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.
Monday, May 25, 2009
Statistical Analysis Consulting
In the current fast-paced and demanding world of competition, it is rather important for any organization, business, or company to maintain quality in all aspects of their organization. These companies must be willing and able to maintain their high positions in the fast-changing world. In order to do so, they must have sound strategies. These sound strategies will allow them to excel to their highest potential. Small businesses and entrepreneurs, however, don’t always have the expertise and skills necessary to propel their organizations to the top. Statistical Analysis Consulting can be of great use, however, as Statistical Analysis Consulting can provide the necessary skills and expertise. These consulting services can also help with the burden of entrepreneurs who must complete tasks that are not within their area of expertise. Statistics consultants can help these time-strapped and struggling small businesses and entrepreneurs, as they can give professional guidance and important statistical analysis and research advice to these organizations.
First, it is crucial for a small business to realize exactly what kind of help needs to be gained from Statistical Analysis Consulting. In order to make sound business decisions, an organization or firm must realize its goals and objectives. Statistical Analysis Consulting can definitely help with this as the organization’s decisions should be based on the information and the conclusions provided by Statistical Analysis Consulting. Statistical Analysis Consulting can be extremely beneficial to just about every field possible (for example, marketing, medicine, telecommunications, and manufacturing, etc.). Thus, there are many statistical firms that are able to provide Statistical Analysis Consulting.
Additionally, Statistical Analysis Consulting is very cost effective. In comparison to how much money can be spent following an inefficient business plan, Statistical Analysis Consulting is well worth the cost. In other words, Statistical Analysis Consulting is not very expensive and Statistical Analysis Consulting can be obtained at very reasonable prices. If one were to do a cost/benefit analysis, he or she would see just how cost effective it is to obtain Statistical Analysis Consulting.
With Statistical Analysis Consulting, the consultant shares his or her knowledge and input with the business or organization. This is extremely helpful as it provides the team with a fresh outlook and new ideas. These new ideas, when put into play, can greatly benefit any business, organization or company.
Additionally, Statistical Analysis Consulting is invaluable for students who are writing their dissertation or doing any project. They act as an expert advisor throughout the entire dissertation. Statistical Analysis Consulting can help any student working on his or her dissertation as Statistical Analysis Consulting can be extremely helpful in the collection of data and the analysis of that data. Because a dissertation is such a large part of a doctoral student’s academic career, getting Statistical Analysis Consulting can be one of the best decisions a student can make.
Experts who offer Statistical Analysis Consulting must be skilled in many areas. In short, people who offer Statistical Analysis Consulting must be able to communicate extremely well, they must have a vast amount of statistical knowledge, they must be experts in science, and they must be proficient with computers. In short, then, people who offer Statistical Analysis Consulting must be ready, able, and willing to help.
Thus, businesses and students alike can benefit from Statistical Analysis Consulting. There is no better way to ensure success than to seek the help of Statistical Analysis Consulting. Because Statistical Analysis Consulting is provided by expert statisticians with a unique outside perspective, Statistical Analysis Consulting can help any business or student achieve results. In this ever-changing, ever fast-paced environment, there is no better decision to make than to seek Statistical Analysis Consulting.
First, it is crucial for a small business to realize exactly what kind of help needs to be gained from Statistical Analysis Consulting. In order to make sound business decisions, an organization or firm must realize its goals and objectives. Statistical Analysis Consulting can definitely help with this as the organization’s decisions should be based on the information and the conclusions provided by Statistical Analysis Consulting. Statistical Analysis Consulting can be extremely beneficial to just about every field possible (for example, marketing, medicine, telecommunications, and manufacturing, etc.). Thus, there are many statistical firms that are able to provide Statistical Analysis Consulting.
Additionally, Statistical Analysis Consulting is very cost effective. In comparison to how much money can be spent following an inefficient business plan, Statistical Analysis Consulting is well worth the cost. In other words, Statistical Analysis Consulting is not very expensive and Statistical Analysis Consulting can be obtained at very reasonable prices. If one were to do a cost/benefit analysis, he or she would see just how cost effective it is to obtain Statistical Analysis Consulting.
With Statistical Analysis Consulting, the consultant shares his or her knowledge and input with the business or organization. This is extremely helpful as it provides the team with a fresh outlook and new ideas. These new ideas, when put into play, can greatly benefit any business, organization or company.
Additionally, Statistical Analysis Consulting is invaluable for students who are writing their dissertation or doing any project. They act as an expert advisor throughout the entire dissertation. Statistical Analysis Consulting can help any student working on his or her dissertation as Statistical Analysis Consulting can be extremely helpful in the collection of data and the analysis of that data. Because a dissertation is such a large part of a doctoral student’s academic career, getting Statistical Analysis Consulting can be one of the best decisions a student can make.
Experts who offer Statistical Analysis Consulting must be skilled in many areas. In short, people who offer Statistical Analysis Consulting must be able to communicate extremely well, they must have a vast amount of statistical knowledge, they must be experts in science, and they must be proficient with computers. In short, then, people who offer Statistical Analysis Consulting must be ready, able, and willing to help.
Thus, businesses and students alike can benefit from Statistical Analysis Consulting. There is no better way to ensure success than to seek the help of Statistical Analysis Consulting. Because Statistical Analysis Consulting is provided by expert statisticians with a unique outside perspective, Statistical Analysis Consulting can help any business or student achieve results. In this ever-changing, ever fast-paced environment, there is no better decision to make than to seek Statistical Analysis Consulting.
Thursday, May 14, 2009
Statistical Analysis Help
Data analysis and statistics are extremely important to many fields. In fact, statistics and data analysis are crucial for conducting internal audits and performance reviews. In many industries like marketing, research, financial services and medical or clinical research, statistics and data analysis are crucial. This is true because of the inherent power of statistics in the analyzing of data. Statistics can also be essential to achieve extremely important growth and efficiency objectives. Because it is so important for businesses to use statistics to lead to meaningful results, interpretations and business decisions, the importance of statistics can not be overstated. Unfortunately, sometimes businesses do not have the necessary skills to harness the power of statistics. This, however, is exactly where Statistical Analysis Help can help as Statistical Analysis help can assist with all aspects of statistics.
Click here for Statistical Analysis Help.
Statistical Analysis help is not limited to helping businesses, however. Statistical Analysis Help can play a pivotal role in any project involving statistics as Statistical Analysis Help produces high quality results regardless of the field or subject matter. Statistical Analysis Help can give assistance as it provides statistical tools and advice. Additionally, Statistical Analysis Help is given by an expert who has the know-how to implement the crucial tools in a project. Statistical Analysis Help is given by a statistical analysis consultant who is able to step into diverse settings. The consultant can then evaluate the scenario and make recommendations based on numerous things like which methods are most suitable to data collection, how to analyze the results, and how to interpret the results. Depending on what the client, business or student needs, Statistical Analysis Help can offer a vast array of services. All of these services will suit different goals and budgets.
Statistical Analysis Help often occurs in an academic environment. This is true because students often need Statistical Analysis Help. Statistical Analysis Help, then, is valuable to any student in need of help with statistics. This is often the case when students are working on their dissertations. Statistical Analysis Help can help students as well as faculty members. Statistical Analysis Help, then, is given with the purpose of helping graduate students and faculty members who need any kind of help with statistical analysis. This help can be used in subjects outside the normal statistics-related subjects. Statistical Analysis Help can also assist students and faculty on statistical methodologies as well as research design. In short, Statistical Analysis Help can assist any student or any faculty member at any time in their research project.
Statistical Analysis Help can also give immense support to businesses. This is true because Statistical Analysis help can assist a business in taking charge of their internal data flow. Statistical Analysis Help uses internal data to extract valuable information. This can help a business be more efficient and save a lot of money.
It is important, of course, to find the right consultant to provide Statistical Analysis Help. Obviously, the consultant should be professional. The consultant should also be responsive and able to communicate effectively. Skills and knowledge are clearly a must. Equally important is experience. A consultant must also come up with a solution that is doable—in other words, they must work closely with the company, business or student to listen to the needs of that client. In other words, they must see the big picture and help the client attain success in a manner that is acceptable to the client.
Statistical Analysis Help can be invaluable. This is true as it can save anyone much time and money. Acquiring the necessary information to do statistics and interpreting the results of those statistics can be a lengthy and difficult task. Statistical Analysis Help can make the entire process of statistics more efficient and easy to understand. Clearly, Statistical Analysis Help is invaluable for any project that requires any kind of statistics.
Click here for Statistical Analysis Help.
Statistical Analysis help is not limited to helping businesses, however. Statistical Analysis Help can play a pivotal role in any project involving statistics as Statistical Analysis Help produces high quality results regardless of the field or subject matter. Statistical Analysis Help can give assistance as it provides statistical tools and advice. Additionally, Statistical Analysis Help is given by an expert who has the know-how to implement the crucial tools in a project. Statistical Analysis Help is given by a statistical analysis consultant who is able to step into diverse settings. The consultant can then evaluate the scenario and make recommendations based on numerous things like which methods are most suitable to data collection, how to analyze the results, and how to interpret the results. Depending on what the client, business or student needs, Statistical Analysis Help can offer a vast array of services. All of these services will suit different goals and budgets.
Statistical Analysis Help often occurs in an academic environment. This is true because students often need Statistical Analysis Help. Statistical Analysis Help, then, is valuable to any student in need of help with statistics. This is often the case when students are working on their dissertations. Statistical Analysis Help can help students as well as faculty members. Statistical Analysis Help, then, is given with the purpose of helping graduate students and faculty members who need any kind of help with statistical analysis. This help can be used in subjects outside the normal statistics-related subjects. Statistical Analysis Help can also assist students and faculty on statistical methodologies as well as research design. In short, Statistical Analysis Help can assist any student or any faculty member at any time in their research project.
Statistical Analysis Help can also give immense support to businesses. This is true because Statistical Analysis help can assist a business in taking charge of their internal data flow. Statistical Analysis Help uses internal data to extract valuable information. This can help a business be more efficient and save a lot of money.
It is important, of course, to find the right consultant to provide Statistical Analysis Help. Obviously, the consultant should be professional. The consultant should also be responsive and able to communicate effectively. Skills and knowledge are clearly a must. Equally important is experience. A consultant must also come up with a solution that is doable—in other words, they must work closely with the company, business or student to listen to the needs of that client. In other words, they must see the big picture and help the client attain success in a manner that is acceptable to the client.
Statistical Analysis Help can be invaluable. This is true as it can save anyone much time and money. Acquiring the necessary information to do statistics and interpreting the results of those statistics can be a lengthy and difficult task. Statistical Analysis Help can make the entire process of statistics more efficient and easy to understand. Clearly, Statistical Analysis Help is invaluable for any project that requires any kind of statistics.
Thursday, March 19, 2009
MANOVA in SPSS
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
Analysis of Variance, i.e. ANOVA in SPSS, is used for examining the differences in the mean values of the dependent variable associated with the effect of the controlled independent variables, after taking into account the influence of the uncontrolled independent variables. Essentially, ANOVA in SPSS is used as the test of means for two or more populations.
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:
SSy=SSbetween+SSwithin
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.
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:
SSy=SSbetween+SSwithin
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
Correlation in SPSS
Correlation is a statistical technique that shows how strongly two variables are related to each other or the degree of association between the two. For example, if we have the weight and height data of taller and shorter people, with the correlation between them, we can find out how these two variables are related. We can also find the correlation between these two variables and say that their weights are positively related to height. Correlation is measured by the correlation coefficient. It is very easy to calculate the correlation coefficient in SPSS. Before calculating the correlation in SPSS, we should have some basic knowledge about correlation. The correlation coefficient should always be in the range of -1 to 1. There are three types of correlation:
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.
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.
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