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المنتدى : دروس فى برنامج SPSS للتحليل الإحصائى
افتراضي spss

Spss
What do you think of SPSS?
Great statistical analysis application
SPSS is better than Excel when you need a data manger. it's more easy to learn than excel in my opinion.

SPSS Statistics has three main windows, plus a menu bar at the top. These allow you to (1) see your data, (2) see your statistical output, and (3) see any programming commands you have written. Each window corresponds to a separate type of spss file.
Data Editor (.sav files)
Output Viewer (.spv files)
Syntax Editor (.sps files)

Data Editor = (data view , data variable ) The Data Editor lets you see and manipulate your data.
يتيح محرر البيانات إمكانية رؤية وتعديل والتعامل مع البيانات

It is also possible to open some non-SPSS data files by this method, such as Excel, Stata, or SAS files.
Statistical results will show up in the Output Viewer.
The Output Viewer shows you tables of statistical output and any graphs you create
If you are working with the spss programming language directly, you will also open a Syntax Editor. The Syntax Editor allows you to write, edit, and run commands in the spss programming language
Dialog Boxes المربعات الحوارية
Working with the Data Editor
The main use of the Data Editor is to show you (a portion of) the data values you are working with. It can also be used to redefine the characteristics of variables (change the type, add labels, define missing values, etc.), create new variables, and enter data by hand.
The Data Editor gives you two views of your data set: a Data View and a Variable View, selected by clicking on the appropriate tab in the lower left corner of the window.
Data View
Each row represents a unit of observation, sometimes also referred to as a “record” or in spss as a “case.” The case (observation) number in the leftmost column is assigned automatically and is not saved as data. Each column represents a variable. All of the data in a column must be of the same “type,” either numeric or string (also called “character”).
You can switch the Data View between formatted and unformatted data by clicking on theValue Labels button on the Toolbar
Variable View
In the Variable View you can see and edit the information that defines each variable (sometimes called “meta-data”) in your data set: each column of the Data View is described by a row of the Variable View.
كل عامود فى data view يوصف بصف فى variable view

The first attribute of each variable is its Name. The variable name is how the data column is identified in the programming language, and in order for the programming language to work gracefully variable names have to abide by certain restrictions: names must begin with a letter, and may be made up of characters, numerals, non-punctuation characters, and the period. Capitalization is ignored. Variable names may be up to 64 characters long. Other restrictions may apply – no coupons please. Variable names may be added or changed simply by typing them in
The basic variable types are either numeric or string. However, just to make things confusing, spss allows you to select among several different standard formats for displaying numeric data (e.g. scientific notation, comma formatting, currencies) and calls itType.

The Label attribute allows you to give each variable a longer description that is displayed in place of the variable name

The Valuesattribute allows you to create a list of value labels.

The Missing attribute is a place for you to designate certain data values that you want spss to ignore when it calculates statistics.
Working with the Output Viewer

The Output Viewer collects your statistical tables and graphs
The Output Viewer is divided into two main sections, an outline pane on the left, and a contents pane on the right
When spss creates output (tables, syntax, error messages, etc.)
To edit objects, double-click on them in the contents pane

Now that you understand the basics of using the spss windows, you can learn how to carry out statistical tasks by reading part two of spss for Students. It covers common statistics, regression, and graphs.
The Data Editor
This window is where you enter, edit, view, transform and label the data that you want to analyse. Each column represents a single variable (for example, age or height) and each row represents a single observation or case (a respondent in a survey, for example).


Defining Variables
Before entering data, you should define it by naming each variable and specifying its type (for example, numeric, string or date) and its length or display width.
To define variables select the Variable View tab located at the bottom of the Data Editor.
To define variables select the Variable View tab located at the bottom of the Data Editor. In this view, each variable is now represented by a row and each column represents a characteristic of the variable such as name, type or length:
Must not start with numbers.
String is for any variable that contains 'alpha' characters, (anything other than numbers) as data. Numeric is the most common and is the default type
Variable Labels
Variable labels allow you to attach longer descriptions to variable names. These labels may be displayed in spss output to make the output more meaningful.
Value Labels
An example of the use of value labels might be where your data represents the value 'Present' by the number 1 and 'Absent' by the number 2 or 'Male' by the letter M and 'Female' by the letter F.
SPSS will not allow you to type alpha characters into columns defined as numeric. To use characters specify the Type as 'string' on the Variable View sheet.
SPSS Appendix 1: The Menus
SPSS has ten main menus available when you are viewing the Data Editor window. The list of menu options changes when you are in different windows. The menus follow the standard Windows format of having the File and Edit menus to the left-hand side of the screen, and the Window and Help menus on the right. The menus found in the Data Editor window are described in more detail below:
File - The File menu deals with all the file-handling aspects of SPSS. such as opening an existing file, opening a new file, saving a file or printing a file. There is an Apply Data Dictionary command that stores information about your data. This is especially useful if you have received data from elsewhere and you need to know what it is and how it is organised. The command to exit from spss when you have finished is in this menu.
Edit - This menu contains commands for undoing the last action, cut, copy and paste actions, and the command for find.
View - The View menu changes some display attributes, such as the appearance of grid lines and whether value labels are displayed instead of codes. The chosen commands are marked with a tick.
Data - The Data menu is used to make global changes to spss data files, such as merging files, transposing variables and cases, or creating subsets of cases for analysis. You can insert new variables and new cases, or you can sort your cases according to a particular variable, transpose the cases or weight the cases. These changes are temporary unless you specifically save the file with the changes.
Transform - The Transform menu is used to make changes to selected variables in the data file and to compute new variables based on the values of existing ones. Random numbers can be generated and ranking or recoding of data can also be done. Changes are only temporary unless you specifically save the data file
Analyze - The Analyze menu contains all the categories of statistics that spss is capable of performing. The arrows to the right of all the menu options indicate that there is a submenu of more specific tests or groups of tests.
Graphs - The Graph menu enables you to plot graphs from the data. Some of these graphs can also be plotted as part of a wider analysis. spss supports the five main chart types - Bar (or Column), Line, Area, Pie and High-Low. It can also produce real histograms, quality control graphs, scatter plots in up to 3 dimensions, and several other specialist graph types (version 9.0 can also produce an ROC curve). One extra graph spss supports is the Boxplot, used mainly in non-parametric analysis, or when you want to get a feel for the shape of a small quantity of data.
Utilities - The Utilities menu contains a list of variables currently available from the data sheet; it allows you to define sets of variables that need to be analysed together. Finally the Utilities menu allows you to run a script or change the menus using the menu editor.
Window - The Window menu allows you to select and resize the windows used in SPSS. Most of the commands here can also be done with the mouse.
Help - Contains the commands for the spss Help system.
Appendix 2: The Main Toolbar
File Buttons
The first three buttons give the three most common commands from the File menu – Open an Existing File, Save the Current File and Print the Current File respectively.
Dialog Recall
This button gives you immediate access to the last 12 dialog boxes you were working with. This is extremely useful if you are building up an analysis and constantly going back to the same box to change or modify an option.
Go to Chart
This button allows you to go to an open Chart Editor.
Go to Data
When in a window other than the Data Editor window this button will return you to the Data Editor.
Go to Case
This button allows you to go directly to a specific case in the data editor. It is useful if you need to edit your data, or if something unusual has cropped up in your analysis and you want to check out the source data.
Variables
Clicking on this button produces a dialog box containing a list of all the variables defined in the data file. Selecting a variable from this list displays the variables properties - its name, label, type, information about missing values and the value labels. This box can be kept open whilst you work with the data file so that you can examine a variable's information as you examine the results of an analysis.
Find
This button allows you to perform a simple search to find a value.
Insert Case / Insert Variable
Data entry, being the tedious exercise that it is, is fraught with errors. Consequently it is not unusual to find yourself wanting to add a case or a variable in the middle of ones already created. These two buttons will add a blank row or column in your data set to allow you to do that.
Split File/Weight Cases/Select Cases
These buttons allow you to do three of the Data menu commands with ease.
Value Labels
In spss you can create meaningful labels for non-meaningful numerical data. This button allows you to display the labels in the data editor so that you don’t have to remember what the numbers meant.
Use Sets
In spss you can group variables together into sets so that the variables can be analysed together. This button allows you to specify what sets of the ones you have defined you want to use.
Designate Window Button
SPSS allows you to have more than one Output or Syntax window open at any one time so that the results you create can be sorted into different files. One of the Output windows has to be the Designated Window. This is indicated by an exclamation mark (!) in the title bar. If you want an Output or Syntax window to become the designated window, make it the active window and click this button on the toolbar.
Note: The Designated Window is not the same as the Active Window. The Active Window is the window at the forefront of your screen. Anything you type, or any menus you use should happen in the Active window. Of all the windows you have open on your desktop, only one will be the Active Window. The term Designated Window only applies to Output and Syntax windows which act as a destination for spss produced text. There is one designated Output window at any one point in time plus one designated Syntax window.
Run Commands
This button allows you to run commands from the Syntax window. spss will begin from the current insertion point and go through the commands until it finishes.
In this data file, cases represent individual respondents to a survey.
Variables represent responses to each question asked in the survey.
The Data Editor displays the contents of the active data file. The information in the Data Editor consists of variables and cases.
• In Data View, columns represent variables, and rows represent cases (observations).
• In Variable View, each row is a variable, and each column is an attribute that is associated with that variable.
Variables are used to represent the different types of data that you have compiled. A common analogy is that of a survey. The response to each question on a survey is equivalent to a variable. Variables come in many different types, including numbers, strings, currency, and dates
New variables are automatically given a Numeric data type.
Labels can be up to 255 bytes. These labels are used in your output to identify the different variables.
Value labels provide a method for mapping your variable values to a string label. In this example, there are two acceptable values for the marital variable.
The value is the actual numeric value.
• Nominal. Categorical data where there is no inherent order to the categories. For example, a job category of sales is not higher or lower than a job category of marketing or research.
يستخدم مع المتغيرات الاسمية دون أفضلية فى الترتيب أجا منصورة مت غمر

• Ordinal. Categorical data where there is a meaningful order of categories, but there is not a measurable distance between categories. For example, there is an order to the values high, medium, and low, but the "distance" between the values cannot be calculated.
Scale. Data measured on an interval or ratio scale, where the data values indicate both the order of values and the distance between values. For example, a salary of $72,195 is higher than a salary of $52,398, and the distance between the two values is $19,797. Also referred to as quantitative or continuous data.

 أنواع البيانات الإحصائية: Type of Data
كلما كان جمع البيانات دقيقا زادت ثقة الدارس في الاعتماد عليها، ولا يكون تحليل البيانات صحيحا أو مفيدا إذا كان هناك أخطاء في جمع البيانات، وهناك نوعين من البيانات وهما:
1- البيانات النوعية: Qualitative or Categorical Data
نحصل على هذا النوع من البيانات عندما تكون السمة ( الخاصية) تحت الدراسة هي سمة نوعية والتي يمكن تصنيفها حسب أصناف أو أنواع وليس بقيم عددية مثل تصنيف الجنس إلى ذكر وأنثى، وتصنيف كليات الجامعة إلى طب وهندسة وعلوم وتجارة وآداب وتجارة وغيرها ، وتستخدم عدة مقاييس لقياس البيانات النوعية منها:
(أ‌) التدرج الاسمي Nominal Scale
هذا المقياس يصنف عناصر الظاهرة التي تختلف في النوعية لا في الكمية، وكثيرا ما نستخدم الأعداد لتحديد هوية المفردات، وفي هذه الحالة لا يكون للعد ذلك المدلول الكمي الذي يفهم منه عادة. فمثلا يمكن استعمال العددين 0، 1 ليدلا على التصنيف حصب الجنس فيجعل الصفر يدل على الذكر و الـ 1 يدل على الأنثى، لاحظ أن 0، 1 لا يدلان على قيم عددية أي لا يخضعان للعمليات الحسابية لأنه يمكن تعيين أي عددين بدلهما ليدلا على نوع الجنس. وأمثلة أخرى على المقياس الاسمي : الحالة الاجتماعية ( أعزب- متزوج) ، ونوع العمل ( إداري – أكاديمي – عمل آخر) . ويجدر بالذكر أن هذا المقياس لا يعطي الأفضلية لإحدى طبقات المجتمع على الأخرى.
(ب)التدرج الترتيبي Ordinal Scale
يقع هذا التدرج في مستوى أعلى من التدرج الاسمي، فبالإضافة إلى خواص التدرج الاسمي فان التدرج الترتيبي يسمح بالمفاضلة، أي بترتيب العناصر حسب سلم معين: مثل الرتب الأكاديمية ( أستاذ (1)، استلذ مشارك(2)، أستاذ مساعد (3)، محاضر(4)، مدرس(5)، معيد(6)) وتقديرات الطلاب ( ممتاز(5)، جيد جدا(4)، جيد(3)، مقبول(2)، راسب(1)) ، وكذلك درجة التأييد لإجابة السؤال ( موافق بشدة (5)، موافق (4)، متردد(3)، لا أوافق (2)، لا أوافق بشدة (1))ويجدر بالذكر أن هذا المقياس لا يحدد الفرق بدقة بين قيم الأفراد المختلفة.



2- البيانات الكمية أو العددية Quantitative or Numerical Data
عندما تكون السمة تحت الدراسة قابلة للقياس على مقياس عددي فان البيانات التي نحصل عليها تتألف من مجموعة من الأعداد وتسمى بيانات كمية أو عددية، مثل علامات الطلاب في امتحان ما أو كميات السلع المستوردة، أجور العاملين في مصنع معين، وغيرها كثير.....
• Measures of central tendency. The most common measures of central tendency are the mean (arithmetic average) and median (value at which half the cases fall above and below).
What does this test do?
The one-way ANOVA compares the means between the groups you are interested in and determines whether any of those means are statistically significantly different from each other. Specifically, it tests the null hypothesis:

Types of Variables
Variables may have two types, continuous and categorical:
Continuous variables -- A continuous variable has numeric values such as 1, 2, 3.14, -5, etc. The relative magnitude of the values is significant (e.g., a value of 2 indicates twice the magnitude of 1). Examples of continuous variables are blood pressure, height, weight, income, age, and probability of illness. Some programs call continuous variables “ordered” or “monotonic” variables.
Categorical variables -- A categorical variable has values that function as labels rather than as numbers. Some programs call categorical variables “nominal” variables. For example, a categorical variable for gender might use the value 1 for male and 2 for female. The actual magnitude of the value is not significant; coding male as 7 and female as 3 would work just as well. As another example, marital status might be coded as 1 for single, 2 for married, 3 for divorced and 4 for widowed. DTREG allows you to use non-numeric (character string) values for categorical variables. So your dataset could have the strings “Male” and “Female” or “M” and “F” for a categorical gender variable. Because categorical values are stored and compared as string values, a categorical value of 001 is different than a value of 1. In contrast, values of 001 and 1 would be equal for continuous variables.





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