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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?
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).
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.
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.
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
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.
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
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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
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
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.
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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.
• 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?
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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
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.
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.
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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
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.
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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.