When and How to Use One Sample t-test in SPSS?

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When and How to Use One Sample t-test in SPSS

Meaning of T-test

The one-sample t-test is used to determine whether a sample comes from a population with a specific mean. This population mean is not always known, but is sometimes hypothesized.

For example

you want to show that the new cultivation method for Carrot helps to grow carrot better than the mean growth of carrot with other methods.

When to do T-test

When we plan to analyse our data using a one-sample t-test, this process involves checking to make sure that the data we want to analyse can actually be analysed using a one-sample t-test.

1. The data must be Quantitative. Your dependent variable should be measured at the interval or ratio level.


2. The data are independent (i.e., not correlated/related), which means that there is no relationship between the observations.


3. Your dependent variable should be approximately normally distributed.


4. No outliers.

How to Test using T-test using SPSS

Following are the steps to use t-test with spss.

1. Click Analyze > Compare Means > One-Sample T Test


2. You will be presented with the One-Sample T Test dialogue box. Transfer the dependent variable into the Test Variable(s) box.


3. In the test value box, give the mean value (assumed/expected). In case of our carrot example we give 8 inches in the mean value.


4. Click the Continue button and then OK button.

Inference of T test

In our example, p < .05 (it is p = .022). Therefore, it can be concluded that the population means are statistically significantly different. That is the growth of carrot is different than the other methods which normally grows 8 inches.

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Content Validity - How and When?

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Content-Validity-How-and-When?

Content-Validity – Meaning

The idea of content validity is about the questions administered in a survey, questionnaire, usability test, or outcome of relevant content from focus group.

For example

Specially made Grape juice is offered to expert tasters to test whether it is smackingly refreshing. There are list of qualities in the juice to be smackingly refreshing. If those qualities are present in those then the content of the Grape Juice is “smackingly refreshing” and the content of the grape juice serves its purpose.

Content Validity – How and When?

The following questions will be answered in this section.

1. How is content validity measured?


2. How do researchers know if an assessment has content validity?


3. When this validity needs to be tested?

Content validity is most often measured by relying on the knowledge of people who are familiar with the construct being measured. These subject-matter experts are usually provided with access to the measurement tool and are asked to provide feedback on how well each question measures the construct in question. Their feedback is then analyzed, and informed decisions can be made about the effectiveness of each question. This test of content validity mostly applies to survey and in some types of science experiments too.

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Criterion validity Using Correlation

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Criterion Validity using Correlation

Meaning of Criterion Validity

Criterion validity (or criterion-related validity) measures how well one measure predicts an outcome for another measure. A test has this type of validity if it is useful for predicting performance or behavior in another situation (past, present, or future). For example:

• A regular juice taker monitors to test the body-healing effects of Pomegranates. If this test accurately predicts how well the Pomegranates heals the body, then the test is said to have criterion validity.


• A graduate student takes the GRE. The GRE has been shown as an effective tool (i.e. it has criterion validity) for predicting how well a student will perform in graduate studies.

The first measure (in the above examples, the body-healing effects test and the GRE) is sometimes called the predictor variable or the estimator. The second measure is called the criterion variable as long as the measure is known to be a valid tool for predicting outcomes.

One major problem with criterion validity, especially when used in the social sciences, is that relevant criterion variables can be hard to come by.

Types of Criterion Validity

The three types are:

• Predictive Validity: if the test accurately predicts what it is supposed to predict. For example, the SAT exhibits predictive validity for performance in college. It can also refer to when scores from the predictor measure are taken first and then the criterion data is collected later.


• Concurrent Validity: when the predictor and criterion data are collected at the same time. It can also refer to when a test replaces another test (i.e. because it’s cheaper). For example, a written driver’s test replaces an in-person test with an instructor.


• Postdictive validity: if the test is a valid measure of something that happened before. For example, does a test for adult memories of childhood events work?

More Examples of Criterion Validity

As an example of criterion validity, imagine science reasoning essay examination that was developed to admit students into a science course at the university. Criterion validity for this new science essay exam would consist of the following:

• Convergent Validity: The new science exam should have high correlations with other science exams, particularly well established science exams.


• Divergent Validity: The new science exam should have low correlations with measures of writing ability because the exam should only measure science reasoning, not writing ability.


• Predictive Validity: The new science exam should have high correlations with future grades in science courses because the purpose of the test is to determine who will do well in the science program at the university.

Criterion validity evidence for the new science test as in the image:

Therefore, to provide evidence of criterion validity, administer the instrument with other instruments measuring variables that are similar (and are predicted to have high correlations) and other instruments measuring variables that are different (and are predicted to have low correlations). The same participants should complete all instruments, and then calculate the correlations between assessments. For evidence of predictive validity, give a sample the instrument at Time 1. Then wait for time to pass (probably at least a year) and give the exact same sample an instrument of a variable that your instrument should predict. Then calculate the correlation between your instrument and the predictive criterion.

Verdict: High Positive Correlation is a Measure of Criterion Validity. Values above 0.5 is said to be highly correlated.

Visit: Saru publications – Online Journals

1. 
   

   INTERNATIONAL JOURNAL OF SCIENCE & ENGINEERING RESEARCH – IJOSER

   
   


2. 
   

   INTERNATIONAL JOURNAL OF COMMERCE, ECONOMICS & MANAGEMENT – IJOCEM

   
   


3. 
   

   INTERNATIONAL JOURNAL OF SERVICE QUALITY – IJSQ

   
   


4. 
   

   INTERNATIONAL JOURNAL OF WEB COMPONENTS, DATA & ANALYTICS – IJWCDA

   
   

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Cronbach's alpha to Measure Internal Consistency/Reliability using SPSS

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Cronbach’s alpha to measure internal consistency/Reliability using SPSS

Cronbach’s alpha-Meaning 

When you have multiple Likert questions in a survey/questionnaire that form a scale and you wish to determine if the scale is reliable. Cronbach’s alpha is a measure of internal consistency, that is, how closely related a set of items are as a group. It is considered to be a measure of scale reliability. A “high” value for alpha does not imply that the measure is unidimensional.

Cronbach’s alpha to Measure Internal Consistency/Reliability using SPSS

A) Reliability of a Single Construct

Step1-Keep the data file opened and Click Analyze > Scale > Reliability Analysis

Step2-Reliability Analysis dialogue box will open,

Step3-Choose the variables/items that belong to a specific construct to the “items box” on the right.

Step4:The model is Alpha. Click “Statistics” Button. Select the Item, Scale and Scale if item deleted options in the “Descriptives for” , and the Correlations option in the “Inter-Item” . Click the “Continue” button. This will return you to the Reliability Analysis dialogue box. Click the “OK” button to generate the output.

OUTPUT

From our example, we can see that Cronbach’s alpha is 0.805, which indicates a high level of internal consistency for our scale with this specific sample

This column presents the value that Cronbach’s alpha would be greater if that particular item was deleted from the scale. We can see that “Qu8” has 0.128 and if this item is deleted from the construct then it would increase the alpha to 0.823 as seen in the “Corrected Item-Total Correlation” column for this item.

Verdict: It is advisable to remove the item “Qu8” and go ahead with the research but if that item is important the researcher can retain the item since the overall alpha score is greater than 0.7  in reliability statistics table. If the values are less than 0.7 then the reliability is less but still the research can proceed if he feels it is greater than 0.5 because the data is qualitative in nature.

B) Reliability of ALL the Constructs

To find the reliability of all the items in all the construct. Follow the steps as in “A” but select all the items of all the constructs to the “items window”.

Verdict: it is same as in heading “A”.

Reference

https://statistics.laerd.com/spss-tutorials/cronbachs-alpha-using-spss-statistics.php

Visit: Saru Publications – Online Journals

List of Journals:

1. 
   

   INTERNATIONAL JOURNAL OF SCIENCE & ENGINEERING RESEARCH – IJOSER

   
   


2. 
   

   INTERNATIONAL JOURNAL OF COMMERCE, ECONOMICS & MANAGEMENT – IJOCEM

   
   


3. 
   

   INTERNATIONAL JOURNAL OF SERVICE QUALITY – IJSQ

   
   


4. 
   

   INTERNATIONAL JOURNAL OF WEB COMPONENTS, DATA & ANALYTICS – IJWCDA

   
   

 

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The Same Refrigerator that Hardens Ice Softens Jelly - Thought

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The same refrigerator that hardens ice softens jelly

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Convergent & Discriminant Validity using Correlation Coefficient

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Convergent and Discriminant Validity

Meaning of Convergent and Discriminant Validity

Campbell and Fiske (1959) proposed two aspects to asses the construct validity of a test:

1. Convergent validity: It is the degree of confidence we have that a trait is well measured by its indicators. ie. The statements measures the construct well.


2. Discriminant validity: It is the degree to which measures of different traits are unrelated. ie. Each construct measures a different attribute of the problem.

In structural equation modelling, Confirmatory Factor Analysis has been usually used to asses construct validity (Jöreskog, 1969).

Convergent & Discriminant Validity using Correlation Coefficient

To estimate the degree to which any two measures are related to each other we typically use the correlation coefficient. That is, we look at the patterns of intercorrelations among our measures. Correlations between theoretically similar measures should be “high” while correlations between theoretically dissimilar measures should be “low”. Use factor analysis to make correlation matrix in SPSS OR use Analyse»correlate»Bi-variate.

Convergent Validity

To confirm convergent validity, the results should show that there exist positive correlation between the statements that measures a construct.

Example of Convergent validity

We have four items/statements of “Self esteem” as “Respect, Care, Sincerity and Emotional Stability”. These four items should have a positive correlation between them.

Convergent and Discriminant Validity1

Discriminant Validity

To confirm discriminant validity, the results of correlation should show that there exist less or no correlation between the constructs of the problem studied.

Discriminant Validity2

we have two constructs named as “self esteem” and “locus of control”.  Self Esteem and locus of control has two items each (SE1,SE2, LOC1, LOC2). The correlation between the items in Self esteem and Locus of Control is very low ie near to no. As we know the value of correlation ranges between -1 to +1. Zero means no correlation. All the values in the above example are positive and close to zero. This shows that the construct “Self esteem” is different from the construct “locus of control”, hence discriminant validity proved.

Reference:

https://www.socialresearchmethods.net/kb/convdisc.php

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List of Journals:

1. 
   

   INTERNATIONAL JOURNAL OF SCIENCE & ENGINEERING RESEARCH – IJOSER

   
   


2. 
   

   INTERNATIONAL JOURNAL OF COMMERCE, ECONOMICS & MANAGEMENT – IJOCEM

   
   


3. 
   

   INTERNATIONAL JOURNAL OF SERVICE QUALITY – IJSQ

   
   


4. 
   

   INTERNATIONAL JOURNAL OF WEB COMPONENTS, DATA & ANALYTICS – IJWCDA

   
   

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Discriminant Validity Using SmartPLS

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Discriminant Validity

Validity Meaning

Validity is the extent to which a concept, conclusion or measurement is well-founded and corresponds accurately to the real world. In short it is to know whether it serves the purpose.

Example:

The purpose of grapes should serve its purpose by blocking the body’s production of vitamin K and making the blood thinner. when grapes fails to serve this purpose then it looses validity. Similarly, when the constructs of the questionnaire fail to measure what it has to measure, then the questionnaire looses validity. so this test is essential for a research at the initial level and this also gives the reliability of the construct.

Discriminant Validity Using SmartPLS

Discriminant validity is the degree to which any single construct is different from the other constructs in the model (Carmines and Zeller, 1979). Validity is measured with the following,

1. Content validity


2. Criterion validity


3. Construct validity – It has two, named as a) Convergent and b) Discriminant validity

Discriminant validity can also be measured with SPSS, AMOS etc. Now in this article we are going to be clear that Discriminant validity can be better measured with SmartPLS software. This company also shares its lower version at free of cost. The key is sent to us by email.

Output table of Discriminant Analysis

Discriminant Analysis: Table1

From table1, it is clear that the cross loading of the respective factor is greater than that factor’s AVE. For Example The cross loading of “Customization” is 0.837 which is greater than its respective AVE of 0.700. Since the cross loading  is greater for all the factors than the AVE of the respective construct, discriminant validity is confirmed.

More details about SmartPLS from the official website

When running the PLS and PLSc algorithm in SmartPLS, the results report includes discriminant validity assessment outcomes, in the section “Quality Criteria”. The following results are provided:

• the Fornell-Larcker criterion,


• cross-loadings, and


• the HTMT criterion results.

Recommend using the HTMT criterion to assess discriminant validity. If the HTMT value is below 0.90, discriminant validity has been established between two reflective constructs.

If you like to obtain the HTMT_Inference results, you need to run the bootstrapping routine. When starting the bootstrapping routine, it is important that you select the option “Complete Bootstrapping”. Then, in the bootstrapping results report, you find the bootstrapped HTMT criterion results in the section “Quality Criteria”.

The table of Discriminant Analysis

Please note: In SmartPLS 3.2.1 and later version, the HTMT criterion computation differs from the equation given by Henseler, Ringle and Sarstedt (2015). Instead of using the correlations between indicators, SmartPLS uses the absolute value of the correlation between indicators.

For example, when instead of using 0.1, 0.2 and -0.3, which results in an average correlation of 0 an causes problems in the original HTMT equation, SmartPLS uses 0.1, 0.2 and 0.3, which results in an average correlation of 0.2. In consequence, the HTMT criterion is normed between 0 and 1 in SmartPLS and no issues result from negative correlations.

References

Henseler, J., Ringle, C. M., and Sarstedt, M. 2015. A New Criterion for Assessing Discriminant Validity in Variance-based Structural Equation Modeling. Journal of the Academy of Marketing Science, 43(1): 115-135.

Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. 2017. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 2nd. Ed., Sage: Thousand Oaks.

https://www.smartpls.de/documentation/discriminant-validity-assessment

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1. INTERNATIONAL JOURNAL OF SCIENCE & ENGINEERING RESEARCH – IJOSER


2. INTERNATIONAL JOURNAL OF COMMERCE, ECONOMICS & MANAGEMENT – IJOCEM


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4. INTERNATIONAL JOURNAL OF WEB COMPONENTS, DATA & ANALYTICS – IJWCDA

 

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Interpretation, When & How to do Chi-Square Test

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Statistics_are in the eye of the Manipulator

Introduction

The Chi-Square (X²) statistic may be used to determine if two categorical (nominal or ordinal variables with less than 5 rankings) variables are related.  For example, you may hypothesize that gender influences a person’s political party identification. You can determine some of this information by looking at the cross tabulation and comparing the percentages of men and women for each party identification.  This statistic involves comparing your actual results with the results you would expect to have if there were NO difference between women and men in terms of their political party affiliation.

Assumptions

When you choose to analyse your data using a chi-square test for independence, you need to make sure that the data you want to analyse “passes” two assumptions.  These two assumptions are:

• Assumption #1: Your two variables should be measured at an ordinal or nominal level (i.e., categorical data). You can learn more about ordinal and nominal variables in our article: Types of Variable.


• Assumption #2: Your two variable should consist of two or more categorical, independent groups. Example independent variables that meet this criterion include gender (2 groups: Males and Females), ethnicity (e.g., 3 groups: Caucasian, African American and Hispanic), physical activity level (e.g., 4 groups: sedentary, low, moderate and high), profession (e.g., 5 groups: surgeon, doctor, nurse, dentist, therapist), and so forth.


• Assumption #3: One of the requirements for Chi-Square is that each and every cell has a frequency of 5 or greater.

Chi-Square Test Procedure in SPSS 

We show you how to interpret the results from your chi-square test for independence.

• Click Analyze > Descriptives Statistics > Crosstabs… on the top menu


• You will be presented with the following Crosstabs dialogue box:


• Transfer one of the variables into the Row(s): box and the other variable into the Column(s): box. In our example, we will transfer the Gender variable into the Row(s): box and Preferred_Learning_Medium into the Column(s): box. There are two ways to do this. You can either: (1) highlight the variable with your mouse and then use the relevant buttons to transfer the variables; or (2) drag-and-drop the variables. How do you know which variable goes in the row or column box? There is no right or wrong way. It will depend on how you want to present your data.
  

  If you want to display clustered bar charts (recommended), make sure that Display clustered bar charts checkbox is ticked.

  
  

   



• Click on the button. You will be presented with the following Crosstabs: Statistics dialogue box:


• Select the Chi-square and Phi and Cramer’s V options (for Nominal data) or Somers’d for Ordinal data.


• Click the button.


• Click the button. You will be presented with the following Crosstabs: Cell Display dialogue box:
  
  Published with written permission from SPSS Statistics, IBM Corporation.
  
  


• Select Observed from the –Counts– area, and Row, Column and Total from the –Percentages– area, check Observed.


• Once you have made your choice, click the button.


• 
  

  Click the button to generate your output.

  
  

Output

You will be presented with some tables in the Output Viewer under the title “Crosstabs”. The tables of note are presented below:

The Crosstabulation Table (Gender*Preferred Learning Medium Crosstabulation)

This table allows us to understand that both males and females prefer to learn using online materials versus books.

The Chi-Square Tests Table

When reading this table we are interested in the results of the “Pearson Chi-Square” row. We can see here that χ(1) = 0.487, p = .485. This tells us that there is no statistically significant association between Gender and Preferred Learning Medium; that is, both Males and Females equally prefer online learning versus books.

In short, Now look at the “Pearson Chi-Square Asymp. Sig (2 sided)”*. Since Chi-Square is testing the null hypothesis, the Sig value must be .05 or less for there to be a significant statistical for the relationship between the variables. In this example, the Sig. is .485, so there is no statical significance.

Look at the “Continuity Correction” line below. This will appear if you are examing variables that each have 2 possible responses. The corrected significance is .216; therefore, this also suggests that there is statistical significance between the relationship of the two variables.

The Symmetric Measures Table

The most commonly used statistic is the Phi coefficient, which ranges from 0 to 1. Higher values indicate a stronger correlation between the two variables. Phi and Cramer’s V are both tests of the strength of association. We can see that the strength of association between the variables is very weak.

Reference:

1. https://statistics.laerd.com/spss-tutorials/chi-square-test-for-association-using-spss-statistics.php


2. http://latrobe.libguides.com/SPSS/chi-square

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List of Journals:

1). International Journal Of Science & Engineering Research – IJOSER

2). International Journal Of Commerce, Economics & Management – IJOCEM

3). International Journal Of Service Quality – IJSQ

4). International Journal of Web Components, Data & Analytics – IJWCDA

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Interpretation and Steps to Test Heteroskedasticity

Interpretation and Steps to Test Heteroskedasticity

Heteroskedasticity is useful to examine whether there is a difference in the residual variance of the observation period to another period of observation. A Good regression model is not the case heteroscedasticity problem.

Statistical methods to test Heteroskedasticity

Many statistical methods are there to determine whether a model is free from the problem of heteroscedasticity or not, like

1. White Test,


2. Test Park,


3. Test Glejser.

SPSS Test will introduce one of heteroscedasticity test that can be applied in SPSS, namely

Test Glejser. Glejser test conducted by regressing absolud residual value of the independent

variable with regression equation is: Ut = A + B Xt + vi

Interpretation of Heteroskedasticity Test with Test Glejser (SPSS)

1. If the value Sig. > 0.05, then there is no problem of heteroscedasticity


2. If the value Sig. <0.05, then there is a problem of heteroscedasticity

Based on Output from the above table, Coefficients obtained value of Sig. Competence variable of 0.834, and the Sig. Motivation variable of 0.348, meaning that the value of the variable sig Competence and Motivation > 0.05, it can be concluded that there is no heteroscedasticity problem.

http://www.spsstests.com/2015/03/test-heteroskedasticity-glejser-using.html

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