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IBM® SPSS® Statistics is a comprehensive system for analyzing data. The Advanced and SPSS Statistics: Advanced Statistical Procedures Companion, written by Marija Norušis and published by GLM Repeated Measures Contrasts. When obtaining this book ibm spss statistics 19 advanced statistical procedures companion pdf%0D as referral to review, you could get not just inspiration but. IBM SPSS Statistics 19 Advanced Statistical Procedures Companion (): Marija Norusis, Inc. SPSS: Books.

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IBM® SPSS® Statistics is a comprehensive system for analyzing data. and SPSS Statistics: Advanced Statistical Procedures Companion, written by Marija. IBM SPSS Statistics 19 Advanced Statistical Procedures Companion. Marija Norusis PASW Statistics 18 Advanced Statistical Procedures. Norusis & SPSS Inc. For hierarchical clustering, you choose a statistic that quantifies how far apart (or IBM SPSS Statistics has three different procedures that can be used to cluster.

Command syntax programming has the benefits of reproducible output, simplifying repetitive tasks, and handling complex data manipulations and analyses. Additionally, some complex applications can only be programmed in syntax and are not accessible through the menu structure. The pull-down menu interface also generates command syntax: this can be displayed in the output, although the default settings have to be changed to make the syntax visible to the user. They can also be pasted into a syntax file using the "paste" button present in each menu. Programs can be run interactively or unattended, using the supplied Production Job Facility.

Explanation: We entered "1" because this reflects the code assigned to the event occurring in our example i. In our example, "0" meant that the data was "censored" and "1" that the "event" occurred. If you are unsure how to set up this coding in the Value Labels dialogue box in SPSS Statistics, we should you how in our enhanced Kaplan-Meier guide, which you can access by subscribing to the site here.


Leave the Survival table s and Mean and median survival checkboxes ticked in the —Statistics— area and select the Survival checkbox in the —Plots— area. Click the button and you will be returned to the Kaplan-Meier dialogue box. Click the button to generate the output for the Kaplan-Meier test.

Now that you have run the Kaplan-Meier procedure, we show you how to interpret and report your results. Join the 10,s of students, academics and professionals who rely on Laerd Statistics. If you have statistically significant differences between the survival functions, you will also need to interpret the Pairwise Comparisons table, allowing you to determine where the differences between your groups lie.

In the sections below, we focus on the Overall Comparisons table, as well as touching on the Survival Functions plot. Note: If you are unsure how to interpret and report the descriptive statistics from the Mean and Medians for Survival Time table, or the the percentages from the Case Processing Summary table, which is part of the assumption testing we discussed in the Assumptions section earlier, we show you how to do this in our enhanced Kaplan-Meier guide.

If you find that you have statistically significant differences between your survival distributions, we also explain how to interpret and report the Pairwise Comparisons table. You will also need to run additional procedures in SPSS Statistics to carry out these pairwise comparisons because the 13 steps in the Test Procedure in SPSS Statistics section above do not include the procedure for pairwise comparisons.

SPSS Statistics Survival functions The first and best place to start understanding and interpreting your results is usually with the plot of the cumulative survival functions for the different groups of the between-subjects factor i. This is a plot of the cumulative survival proportion against time for each intervention group and is labelled the Survival Functions plot in SPSS Statistics. The plot above will help you to understand how the survival distributions compare between groups.

A useful function of the plot is to illustrate whether the survival curves cross each other i. This has implications on the power of the statistical tests to detect differences between the survival distributions.

In addition, you should decide whether the survival curves are similarly shaped, even if they are above or below one another. This has implications for the choice of statistical test that is used to analyse the results from the Kaplan-Meier method i.

The "event" you are interested in is usually considered to be deleterious e.

IBM SPSS Statistics Base 19

Therefore, it is not something you want to occur. All other things being equal e. We can see from our plot that the cumulative survival proportion appears to be much higher in the hypnotherapy group compared to the nicotine patch and e-cigarette groups, which do not appear to differ considerably although the nicotine patch intervention appears to have a small advantage on survival; that is, fewer participants resuming smoking.

It would appear that the hypnotherapy programme significantly prolongs the time until participants resume smoking i. We will look into determining if these survival curves are statistically significantly different later. This will help to clarify the various survival times for your groups.

You can also plot the median survival times of the groups on top of the survival plot illustrated above. All three tests compare a weighted difference between the observed number of events i. We discuss the differences between these three statistical tests and which test to choose in our enhanced Kaplan-Meier guide. It is fairly common to find that all three tests will lead you to the same conclusion i. Unfortunately, you cannot rely on there being one best test — it will depend on your data.

If you choose the approach of picking a particular test, you will need to do this before analysing your data. You shouldn't run all of them and then simply pick the one that happens to have the "best" p-value for your study Hosmer et al. In our example, the log rank test is the most appropriate, so we discuss the results from this test in the next section. The log rank test is testing the null hypothesis that there is no difference in the overall survival distributions between the groups e.

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.

Click the Variable View tab at the bottom of the Data ditor window. You need to define the variables that will be used. In this case, only three variables are needed: age, marital status, andincome.

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In the second row, type marital. In the third row, type income. New variables are automatically given a Numeric data type. If you don t enter variable names, unique names are automatically created.

However, these names are not descriptive and are not recommended for large data files. Click the Data View tab to continue entering the data. The names that you entered in Variable View are now the headings for the first three columns in Data View.

Figure Values entered in Data View In the age column, type In the marital column, type 1. In the income column, type Move the cursor to the second row of the first column to add the next subject s data.

In the age column, type In the marital column, type 0. In the income column, type Currently, the age and marital columns display decimal points, even though their values are intended to be integers. To hide the decimal points in these variables: Click the Variable View tab at the bottom of the Data ditor window. In the Decimals column of the age row, type 0 to hide the decimal. Figure Updated decimal property for age and marital ntering String Data Non-numeric data, such as strings of text, can also be entered into the Data ditor.

In the first cell of the first empty row, type sex for the variable name. Click the Type cell next to your entry. Figure ButtonshowninTypecellforsex Select String to specify the variable type. Click OK to save your selection and return to the Data ditor. Figure Variable Type dialog box 41 31 Using the Data ditor Defining Data Inadditiontodefining data types, you can also define descriptive variable labels and value labels for variable names and data values.

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These descriptive labels are used in statistical reports and charts. Adding Variable Labels Labels are meant to provide descriptions of variables. These descriptions are often longer versions of variable names. Labels can be up to bytes. These labels are used in your output to identify the different variables. In the Label column of the age row, type Respondent's Age.

In the Label column of the marital row, type Marital Status. In the Label column of the income row, type Household Income. In the Label column of the sex row, type Gender. The most common data types are numeric and string, but many other formats are supported. In the current data file, the income variable is defined as a numeric type. Click the Type cell for the income row,andthenclickthebuttonontherightsideofthecellto open the Variable Type dialog box.

Select Dollar. Figure Variable Type dialog box The formatting options for the currently selected data type are displayed. Click OK to save your changes. Adding Value Labels for Numeric 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. A value of 0 means that the subject is single, and a valueof1meansthatheorsheismarried.

Click the Values cell for the marital row, and then click the button on the right side of the cell to open the Value Labels dialog box. The value is the actual numeric value. The value label is the string label that is applied to the specified numeric value. Type 0 in the Value field.

Type Single in the Label field. Click Add, and then click OK to save your changes and return to the Data ditor. These labels can also be displayed in Data View, which can make your data more readable. Click the Data View tab at the bottom of the Data ditor window. This setup has the benefit of suggesting a valid response and providing a more descriptive answer. For example, your data may use single letters, M or F, to identify the sex of the subject.

Value labels can be used to specify that M stands for Male and F stands for Female. Click the Values cell in the sex row,andthenclickthebuttonontherightsideofthecellto open the Value Labels dialog box. Type F in the Value field, and then type Female in the Label field. Because string values are case sensitive, you should be consistent.

A lowercase m is not the same as an uppercase M. Using Value Labels for Data ntry You can use value labels for data entry. In the firstrow,selectthecellforsex. Click the button on the right side of the cell, and then choose Male from the drop-down list.

In the second row, select the cell for sex. Figure Using variable labels to select values Only defined values are listed, which ensures that the entered data are in a format that you expect. Handling Missing Data Missing or invalid data are generally too common to ignore.

Survey respondents may refuse to answer certain questions, may not know the answer, or may answer in an unexpected format. If you don t filter or identify these data, your analysis may not provide accurate results. For numeric data, empty data fields or fields containing invalid entries are converted to system-missing, which is identifiable by a single period.

For example, you may find it useful to distinguish between those respondents who refused to answer a question and those respondents who didn t answer a question because it was not applicable. Click the Missing cell in the age row, and then click the button on the right side of the cell to open the Missing Values dialog box. In this dialog box, you can specify up to three distinct missing values, or you can specify a range of values plus one additional discrete value. Type in the first text box and leave the other two text boxes empty.

Click OK to save your changes and return to the Data ditor. Now that the missing data value has been added, a label can be applied to that value. Click the Values cell in the age row, and then click the button on the right side of the cell to open the Value Labels dialog box. Type in the Value field. Type No Response in the Label field. Figure Value Labels dialog box Click Add to add this label to your data file.

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Missing Values for a String Variable Missing values for string variables are handled similarly to the missing values for numeric variables. However, unlike numeric variables, empty fields in string variables are not designated as system-missing.

Rather, they are interpreted as an empty string. Click the Missing cell in the sex row, and then click the button on the right side of the cell to open the Missing Values dialog box.

Select Discrete missing values. Type NR in the first text box. Missing values for string variables are case sensitive.

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So, a value of nr is not treated as a missing value. Now you can add a label for the missing value. Type NR in the Value field. Figure Value Labels dialog box Click Add to add this label to your project. Copying and Pasting Variable Attributes After you ve defined variable attributes for a variable, you can copy these attributes and apply them to other variables.

Click the Values cell in the age row. Figure Multiple cells selected When you paste the attribute, it is applied to all of the selected cells. New variables are automatically created if you paste the values into empty rows. Figure All values pasted into a row Defining Variable Properties for Categorical Variables For categorical nominal, ordinal data, you can use Define Variable Properties to define value labels and other variable properties.

The Define Variable Properties process: Scans the actual data values and lists all unique data values for each selected variable. Identifies unlabeled values and provides an auto-label feature. Provides the ability to copy defined value labels from another variable to the selected variable or from the selected variable to additional variables. This example uses the data file demo. For more information, see the topic Sample Files in Appendix A on p This data file already has defined value labels, so we will enter a value for which there is no defined value label.

In Data View of the Data ditor, click the first data cell for the variable ownpc you may have to scroll to the right , and then enter You might notice that the measurement level icons for all of the selected variables indicate that they are scale variables, not categorical variables.

All of the selected variables in this example are really categorical variables that use the numeric values 0 and 1 to stand for No and Yes, respectively and one of the variable properties that we ll change with Define Variable Properties is the measurement level.

Click Continue. The current level of measurement for the selected variable is scale. You can change the measurement level by selecting a level from the drop-down list, or you can let Define Variable Properties suggest a measurement level. Click Suggest. The Suggest Measurement Level dialog box is displayed. Select Ordinal, and then click Continue. The measurement level for the selected variable is now ordinal. The Value Label grid displays all of the unique data values for the selected variable, any defined value labels for these values, and the number of times count that each value occurs in the scanned cases.

The value that we entered in Data View, 99, is displayed in the grid. The count is only 1 because we changed the value for only one case, and the Label column is empty because we haven t defined a value label for 99 yet. An X in the first column of the Scanned Variable List also indicates that the selected variable has at least one observed value without a defined value label. In the Label column for the value of 99, enter No answer. ChecktheboxintheMissing column for the value 99 to identify the value 99 as user-missing.

Data values that are specified as user-missing are flagged for special treatment and are excluded from most calculations. If you select any other variable in the Scanned Variable List of the Define Variable Properties main dialog box now, you ll see that they are all ordinal variables, with a value of 99 defined as user-missing and a value label of No answer.

Figure New variable properties defined for ownfax 59 49 Using the Data ditor Click OK to save all of the variable properties that you have defined. Compare the contents of different data sources. Copy and paste data between data sources. Merge multiple data sources from various data formats for example, spreadsheet, database, text data without saving each data source first. Basic Handling of Multiple Data Sources Figure Two data sources open at same time By default, each data source that you open is displayed in a new Data ditor window.

Any previously open data sources remain open and available for further use. When you first open a data source, it automatically becomes the active dataset. Only the variables in the active dataset are available for analysis. Figure Variable list containing variables in the active dataset You cannot change the active dataset when any dialog box that accesses the data is open including all dialog boxes that display variable lists.

At least one Data ditor window must be open during a session. When working with command syntax, the active dataset name is displayed on the toolbar of the syntax window. Select a dataset name from the toolbar in the syntax window. Figure Open datasets displayed on syntax window toolbar Copying and Pasting Information between Datasets You can copy both data and variable definition attributes from one dataset to another dataset in basically the same way that you copy and paste information within a single data file.

Copying and pasting selected data cells in Data View pastes only the data values, with no variable definition attributes. Copying and pasting an entire variable in Data View by selecting the variable name at the top of the column pastes all of the data and all of the variable definition attributes for that variable. Copying and pasting variable definition attributes or entire variables in Variable View pastes the selected attributes or the entire variable definition but does not paste any data values.

Renaming Datasets When you open a data source through the menus and dialog boxes, each data source is automatically assigned a dataset name of DataSetn, wheren is a sequential integer value, and when you open a data source using command syntax, no dataset name is assigned unless you explicitly specify one with DATAST NAM.

Click the General tab. Select check Open only one dataset at a time. We will use the data file demo. For more information, see the topic Sample Files in Appendix A on p Level of Measurement Different summary measures are appropriate for different types of data, depending on the level of measurement: Categorical.

Data with a limited number of distinct values or categories for example, gender ormaritalstatus.

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Alsoreferredtoasqualitative data. There are two basic types of categorical data: 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. 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, andlow, but the distance between the values cannot be calculated. Data measured on an interval or ratio scale, where the data values indicate both the order of values and the distance between values. Also referred to as quantitative or continuous data.

Summary Measures for Categorical Data For categorical data, the most typical summary measure is the number or percentage of cases in each category. The mode is the category with the greatest number of cases. For ordinal data, the median the value at which half of the cases fall above and below may also be a useful summary measure if there is a large number of categories.