Calculate composite factor scores after factor analysis

Calculate composite factor scores after factor analysis
After running Cronbach's alpha to check the reliability scale, you will run EFA factor analysis to confirm the model of your research, show how many factors.
If you continue to run the correlation and multivariate regression in SPSS, next step is to calculate composite factor scores representing these factors.

There are usually two methods to Calculate composite factor scores after factor analysis


Method 1. Creating Summated Scale


You can use the function to calculate the mean average of the observed variables of factors – this is applied quite popular because it is easy to explain
Enter menu Transform> Compute Variable


Calculate the average value of factor by using the mean() function in the numeric Expression of the SPSS

 

Method 2.

When analyzing EFA in SPSS, you select the button to save the Scores automatically (SPSS will calculate for you). Number calculated in this way has been standardized SPSS. According to experience,this way suitable for the observed variables with different units.

 

Formula to determine how much the sample size in SPSS

The formula to determine how much the sample size is suitable for research

There are two formulas to determine sample size. The size of the sample applied in the study are based on demand of factor analysis to discover EFA (Exploratory Factor Analysis) and regression:

Formula 1:

For exploring factor analysis EFA: Based on research by Hair, Anderson, Tatham and Black (1998) to refer to the planned sample size. Minimum sample size is 5x(number of observe variables). This is the appropriate sample size for studies using factor analysis (Comrey, 1973; Roger, 2006). n = 5 * m, note that m is the number of questions in all.

Formula 2:

For multivariate regression analysis: the minimum sample size to be achieved by the formula is n = 50 + 8 * m (m: number of independent variables) (Tabachnick and Fidell, 1996). Note m is the number of independent factors, rather than the question of independence.

Therefore, when choosing the number of samples must meet both of the above formula.

Overview of Exploratory Factor Analysis (EFA) and how to run EFA in SPSS

What is EFA

Before testing scientific theories it is necessary to evaluate the reliability and validity of the scale. Cronbach’s Alpha method used to evaluate the reliability of the scale. Exploratory Factor Analysis ( EFA) help us to check convergent value and discriminant value.
EFA have no dependent variable and independent variables, it only rely on correlations between variables together (interrelationships). EFA to shorten a set of K observed variables into a set F (F <K) the more significant factor. The basis of this reduction is based on a linear relationship of the factors with the original variables (observed variables).
The authors Mayers, LS, Gamst, G., AJ Guarino (2000) mentioned that: In the factor analysis, methods of extraction Principal Components Analysis, Varimax rotation method used is the most popular.
(Hair, Anderson et al. 1995a) categorized the Factor loadings as 0.30 = minimal, 0.40 = important, and 0.50 = practically.  If the Factor loadings is less than 0.30, then it should be reconsidered if Factor Analysis is proper approach to be used for the research (Hair, Anderson et al. 1995a; Tabachnick and Fidell 2001). If the correlation matrix is an identity matrix (there is no relationship among the items) (Kraiser 1958), EFA should not be applied.
The sampling adequacy can be assessed by examining the Kaiser- Meyer -Olkin (KMO) (Kaiser 1970). It ranges from 0 to 1, while according to (Hair, Anderson et al. 1995a; Tabachnick and Fidell 2001) , 0.50 considered suitable for FA . On the other hand, (Netemeyer, Bearden et al. 2003) stated that a KMO correlation above 0.60 – 0.70 is considered adequate for analyzing the EFA output.
Bartlett’s test of Sphericity (Bartlett 1950) provides a chi-square output that must be significant. It indicates the matrix is not an identity matrix and accordingly it should be significant (p<.05) for factor analysis to be suitable (Hair, Anderson et al. 1995a; Tabachnick and Fidell 2001).


Explore Factor Analysis must satisfy the following requirements:

    -Factor loading> 0.5
    -KMO ≤ 0.5 ≤ 1
    -Bartlett test the statistical significance (Sig. <0.05):
    -Percentage of variance in Extraction Sums of Squared Loadings > 50%: Show percentage variation of the observed variables.  

How to run EFA in SPSS.

Select on the menu: Analyze-> Data Reduction -> Factor.

Select all needed variables to the  Variables column on the right.

Click Descriptives, check KMO and Bartlett's test of sphericity

Click Rotation button, select Varimax

Click the Options button, select Sorted by size and select Suppress absolute values less than, type in .3

Then click OK, the results will show as follow, including Rotated Component Matrix table as follows:

There are 5 factor appear. That all about EFA

Contact Master of Business Administration Group at : https://facebook.com/hoidapspss or email: hotrospss@gmail.com or
We can help you:
– Market survey / processing / editing the survey data to run a factor analysis results converged, the regression analysis statistical significance.
– Consult models / questionnaires / Traning directly on regression analysis, factors, Cronbach alpha … in SPSS, and SEM model, CFA, AMOS

Links download SPSS, AMOS free for studying

MBA Group hotrospss@gmail.com introduce link download SPSS, AMOS, all download file free for study purpose, all with free installation guide, no need any crack.

SPSS


SPSS version 16.0 free download:

This section only 200Mb capacity, the installation does not need to enter key. Recommend using this version.
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The latest version of SPSS 23.0 2016

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SPSS version 20.0 for Mac OS version:

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AMOS Software


IBM AMOS 20:

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Spss Recode Into Different Variables

MBA Group hotrospss@gmail.com introduce how to use Recode function in SPSS, there are two types of recode: recode into the same variables and recode into new variables. This post is about recode into different variables. Create new variables without modify old variables.

Introduce


For example you have Age variables which value is 10, 11, 12, 13, 14……49, 50, 60. Now you want divide into 3 group, group 1 have age range 10-30, group 2 have age range 31-40, group 3 have age range 41-60. The result is new variables AgeGroup is created. This variable have three value: 1, 2, and 3.

How to do in SPSS

On the menu Transform> Recode Into Different Variables


Dialog show here:


Add variable to Variable Output
Place names and labels for the new variables, such as AgeGroup, then click the Change button to notify SPSS variables that you want to recode. Do not forget to remember the Change button if not recode your command will fail.
Click the button Old and new value
Open dialog box “Recode Into Next different Variables: Old and New Values” to determine the transition between the old value and the new value respectively.

In this dialog, in turn declare the value of the old (Old Value on the left), corresponding to the new value (New Value on the right), with the kind of old values can be recoded as follows
–    Value: each discrete old value have 1 new value
–    System-missing
–    System or user missing
–    Range
–    Ranging from the minimum value to a defined value is entered (Lowest through … Range)
–    Between a value is entered to determine the maximum value (Range … through Highest)

Each time you finish a pair identify the old value and the new value specified, the Add button will appear to light up, press the button to put the old value pairs declared and this new value into the box Old -> New : (do not forget to click the Add button after each determined before a pair of old values – new)
Click the on Continue button, open Data View, Variables name AgeGroup just created here.

So you finish use Recode to create new variables.

Contact Master of Business Administration Group at : https://facebook.com/hoidapspss or email: hotrospss@gmail.com or
We can help you:
– Market survey / processing / editing the survey data to run a factor analysis results converged, the regression analysis statistical significance.
– Consult models / questionnaires / Traning directly on regression analysis, factors, Cronbach alpha … in SPSS, and SEM model, CFA, AMOS

Macbook error Spss statistics installer cannot be opened because it is from an unidentified developer

Problem:

How to install SPSS on MacBook which errors “Spss statistics installer cannot be opened because it is from an unidentified developer”
When installing SPSS for Mac OS computers. Most cases you will not be installed due to the following error:
Spss statistics installer cannot be opened because it is from an unidentified developer.
Your security preferences allow installation of only apps from the Mac App Store and identified developers
As shown below:

Solution:

To solve it, go to Apple menu> System Preferences …> Security & Privacy> General tab


 Under the tab "Allow applications downloaded from:",this section has three types of settings
Allow applications downloaded from:
    +Mac App Store
    +Mac App Store and Identified developers
    +Anywhere
You should select ANYWHERE.
Then you can proceed to install SPSS on MAC easy.

 

Analysis Cronbach’s alpha reliability in SPSS

MBA Group hotrospss@gmail.com introduced articles on reliability analysis in SPSS. This article focuses on the introduction of theory and practice analyzing Cronbach's alpha reliability.

What is Cronbach’s alpha?

-Cronbach’s coefficient alpha provides an indication of the average correlation among all of the items that make up the scale. Values range from 0 to 1, with higher values indicating greater reliability. While different levels of reliability are required, depending on the nature and purpose of the scale, Nunnally (1978) recommends a minimum level of 0.7. Cronbach's alpha  measure of internal consistency, how closely related a set of items are in a group.
-Cronbach alpha values are dependent on the number of items in the scale. When there are a small number of items in the scale (fewer than ten), Cronbach alpha values can be quite small. In this situation it may be better to calculate and report the mean inter-item correlation for the items. Optimal mean inter-item correlation values range from .2 to .4 (as recommended by Briggs & Cheek, 1986). Cronbach's alpha is not a statistical test , but it is a coefficient of consistency.

The criteria used to evaluate the reliability scale:

– Remove the observed variables if “Corrected Item-Total Correlation” less than 0.3.
– The observed variables have “Corrected Item-Total Correlation” small (less than 0.3) will be removed and the scale is accepted as Alpha reliability coefficient greater than 0.7 .

Practice analyze Cronbach's alpha in SPSS

-Open SPSS data file.
-Select Analyze menu> Scale-> Reliability Analysis


Select the items in the same factor through the right column,

Then click on Statistic, check the box “Scale if the item deleted”. Then click Continue, then click OK
Cronbach’s alpha results will show as follows:


Conclusion: Cronbach's alpha of the scale was 0.869, the “Corrected Item-Total Correlation” in the scale are greater than 0.3 and no cases eliminate observed variables that can make the Cronbach's alpha of the scale this is greater than 0.869( last column). So, all the observed variables are accepted and will be used in subsequent factor analysis.


Contact Master of Business Administration Group at : https://facebook.com/hoidapspss or email: hotrospss@gmail.com or
We can help you:
– Market survey / processing / editing the survey data to run a factor analysis results converged, the regression analysis statistical significance.
– Consult models / questionnaires / Traning directly on regression analysis, factors, Cronbach alpha … in SPSS, and SEM model, CFA, AMOS

 

One-way anova in SPSS

What is One-way anova

One-way analysis of variance is similar to t-test, but is used when you have two or more groups and you wish to compare their mean scores on a continuous variable. It is called one-way because you are looking at the impact of only one independent variable on your dependent variable. A one-way analysis of variance (ANOVA) will let you know whether your groups differ, but it won’t tell you where the significant difference is, but you can conduct post-hoc comparisons to find out which groups are significantly different from one another.

Analysis of variance (ANOVA) is a collection of statistical models used to analyze the differences among group means and their associated procedures (such as "variation" among and between groups)

Oneway Anova used to test the hypothesis of equal average of the sample group with the ability to make the mistake of only 5%.

Example

Example: Analysis of the difference between customer attributes (gender, age, occupation, income …) for 1 certain issues (usually chosen as dependent factors, e.g.  Satisfaction or Loyal).

Assumptions

Some assumptions when analyzing One-way Anova:

– The comparison group must be independent and are selected at random.

– The comparison group to have a normal distribution or sample size should be large enough to be seen as asymptotic normal distribution.

– Variance of the comparison group must be equal.

Note: if the overall assumption normally distributed with equal variances does not meet the test, then you can use a non-parametric test Kruskal-Wallis replace ANOVA.

Levene test

Ho: "The variance equal"

Sig <0.05: reject Ho

Sig> = 0:05: accepting Ho -> eligible for further analysis Anova

ANOVA test

Ho: "Average of differece group equal"

Sig> 0.05: reject Ho -> unqualified to confirm no difference…

Sig <= 0:05: accepting Ho -> confirmed eligible to differ…

When there are differences, they may further analysis to find out the difference between the groups how to observe.

How to using anova with spss:

For example, we answer the question: are there any difference of Satisfaction of customer between 3 Age group of them. 

Click Analyze menu -> Compare Means -> One-Way ANOVA

Move variable Age to Factor box

Move variable Satisfaction to Dependent list box

Click on the Options, chose “Homegeneity of variance test” to test the homogeneity of variance.

Click OK, SPSS show the result

Two table explain here:

Table Homegeneity:

Ho: "The variance equal"

Sig <0.05: reject Ho

Sig> = 0:05: accepting Ho -> eligible for further analysis Anova

Tables Anova :

Ho: "Average of differece group equal"

Sig> 0.05: reject Ho -> unqualified to confirm no difference…

Sig <= 0:05: accepting Ho -> confirmed eligible to differ…

 

Contact Master of Business Administration Group at : https://facebook.com/hoidapspss or email: hotrospss@gmail.com or
We can help you:
– Market survey / processing / editing the survey data to run a factor analysis results converged, the regression analysis statistical significance.
– Consult models / questionnaires / Traning directly on regression analysis, factors, Cronbach alpha … in SPSS, and SEM model, CFA, AMOS