The stages that a software undergoes before its release- Software release cycle

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A software undergoes various stages of development before it is ready for public release. The software release must be bug free and stable. The software that is released to the public is stable version which usually has no or few bugs.

The first stage of software development is the Pre-Alpha stage where the primary functions are implemented and software gets ready for initial testing.

Then it moves into the alpha stage where it is subjected to several tests. This alpha version is released only to a very small group(usually the developers) as it is quite unstable and buggy in most cases of softwares. If a release is an open one then it is released publically. When the alpha version of the software gets all the features which was planned, it then enters the beta stage.

The beta software contains bugs which are either known or unknown. It is released to a further bigger audience for testing. Some firmwares release it publically for users to test and send reports on the bugs they encounter. When the bugs encountered are fixed the software enters the gamma/delta stage which is a quite stable. This stable version is released publically to few users to finalize that there are no potential bugs in the software.

As soon as this stage is passed the software is ready for release which is known stable release. This release is signed by the developer to verify authenticity of the user. Then copies of the software are made and send for customer consumption through market or through internet. The same procedure is followed in giving that software release its update to include more features or remove bugs.

These are the stages that a software undergoes before its release and in each stage it improves with decrease in bugs.

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Computer Architecture and its types

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Computer Architecture

Computer Architecture comprises of various set of rules and functions according to which the computer works.The first Computer Architecture was proposed by Charles Babbage and Ada Lovelace which describes the analytical engine.

There are several types of architecture classified on different basis. The three main categories are:

1. Instruction Set Architecture or ISA. The ISA defines the machine code that a processor reads and acts upon as well as the word size, memory address, registers, and data.


2. Microarchitecture, or computer organization describes how a particular processor will implement the ISA.


3. System Design includes all of the other hardware components within a computing system. These include:
   
   1. Data processing other than the CPU, such as Direct Memory Access (DMA)
   
   
   2. Other issues such as virtualization, multitasking, and software features
   
   

There are other notable types are Macro Architecture,Pin Architecture,Programmer Visible Macro architecture.

Computer Architecture is also classified into open and closed architecture.

1. Open Architecture:

Open architecture is a type of computer architecture or software architecture that is designed to make adding, upgrading and swapping components easy.

1. Closed Architecture:

Closed architecture is a type of computer architecture or software architecture in which adding, upgrading and swapping components is not easy.

The purpose that all computer architectures must serve is optimisation of all processes, reliability and makes sure that other factors don’t affect its performance.

A good computer architecture is designed such that factors that affect its purpose are well optimised since it’s difficult to construct an architecture for all applications.Some of the factors taken in consideration are latency,cost efficiency, power, performance, memory capacity, features,expandibility. These are optimised based on what is needed the most by trading off one factor for another. We increase the efficiency of something by decreasing the efficiency of anotherthing. So according to the demands the architecture is designed.

Author: A.Sarukrishna

E-mail: a.sarukrishna@gmail.com

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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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