Friday, June 26, 2009
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With the help of this SPSS module, the researcher can easily construct a data dictionary of information (like value labels, etc.) and prepare the data for the purpose of analysis that is more flexible by utilizing the “define variable properties” tool in this SPSS module.
The SPSS regression model type of SPSS module helps the user or the researcher to use more sophisticated models to model the data. This SPSS module enables the user to model the data by utilizing a wide range of non linear regression models. This SPSS module is an add-on module for the SPSS Base. This SPSS module is used in various disciplines, like market research, which involves the study of consumer habits, loan assessment, etc. This SPSS module includes procedures like multinomial logistic regression, binary logistic regression, etc.
The SPSS module called the SPSS advanced model more accurately examines the complicated relationships by using strong statistical tools like multivariate analysis. This type of SPSS module is generally used in disciplines like medical research, which analyzes the patient survival rates, etc. Additionally, it can be helpful in the marketing sector where it can analyze the production process with the help of this SPSS module.
The SPSS module called the SPSS Neural Networks is a new addition in SPSS 16.0. This SPSS module offers non linear data modeling procedures, which help the user in creating more accurate and effective forecasting models. This part of the SPSS module can be used in database marketing, which involves the segmentation of the customer base. It can also be used in operational analysis to manage cash flow, etc.
The SPSS module called the SPSS classification trees constructs classification and decision trees within SPSS in order to help the user to identify the group categories and determine the relationships within the group categories. This part of the SPSS module allows the user to forecast future events of the group categories. This type of SPSS module can be used in the case of marketing in the public sector, or in determining credit risk scoring, etc.
The SPSS tables type of SPSS module allows the user to better understand the data, and it also reports the outcome in an appropriate manner. Other than the simple reporting program, this type of SPSS module provides the user with comprehensive analysis capabilities.
The SPSS module called the SPSS exact test carefully analyzes smaller datasets or those types of events that have rare occurrences. This type of SPSS module provides the user with more than 30 exact tests that include the entire range of the non parametric and the categorical data problems, which have smaller or larger numbers of data sets. This type of SPSS module includes one sample, two sample and K sample tests, etc.
The SPSS module called the SPSS categories provides the user with all the possible tools he wants in order to obtain an approach about complex, high dimensional or categorical data. This type of SPSS module includes correspondence analysis, categorical principal component analysis, multi dimensional scaling, preference scaling, etc.
Quantitative analysis is mainly classified into two categories: Estimation and Testing of the hypothesis.
In Quantitative analysis, the estimation part involves the ideal properties of the estimator that are used while estimating the data. In Quantitative analysis, the estimator is said to be an ideal estimator if it possesses any one of the properties of the ideal estimators. The properties of the ideal estimator in Quantitative analysis are unbiasedness, consistency, efficiency and sufficiency.
The unbiasedness property in Quantitative analysis is basically a kind of property that states that the estimator needs to give unbiased results to be considered an unbiased estimator. If an estimator gives a parameter with some constant as an estimate, then that estimator would not be considered an unbiased estimator.
A sufficient estimator is obtained by the researcher in Quantitative analysis with the help of a criterion called Fisher-Neyman Factorization criterion. This criterion in quantitative analysis would be appropriate for the convenient characterization of a sufficient estimator.
The second category of Quantitative analysis includes the tests of hypothesis. The concept of the testing of hypothesis in Quantitative analysis is mainly based on the testing of the null and alternative hypothesis. The null hypothesis in Quantitative analysis is an assertion that states that there is no statistical difference between the two samples under consideration. On the other hand, the alternative hypothesis in Quantitative analysis is the complement of the null hypothesis.
An important aspect of Quantitative analysis is that the researcher can also commit errors while computing Quantitative analysis. The errors that are conducted by the researcher in Quantitative analysis are divided into two categories, namely Type I error and the Type II error.
Type I error is the one that involves rejection of the correct sample during Quantitative analysis.
On the other hand, Type II error is the one that involves acceptance of an incorrect or false sample during Quantitative analysis.
In the field of medical / nursing, committing Type II error during Quantitative analysis is seriously dangerous. According to the definition of the Type II error in Quantitative analysis, if the researcher accepts a defective drug, then this can pose a serious health hazard problem.
In the field of psychology, quantitative techniques, like statistical significant tests like t-test, f-test, z-test, chi square test, etc. are used. Suppose one wants to compare the literacy rate in region A to the literacy rate in region B. After conducting a primary research over a given sample drawn from the region, Quantitative analysis will be followed. For this case, Quantitative analysis in the form of a right tailed t-test will be performed. This is called a right tailed test because in this case, the alternative hypothesis is LRA>LRB in Quantitative analysis. A t-test statistic in Quantitative analysis is obtained, and if the calculated value is more than the tabulated value at the given level of significance, then the null hypothesis will be rejected. Otherwise it will be accepted.
Monday, May 25, 2009
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Thursday, May 14, 2009
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Thursday, March 19, 2009
Multivariate Analysis of Variance (MANOVA) in SPSS is similar to ANOVA, except that instead of one metric dependent variable, we have two or more dependent variables. MANOVA in SPSS is concerned with examining the differences between groups. MANOVA in SPSS examines the group differences across multiple dependent variables simultaneously.
MANOVA in SPSS is appropriate when there are two or more dependent variables that are correlated. If there are multiple dependent variables that are uncorrelated or orthogonal, ANOVA on each of the dependent variable is more appropriate than MANOVA in SPSS.
Let us take an example in MANOVA in SPSS. Suppose that four groups, each consisting of 100 randomly selected individuals, are exposed to four different commercials about some detergents. After watching the commercial, each individual provided ratings on his preference for the product, preference for the manufacturing company and the preference for the commercial itself. Since these three variables are correlated, MANOVA in SPSS should be conducted to determine the commercial that received the highest preference across the three preference variables.
MANOVA in SPSS is done by selecting “Analyze,” “General Linear Model” and “Multivariate” from the menus.
As in ANOVA, the first step is to identify the dependent and independent variables. MANOVA in SPSS involves two or more metric dependent variables. Metric variables are those which are measured using an interval or ratio scale. The dependent variable is generally denoted by Y and the independent variable is denoted by X.
In MANOVA in SPSS, the null hypothesis is that the vectors of means on multiple dependent variables are equal across groups.
As in ANOVA, MANOVA in SPSS also involves the decomposition of the total variation and is observed in all the dependent variables simultaneously. The total variation in Y in MANOVA in SPSS is denoted by SSy, which can be broken down into two components:
SSy = SSbetween + SSwithin
Here the subscripts ‘between’ and ‘within’ refer to the categories of X in MANOVA in SPSS. SSbetween is the portion of the sum of squares in Y which is related to the independent variable or factor X. Thus, it is generally referred to as the sum of squares of X. SSwithin is the variation in Y which is related to the variation within each category of X. It is generally referred to as the sum of squares for errors in MANOVA in SPSS.
Thus in MANOVA in SPSS, for all the dependent variables (say) Y1,Y2 (and so on), the decomposition of the total variation is done simultaneously.
The next task in MANOVA in SPSS is to the measure the effects of X on Y1,Y2 (and so on). This is generally done by the sum of squares of X. The relative magnitude of the sum of squares of X in MANOVA in SPSS increases as the difference among the means of Y1,Y2 (and so on) in categories of X increases. The relative magnitude of the sum of squares of X in MANOVA in SPSS increases as the variation in Y1,Y2 (and so on) within the categories of X decreases.
The strength of the effects of X on Y1,Y2 (and so on) is measured with the help of η2 in MANOVA in SPSS .The value of η2 varies between 0 and 1. η2 assumes a value of 0 in MANOVA in SPSS when all the category means are equal, indicating that X has no effect on Y1,Y2 (and so on). η2 assumes a value of 1, when there is no variability within each category of X, while there is some variability between the categories.
The final step in MANOVA in SPSS is to calculate the mean square which is obtained by dividing the sum of squares by the corresponding degrees of freedom. The null hypothesis of equal vectors of mean is done by an F statistic, which is the ratio of the mean square related to the independent variable to the mean square related to error.
Wednesday, March 18, 2009
ANOVA in SPSS must have a dependent variable which should be metric (measured using an interval or ratio scale). ANOVA in SPSS must also have one or more independent variables, which should be categorical in nature. In ANOVA in SPSS, categorical independent variables are called factors. A particular combination of factor levels, or categories, is called a treatment.
In ANOVA in SPSS, there is one way ANOVA which involves only one categorical variable, or a single factor. For example, if a researcher wants to examine whether heavy, medium, light and nonusers of cereals differed in their preference for Total cereal, then the differences can be examined by the one way ANOVA in SPSS. In one way ANOVA in SPSS, a treatment is the same as the factor level.
If two or more factors are involved in ANOVA in SPSS, then it is termed as n way ANOVA. For example, if the researcher also wants to examine the preference for Total cereal by the customers who are loyal to it and those who are not, then we can use n way ANOVA in SPSS.
In ANOVA in SPSS, from the menu we choose:
“Analyze” then go to “Compare Means” and click on the “One-Way ANOVA.”
Now, let us discuss in detail how the software operates ANOVA:
The first step is to identify the dependent and independent variables. The dependent variable is generally denoted by Y and the independent variable is denoted by X. X is a categorical variable having c categories. The sample size in each category of X is generally denoted as n, and the total sample size N=nXc.
The next step in ANOVA in SPSS is to examine the differences among means. This involves decomposition of the total variation observed in the dependent variable. This variation in ANOVA in SPSS is measured by the sums of the squares of the mean.
The total variation in Y in ANOVA in SPSS is denoted by SSy, which can be decomposed into two components:
where the subscripts between and within refers to the categories of X in ANOVA in SPSS. SSbetween is the portion of the sum of squares in Y related to the independent variable or factor X. Thus it is generally referred to as the sum of squares of X. SSwithin is the variation in Y related to the variation within each category of X. It is generally referred to as the sum of squares for errors in ANOVA in SPSS.
The logic behind decomposing SSy is to examine the differences in group means.
The next task in ANOVA in SPSS is to measure the effects of X on Y, which is generally done by the sum of squares of X, because it is related to the variation in the means of the categories of X. The relative magnitude of the sum of squares of X in ANOVA in SPSS increases as the differences among the means of Y in categories of X increases. The relative magnitude of the sum of squares of X in ANOVA in SPSS increases as the variation in Y within the categories of X decreases.
The strength of the effects of X on Y is measured with the help of η2 in ANOVA in SPSS .The value of η2 varies between 0 and 1. It assumes a value 0 in ANOVA in SPSS when all the category means are equal, indicating that X has no effect on Y. The value of η2 becomes 1, when there is no variability within each category of X but there is still some variability between the categories.
The final step in ANOVA in SPSS is to calculate the mean square which is obtained by dividing the sum of squares by the corresponding degrees of freedom. The null hypothesis of equal means, which is done by an F statistic, is the ratio between the mean square related to the independent variable and the mean square related to the error.
N way ANOVA in ANOVA in SPSS involves simultaneous examination of two or more categorical independent variables, which is also computed in a similar manner.
A major advantage of ANOVA in SPSS is that the interactions between the independent variables can be examined.
For further assistance with SPSS click here.
Monday, March 16, 2009
1. Positive and negative correlation: When one variable moves in the same direction, then it is called positive correlation. When one variable moves in a positive direction, and a second variable moves in a negative direction, then it is said to be negative correlation.
2. Linear and non linear or curvi-linear correlation: When both variables change at the same ratio, they are known to be in linear correlation. When both variables do not change in the same ratio, then they are said to be in curvi-linear correlation. For example, if sale and expenditure move in the same ratio, then they are in linear correlation and if they do not move in the same ratio, then they are in curvi-linear correlation.
3. Simple, partial and multiple correlations: When two variables in correlation are taken in to study, then it is called simple correlation. When one variable is a factor variable and with respect to that factor variable, the correlation of the variable is considered, then it is a partial correlation. When multiple variables are considered for correlation, then they are called multiple correlations.
Degree of correlation
1. Perfect correlation: When both the variables change in the same ratio, then it is called perfect correlation.
2. High degree of correlation: When the correlation coefficient range is above .75, it is called high degree of correlation.
3. Moderate correlation: When the correlation coefficient range is between .50 to .75, it is called in moderate degree of correlation.
4. Low degree of correlation: When the correlation coefficient range is between .25 to .50, it is called low degree of correlation.
5. Absence of correlation: When the correlation coefficient is between . 0 to .25, it shows that there is no correlation.
There are many techniques to calculate the correlation coefficient, but in correlation in SPSS there are four methods to calculate the correlation coefficient. For continuous variables in correlation in SPSS, there is an option in the analysis menu, bivariate analysis with Pearson correlation. If data is in rank order, then we can use Spearman rank correlation. This option is also available in SPSS in analyses menu with the name of Spearman correlation. If data is Nominal then Phi, contingency coefficient and Cramer’s V are the suitable test for correlation. We can calculate this value by requesting SPSS in cross tabulation. Phi coefficient is suitable for 2×2 table. Contingency coefficient C is suitable for any type of table.
Testing the Significance of a Correlation:
Once we compute the correlation coefficient, then we will determine the probability that observed correlation occurred by chance. For that, we have to conduct a significance test. In significance testing we are mostly interested in determining the probability that correlation is the real one and not a chance occurrence. For this we determine hypothesis. There are two types of hypothesis.
Null hypothesis: In Null hypothesis we assume that there is no correlation between the two variables.
Alternative hypothesis: In alternative hypothesis we assume that there is a correlation between variables.
Before testing the hypothesis, we have to determine the significance level. In most of the cases, it is assumed as .05 or .01. At 5% level of significance, it means that we are conducting a test, where the odds are the case that the correlation is a chance occurrence is no more than 5 out of 100. After determining the significance level, we calculate the correlation coefficient value. The correlation coefficient value is determined by ‘r’ sign.
Coefficient of determination:
With the help of the correlation coefficient, we can determine the coefficient of determination. Coefficient of determination is simply the variance that can be explained by X variable in y variable. If we take the square of the correlation coefficient, then we will find the value of the coefficient of determination.
For further assistance with Correlations or SPSS Click Here.
Data Entry in SPSS is the most important task involved in any analysis. Data may exist in any form; it may be written on a piece of paper or it may be typed in a computer in raw data form. Before doing data entry in SPSS, one should start SPSS. It is very easy to start SPSS from the start menu by just clicking on the “SPSS” icon. As soon as SPSS opens, a window will appear, which is called the “data viewer window.” In SPSS, data viewer column value is known as the record measure or the variable and row to identify the case (or subject). If the data size is small, then the data entry in SPSS can be done manually in the data viewer window. Whenever a data size is large, then the data entry in SPSS is not possible manually. There are a number of options to do data entry in SPSS. Most of the data is available in Excel, CSV (comma separated value), and text. It is also available in other software formats like SAS, STATA etc.
Reading data using import wizard: Whenever data is outside the SPSS or in any other format, then by using the import wizard we can import data in SPSS. To use the import wizard, we have to click on the “open data” option available on the SPSS file menu, then click the “data” option. The window that appears after clicking the open data option is called the open data wizard. This window will show the location where the data is located. For example, if our data is in the D drive, then we have to click on the “my computer” icon and then find the “D drive,” and click on it to access our data, where we will select the folder in which data is available. As we select the data folder, the name of the data folder will appear below the file name option. If data is not in SPSS file format, then the file will not appear there. Thus, from the “file type” option, we will select the format of the data available over there for the specific file format. Then the file will appear in the desired format (over here it is the SPSS format). Then we can select that file and click on the “ok” button. Now data will appear in the data view window. This is an easy way to do data entry in SPSS when data is large. If data is in Excel format and it is in medium size, then by using the copy and paste option (available in SPSS) we can do data entry in SPSS. But just doing the data entry in SPSS is not sufficient for analysis.
Variable properties: One of the drawbacks of data entry in SPSS is that it cannot perform analysis on a character variable. To perform analysis, we have to convert the character variable to a numeric variable. There is another window in SPSS called the “variable view.” This window shows variables properties. In the variable view window, there are a number of options available that can help to clean up the data and to perform analysis. For data entry in SPSS, we can give only 8 characters to name a variable. But most of the time, a variable name is more than 8 characters. To overcome this problem, we can use the “variable view” window, where we can assign a label to that variable, and in this label, we can assign a label length as we want. To convert the character variable into a numeric variable, we will record the new value by using the “value” option. This option will ask for the old value and the new value that we want to assign to it. Sometimes there are a number of extreme values in data, and one more drawback for data entry in SPSS is that in SPSS, zero value is not considered as a missing value. But SPSS has an option called the “missing value option.” Through this option, we can write that this value is missing and this value is an extreme value. Now SPSS considers that value as the missing value. Sometimes data is in the numeric form and SPSS considers that variable as a string variable. In that case, we have to convert it into numeric type from the “type” option. There is another option through which we can assign a decimal point to the variable. We can fix the alignment of variable left, right, center etc. Sometimes during the analysis, we have to perform a number of mathematical operations with variables. For example, if we want to compute one variable from five variables by calculating an average of all the variables. The “compute” option available in the “transform” option can help you do several mathematical operations. Sometimes we have to split the data by category. By using the “split file” option available in “data” option, we can categorize data in different categories.
Syntax option: SPSS function can be performed in two different ways by using the “click” and “menu” option and the “command based” option. If someone is familiar with the SPSS command, then the above mentioned functions can also be performed in the syntax menu.
For more assistance with SPSS click here.
Thursday, March 12, 2009
SPSS Tutorial consists of the following topics:
This part of the SPSS Tutorial mainly explains the usage of sample files. Sample files are the files which contain fictitious survey data. Through the Introduction, we generally get familiar with SPSS as it gives a step-by-step account of how to open a data file, conduct analysis and see the output.
· Reading Data
There are various sources to import in SPSS format data files. These include spreadsheet applications (such as MS Excel), database applications (such as MS Access) and text files to import in SPSS format data files. Reading data in the SPSS Tutorial explains the process of reading the data in a step-by-step manner. This data is stored in SPSS- format data files imported from various sources.
· Using the Data Editor
The Data Editor in the SPSS Tutorial generally informs us about how to enter a string data or a numeric data in an SPSS data file while using the “variable view” and “data view” option. It also tells us about how to handle missing data, how to add the variable labels, how to value labels for numeric variables and string variables, or how to use value labels for data entry. It also discusses defining the variable properties for categorical variables in a graphical manner.
· Working with Multiple Data Sources
This part of the SPSS tutorial helps by telling us about how to switch back and forth between data sources, how to compare the contents of different data sources, how to copy and paste between data sources, and how to create multiple subsets of cases and/or variables for analysis. It also tutors on how to merge multiple data sources from various data formats without first saving each data source. In general, it tutors us about the basic handling of multiple data sources (including copying and pasting information between datasets).
· Examining Summary Statistics for Individual Variables
In this part of the SPSS tutorial, the tutorial mentions how the level of measurement of a variable influences the type of statistics that should be used. It mainly emphasizes the summary measures for categorical data and scale variables.
· Cross tabulation tables
This topic in the SPSS Tutorial is in the form of some case studies from the sample files. It handles the different types of cross tabulation including simple cross tabulation, count vs. percentages, significance testing for cross tabulations etc…
· Creating and Editing charts
In this section of the SPSS tutorial, the tutorial mainly details how to create a chart using the chart builder gallery. It also explains how to edit the charts. This can be understood with the help of examples of Pie Chart and Grouped Scatter Plot.
· Working with Output
In this section of the SPSS tutorial, the tutorial explains the output, which is displayed after every analysis. This section mainly covers the methodology of observing the output and editing it with the help of Pivot Table Editor as per the requirement.
· Working with Syntax
Instead of clicking on the menus to perform operations, one can write programs in SPSS. This topic in the SPSS Tutorial details information about editing the syntax while programming in SPSS. It also tells us about how to open and run a syntax file.
· Time Saving Features
This topic in the SPSS Tutorial generally gives information about features which can save time while performing the same type of analysis on similar sets of data.
· Customizing SPSS
This topic in the SPSS Tutorial gives guidance on how to make the frequently used menus and toolbars more accessible in order to save time.
· Getting started with tables
There is much analysis that results in the form of tables. This topic in the SPSS Tutorial explains the table add-on module, which is especially useful in survey analysis and market research.
· Index Feature
This feature in the SPSS Tutorial generally enables the user to be comfortable with all types of statistical analyses from A to Z. It details all the tests and techniques in a step-by-step graphical manner.
For assistance with using SPSS for your thesis or dissertation click here.