-pasangan min. Ujianperbandingan berganda yang selalu digunakan ialah Ujian Tukeyatau Ujian Scheffe.
Ujian ini perlu dijalankan kerana Ujian ANOVA tidak menjelaskansecara khusus perbezaan min yang sebenar bagi setiap pasangan,ia hanya menyatakan secara keseluruhan min-min kumpulanadalah tidak sama atau berbeza secara signifikan. 80
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• Contoh Soalan Kajian: Adakah terdapat perbezaan yang
signifikan tahap kualiti peribadi pengetua kanan berdasarkan
persepsi guru berdasarkan pengalaman mengajar guru?
• Hipotesis:
Ho : µ1 = µ2 = µ3
Ho : Tidak terdapat perbezaan yang signifikan skor min tahap
kualiti peribadi pengetua kanan berdasarkan persepsi guru
mengikut pengalaman mengajar guru.
81
• Dalam Post Hoc, nak guna Tukey atau Scheffe? Lazimnya, Scheffe digunakan
bagi N tiga atau lebih kumpulan berbeza, manakala Tukey memerlukan N
bagi setiap kumpulan adalah sama!
• Keputusan Post Hoc wajib dilihat dan dilaporkan hanya sekiranya ujian
Anova didapati terdapat perbezaan yang signifikan (lihat Sig.), Jika nilai
Sig. melebihi had yang ditetapkan oleh pengkaji (cth. p<0.05), maka tidak
perlu melihat keputusan post hoc.
• Untuk melaporkan keputusan ujian Anova, pengkaji perlu mengemukakan
Jadual Deskriptif, Jadual Ujian Anova, dan Jadual Post Hoc (jika Nilai Sig. <
0.05).
• Nilai Eta Squared dalam Anova memerlukan pengkaji melakukan pengiraan
sendiri dengan menggunakan formula di bawah dan seterusnya tentukan
sama ada tinggi/sederhana/rendah berdasarkan saranan Cohen atau yang
lain.
• Eta squared = Sum of squares between groups
Total sum of squares
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Example of research question: Is there a difference in optimismscores for young, middle-aged and old participants?
What you need: Two variables:
• one categorical independent variable with three or more distinctcategories. This can also be a continuous variable that has beenrecoded to give three equal groups (e.g. age group: participantsdivided into three age categories, 29 and younger, between 30and 44, 45 or above)
• one continuous dependent variable (e.g. optimism scores).
What it does: One-way ANOVA will tell you whether there aresignificant differences in the mean scores on the dependentvariable across the three groups. Post-hoc tests can then be usedto find out where these differences lie.
83
Procedure for one-way between-groups ANOVA with post-hoc tests
1. From the menu at the top of the screen, click on Analyze, then select
Compare Means, then One-way ANOVA.
2. Click on your dependent (continuous) variable (e.g. Total optimism:
toptim). Move this into the box marked Dependent List by clicking on
the arrow button.
3. Click on your independent, categorical variable (e.g. age 3 groups:
agegp3). Move this into the box labelled Factor.
4. Click the Options button and click on Descriptive, Homogeneity of
variance test, Brown-Forsythe, Welch and Means Plot.
5. For Missing values, make sure there is a dot in the option marked
Exclude cases analysis by analysis. Click on Continue.
6. Click on the button marked Post Hoc. Click on Tukey.
7. Click on Continue and then OK84
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Data file: staffsurvey5ED.sav.
Conduct a one-way ANOVA with post-hoc tests (if
appropriate) to compare staff satisfaction scores
(totsatis) across each of the length of service categories
(use the servicegp3 variable).
85
• Tujuan: Mengenal pasti sama ada terdapat perbezaan skor min yang
signifikan pada 1 DV (berskala interval) yang melibatkan 2 IV (jantina &
bangsa).
• Andaian: kenormalan, sampel rawak, taburan normal
• Soalan Kajian: Adakah terdapat perbezaan yang signifikan tahap
gangguan emosi berdasarkan jantina dan bangsa?
• Hipotesis:
Ho1: Tidak terdapat perbezaan yang signifikan tahap gangguan emosi
berdasarkan jantina.
Ho2: Tidak terdapat perbezaan yang signifikan tahap gangguan emosi
berdasarkan bangsa.
Ho3: Tidak terdapat kesan interaksi yang signifikan antara jantina dan
bangsa terhadap tahap gangguan emosi.
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Example of research question: What is the impact of age and genderon optimism? Does gender moderate the relationship between age andoptimism?
What you need: Three variables:
• two categorical independent variables (e.g. sex: males/females; agegroup: young, middle, old)
• one continuous dependent variable (e.g. total optimism).
What it does: Two-way ANOVA allows you to simultaneously test for theeffect of each of your independent variables on the dependentvariable and also identifies any interaction effect. For example, itallows you to test for (a) sex differences in optimism, (b) differences inoptimism for young, middle and old participants, and (c) the interactionof these two variables-is there a difference in the effect of age onoptimism for males and females?
87
CONTOH SOALAN KAJIAN
1. Adakah terdapat perbezaan tahap budaya penyelidikan
berdasarkan lokasi sekolah dan jenis sekolah?
Hipotesis Kajian:
Ho.1. Tidak terdapat perbezaan yang signifikan dari segi tahap
budaya penyelidikan antara guru sekolah bandar dengan
guru sekolah luar bandar.
Ho.2. Tidak terdapat perbezaan yang signifikan tahap budaya
penyelidikan antara guru sekolah menengah dengan guru
sekolah rendah.
Ho.3. Tidak terdapat kesan interaksi yang signifikan antara lokasi
sekolah dan jenis sekolah terhadap tahap budaya
penyelidikan di kalangan guru.
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JADUAL 1
Perbandingan Skor Min Tahap Budaya Penyelidikan Di Kalangan Guru
Berdasarkan Lokasi Sekolah dan Jenis Sekolah
Lokasi Jenis Sekolah Min
Sisihan
Piawai Bil (n)
Bandar Sek. Menengah
Sek. Rendah
Jumlah
3.479
3.511
3.495
0.453
0.448
0.450
120
130
250
Luar Bandar Sek. Menengah
Sek. Rendah
Jumlah
3.278
3.323
3.300
0.558
0.574
0.565
233
214
337
Jumlah Sek Menengah
Sek. Rendah
Jumlah
3.347
3.394
3.370
0.532
0.537
0.535
353
344
69789
JADUAL 2
Ujian ANOVA Dua-Hala Perbandingan Tahap Budaya Penyelidikan Di
Kalangan Guru Berdasarkan Lokasi Sekolah dan Jenis Sekolah
Kesan Utama Jumlah
Kuasadua
Darjah
Kebebasan
Min
Kuasadua
Nilai-
F
Tahap Sig.
(p)
Lokasi
Jenis Sekolah
Interaksi
Lokasi*J. Sek.
Ralat
Jumlah
6.032
0.233
0.006
192.509
198.935
1
1
1
693
696
6.032
0.233
0.006
0.278
5.361
6.574
0.000
0.001*
0.360
0.877
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Berdasarkan jadual di atas didapati terdapat perbezaan yang signifikan tahapbudaya penyelidikan antara guru sekolah bandar dengan guru sekolah luarbandar (F (1,693) =21.716; p=0.001). Guru sekolah bandar mempunyai budayapenyelidikan yang lebih (min=3.495) berbanding dengan guru sekolah luarbandar (min=3.300)
Dari segi jenis sekolah, didapati tidak terdapat perbezaan yang signifikantahap budaya penyelidikan antara guru sekolah menengah dengan gurusekolah rendah (F=0.840; dk=1.693; p=0.360). Ini bermakna budayapenyelidikan di kalangan guru sekolah menengah dan guru sekolah rendahadalah pada tahap yang sama.
Dari segi kesan interaksi pula, didapati tidak terdapat kesan interaksi yangsignifikan antara lokasi sekolah dengan jenis sekolah terhadap budayapenyelidikan di kalangan guru (F=0.024; dk=1.693; p=0.877). Rajah 1 dibawah menunjukkan graf kesan interaksi antara lokasi sekolah dan jenissekolah terhadap budaya penyelidikan di kalangan guru.
Contoh penjelasan
91
Budaya Penyelidikan Di Kalangan Guru
lokasi
luar bandarbandar
Sko
r M
in
3.6
3.5
3.4
3.3
3.2
jenis sekolah
sek. menengah
sek. rendah
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Berdasarkan Rajah 1, dapat dirumuskan bahawa budaya penyelidikan dikalangan guru adalah berbeza secara signifikan antara guru sekolahbandar dengan guru sekolah luar bandar. Tahap budaya penyelidikan dikalangan guru sekolah bandar adalah lebih tinggi berbanding dengan gurusekolah luar bandar sama ada bagi sekolah menengah mahupun sekolahrendah.
93
Data file: staffsurvey5ED.sav.
Conduct a two-way ANOVA with post-hoc tests (if
appropriate) to compare staff satisfaction scores
(totsatis) across each of the length of service categories
(use the servicegp3 variable) for permanent versus
casual staff (employstatus).
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Example of research question: Is there a relationship between
the amount of control people have over their internal states and
their levels of perceived stress? Do people with high levels of
perceived control experience lower levels of perceived stress?
What you need: Two variables: both continuous variables (two
values).
What it does: Correlation describes the relationship between two
continuous variables, in terms of both the strength of the
relationship and the direction.
95
Procedure for requesting Pearson r or Spearman rho
1. From the menu at the top of the screen, click on Analyze, then select
Correlate, then Bivariate.
2. Select your two variables and move them into the box markedVariables (e.g. Total perceived stress: tpstress, Total PCOISS: tpcoiss). Ifyou wish you can list a whole range of variables here, not just two. In theresulting matrix, the correlation between all possible pairs of variableswill be listed. This can be quite large if you list more than just a fewvariables.
3. In the Correlation Coefficients section, the Pearson box is the defaultoption. If you wish to request the Spearman rho (the non-parametricalternative), tick this box instead (or as well).
4. Click on the Options button. For Missing Values, click on the Exclude
cases pairwise box. Under Options, you can also obtain means andstandard deviations if you wish.
5. Click on Continue and then on OK
File: survey5ED.sav
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small r=.10 to .29
Medium r=.30 to .49
large r=.50 to 1.0
Cohen (1988, pp. 79–81)
97
Data file: sleep5ED.sav.
Check the strength of the correlation between
scores on the Sleepiness and Associated
Sensations Scale (totSAS) and the Epworth
Sleepiness Scale (ess).
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Example of research questions:
1. How well do the two measures of control (mastery, PCOISS)
predict perceived stress? How much variance in perceived stress
scores can be explained by scores on these two scales?
2. Which is the best predictor of perceived stress: control of
external events (Mastery Scale) or control of internal states
(PCOISS)?
3. If we control for the possible effect of age and socially
desirable responding, is this set of variables still able to predict a
significant amount of the variance in perceived stress?
99
What you need:
• one continuous dependent variable (Total
perceived stress)
• two or more continuous independent variables
(mastery, PCOISS). (You can also use dichotomous
independent variables, e.g. males=1,
females=2.)
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• What it does: Multiple regression tells you how much
of the variance in your dependent variable can be
explained by your independent variables. It also gives
you an indication of the relative contribution of each
independent variable. Tests allow you to determine the
statistical significance of the results, in terms of both
the model itself and the individual independent
variables.
101
1. From the menu at the top of the screen, click on Analyze, then select
Regression, then Linear.
2. Click on your continuous dependent variable (e.g. Total perceived stress:
tpstress) and move it into the Dependent box.
3. Click on your independent variables (Total Mastery: tmast; Total PCOISS:
tpcoiss) and click on the arrow to move them into the Independent box.
4. For Method, make sure Enter is selected. (This will give you standard multiple
regression.)
5. Click on the Statistics button.
• Select the following: Estimates, Confidence Intervals, Model fit,
Descriptives, Part and partial correlations and Collinearity diagnostics.
• In the Residuals section, select Casewise diagnostics and Outliers outside 3
standard deviations. Click on Continue.
102
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6. Click on the Options button. In the Missing Values section, select
Exclude cases pairwise. Click on Continue.
7. Click on the Plots button.
• Click on *ZRESID and the arrow button to move this into the Y box.
• Click on *ZPRED and the arrow button to move this into the X box.
• In the section headed Standardized Residual Plots, tick the Normal
probability plot option. Click on Continue.
8. Click on the Save button.
• In the section labelled Distances, select Mahalanobis box and Cook’s.
• Click on Continue and then OK
103