SPSS module consists of modules that have various statistical procedures in the SPSS 16.0 version. The SPSS module called the SPSS Base includes the basic statistical analysis that a non statistical person needs to become an expert in SPSS. This SPSS module provides a broad collection of the capabilities for the entire analytical process. With the help of this SPSS module, the researcher can make decisions quiet efficiently.
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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.
Friday, June 26, 2009
Quantitative Analysis
Quantitative analysis consists of performing analysis on quantitative data with the help of several statistical techniques. Quantitative analysis generally involves statistical techniques like significance testing, regression analysis, multivariate analysis, etc. Analysis on quantitative data, called Quantitative analysis, is mainly done by those people who have an in-depth knowledge of statistical techniques that are used to perform Quantitative analysis. Quantitative analysis is mainly done in order to draw a statistical inference about the data under consideration or under study. Quantitative analysis is basically carried out by the researcher at that time when he wants to predict, understand, and interpret the data in a statistical manner in order to get a clear picture about the population under study.
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.
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.
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