The graphical user interface has two views which can be toggled by clicking on one of the two tabs in the bottom left of the SPSS Statistics window. The 'Data View' shows a spreadsheet view of the cases (rows) and variables (columns). Unlike spreadsheets, the data cells can only contain numbers or text, and formulas cannot be stored in these cells. The 'Variable View' displays the metadata dictionary where each row represents a variable and shows the variable name, variable label, value label(s), print width, measurement type, and a variety of other characteristics. Cells in both views can be manually edited, defining the file structure and allowing data entry without using command syntax. This may be sufficient for small datasets. Larger datasets such as statistical surveys are more often created in data entry software, or entered during computer-assisted personal interviewing, by scanning and using optical character recognition and optical mark recognition software, or by direct capture from online questionnaires. These datasets are then read into SPSS.
Instalasi "Finish"Sebelum membuka software SPSS, sebaiknya dilakukan aktivasi terlebih dahulu. Berikut langkah aktivasi software SPSS.
B. Cara Aktivasi Offline Software SPSSUntuk melakukan aktivasi offline diperlukan serial number serta file aktivasi "lservrc".
Serial number spss 22
Klik "Next" pada License Status
Pilih "Authorized user license" pada jendela Product Authorization
Masukkan kode serial number lalu klik "Next"
Klik "Next" pada License Status yang telah diperbarui
Klik "Finish"
Selamat, software SPSS sudah aktif dan dapat digunakan.
Untuk serial number jika memang kampusnya merupakan langganan dari IBM Statistics, sebaiknya langsung dihubungi pihak yang memberikan file (dosen atau pegawai kampus). Karena biaya berlangganan tentunya diambil dari biaya semester mahasiswanya.
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First, we set out the example we use to show how to create dummy variables in SPSS Statistics, before explaining how to set up your data in the Variable View and Data View windows of SPSS Statistics so that you can create dummy variables. If you are unfamiliar with the use of dummy variables, we recommend that you then read about some of the basic principles of dummy variables and dummy coding, including: (a) the number of dummy variables you need to create in your analysis; and (b) how to create dummy variables and dummy coding. In the Procedure section that follows, we set out the simple, 3-step Create Dummy Variables procedure in SPSS Statistics that can be used to create dummy variables. Finally, we explain the SPSS Statistics output after running the Create Dummy Variables procedure, including how your dummy variables will now be set up in the Variable View and Data View windows of SPSS Statistics.
In this guide we will be using the example of 10 triathletes who were asked to select their favourite sport from the three sports they perform when doing a triathlon: swimming, cycling and running. Their answers were recorded in the nominal independent variable, favourite_sport, which has three categories: "swimming", "cycling" and "running". This nominal independent variable, favourite_sport, was to be included in a multiple regression analysis that also had a number of continuous independent variables. Since this independent variable was categorical (i.e., nominal variables and ordinal variables can be broadly classified as categorical variables), dummy variables had to be created before it could be entered into the multiple regression analysis.
As we mentioned in the Introduction, if you are analysing your data using multiple regression and any of your independent variables were measured on a nominal or ordinal scale, you need to know how to create dummy variables and interpret their results. This is because categorical independent variables (i.e., nominal and ordinal independent variables) cannot be directly entered into a multiple regression. Instead, they need to be converted into dummy variables. The exception is ordinal independent variables that are entered into a multiple regression as continuous independent variables, which do not need to be converted into dummy variables. In the sections below, we explain: (a) the number of dummy variables you need to create; and (b) how to create dummy variables and dummy coding.
The number of dummy variables you need to create will depend on how many categories your categorical independent variable has. As a general rule, you will create one less dummy variable than the number of categories in your categorical independent variable. For example, if you have a categorical independent variable with three categories (e.g., favourite_sport, with the following three categories: "swimming", "cycling" and "running"), you will create two dummy variables and select one category to act as a reference category (e.g., "swimming" and "cycling" become dummy variables and "running" becomes the reference category). We explain more about reference categories after the following table, which provides some examples of categorical independent variables and the number of dummy variables that need to be created:
As shown in the table above, you only need to create one less dummy variable than the number of categories in your categorical independent variable. This is because you only need to (and should) transfer this number of dummy variables into a multiple regression when you have a categorical independent variable. However, there are good reasons to create a dummy variable for every category of the categorical independent variable: (a) it is more flexible and (b) it allows multiple comparisons to be made (see the note below). In other words, if your categorical independent variable has three categories you would create three dummy variables, not just two.
The Variable Creation table confirms that you have successfully created dummy variables. There should be as many rows as there are new dummy variables. Since we created three dummy variables, there are three rows in the table, "fs_1", "fs_2" and "fs_3", which reflect the root name and sequential numbering entered in Step 2 of the Create Dummy Variables procedure in the previous section. For each of these dummy variables, a label is provided in the table to make it clear which category of the categorical independent variable each dummy variable represents. For example, the label, "favourite_sport=swimming", is provided for "fs_1", indicating that "fs_1" is the dummy variable for the "swimming" category of the categorical independent variable, favourite_sport.
The analysis was performed with the SPSS version 15 (SPSS, Inc.,Chicago, IL, USA; Network license, serial number 5047404). Data wereexpressed using descriptive statistics such as mean, standard deviation forcontinuous variables, frequency, and percentage for categorical variables.Statistical analysis was done using Chi-square test for categoricalvariables, and Mann–Whitney U-test for continuous variables with 5%significance level. Unpaired two-tailed t -test used for mean age. Chi-squaretest used for age category, duration, and mode of injury. Chi-square testwith Mid-P exact used for gender and symptoms. Logistic regression analysiswas applied to arrive at the equation to find the probability of having anabnormal head CT scan based on the clinical predictors.
Head injuries are commonly managed in the trauma and emergencydepartments, among them 70–80% are mild in nature.[sup][15] Manyhead injured patients are conscious on arrival to casualty and do not have aneurological deficit. However, such a patient constitutes a potential problemfor neurosurgeons because a patient with an apparent minor head injury canrarely develop an intracranial hematoma, deteriorate, and die. There iscontroversy regarding the policy for hospital admission and evaluation withCT scan for these patients. A CT scan is desirable for patients with minorhead injury as it is useful for detection of a clinically significantintracranial lesion, prognostication, and decision for discharge. To obviateunnecessary CT scans, many guidelines are available for indication of CT scanfor minor head injury in adults.[sup][16] Among these, Canadian CT Head Ruleis the most widely validated rule, with a sensitivity of 99–100%and a specificity of 48–77%.[sup][16],[17],[18] Other rules differconsiderably in population, predictors, outcomes, methodologic quality, andperformance. Many of them are not validated in a separate population, andtheir impact on practice has not been assessed. In our patient population,only four symptoms were present: vomiting, LOC, ear/nose bleeding, andseizures. We did not include scalp injury as a variable because it has littlediagnostic value.[sup][18] We do not perform coagulation profile for patientswith minor head injury at our institute; hence, we did not include thisvariable. Posttraumatic amnesia (PTA) is also an important predictor ofabnormal CT scan.[sup][16] The timing of resolution of PTA is difficult toassess in the emergency department; hence, we did not include this variableas well. We included all CT scan findings attributed to trauma includingskull fracture as inclusion criteria for an abnormal (positive) CT scan. Wefound that presence of any of the symptoms attributed to head injury such asLOC, vomiting, ear/nose bleeding, or seizures predicted an abnormal CT scan.Particularly vomiting after head injury was significantly associated withabnormal CT scan. Although the sensitivity of our prediction model was low,the specificity was higher than most of the available prediction rules. Wehad some limitations with our study as can be seen with the number ofclinical predictors used in the study. Further, this study does not takechildren into a different group, very often children have different clinicalpredictor rules for CT scan. We did not define separate significantintracranial injury, which probably has more bearing on management andprognosis. However, the presence of any abnormality on CT scan requiresneurological observation. Our study needs to be conducted in a largerpopulation and needs to be validated in external settings. However, in spiteof these limitations, our study answers one important question forneurosurgeons. What clinical predictors can be used to address the questionthat “should a patient be shifted to higher center for head CTscan, if the treating center does not have CT scan facility?” Yes,if the patient is older than 40 years of age and has any of followingsymptoms: vomiting, LOC, ear/nose bleeding, and seizures after head injuryhe/she should undergo a head CT though not all patients have an abnormalscan. 2ff7e9595c
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