🍎Reliability and validity are two key concepts in research methodology, particularly in the context of measuring instruments, such as surveys, tests, or questionnaires. They are used to assess the quality and rigor of a research study or measurement tool.

✍️Reliability

👍Reliability refers to the consistency and stability of a measure. A reliable instrument produces similar results under consistent conditions.

Types of Reliability:

    👍Test-Retest Reliability: Measures the consistency of results when the same test is administered to the same group at different times.

   👍 Inter-Rater Reliability: Assesses the degree of agreement between different raters or observers.

    👍Internal Consistency Reliability: Evaluates the consistency of items within a test (e.g., Cronbach's Alpha).

    👍Parallel-Forms Reliability: Measures the correlation between two equivalent versions of a test.

How to Test Reliability:

    🌱Use statistical measures like Cronbach's Alpha (for internal consistency), Pearson's correlation coefficient (for test-retest reliability), or Cohen's Kappa (for inter-rater reliability).

    🌱A reliability coefficient of 0.7 or higher is generally considered acceptable.

🌎Validity

👉🏿Validity refers to the extent to which a tool measures what it is intended to measure. A valid instrument accurately reflects the concept it is supposed to measure.

Types of Validity:

         🤙Content Validity: Ensures the test covers all aspects of the concept being measured.

         🤙Construct Validity: Assesses whether the test measures the theoretical construct it claims to measure.

         🤙 Convergent Validity: Measures how closely the test is related to other tests that measure the same
                 construct.

         🤙 Discriminant Validity: Ensures the test is not related to measures of different constructs.

         🤙 Criterion Validity: Evaluates how well the test predicts or correlates with a criterion.

        🤙  Concurrent Validity: Measures how well the test correlates with a criterion measured simultaneously.

         🤙 Predictive Validity: Assesses how well the test predicts future outcomes.

How to Test Validity:

   ✏️ Use expert reviews for content validity.

    ✏️ Conduct factor analysis or correlation studies for construct validity.

    ✏️ Compare the test results with an established criterion for criterion validity.

🍏Key Differences:

    💎Reliability is about consistency (does the test produce stable results?).

    💎Validity is about accuracy (does the test measure what it claims to measure?).

If you need support related to:
                 ⌚️Assignment / አሳይመንት
                 ⌚️Research / ሪሰርች
                 ⌚️Proposal / ፕሮፖዛል
                 ⌚️Term Paper /  ተረም ፔፐር
                 ⌚️Case study/ ኬዝ ስተዲ
                 ⌚️Article Review
                 ⌚️Mini research
                 ⌚️Business plan

🥭Training on basic statistical software's

    🗝GIS
               🗝  STATA
                                 🗝 SPSS
                                               🗝 R
🪜And other related software's...... also  any other Questions please contact us via
☎️
+251912688642
+251912688642

Telegram Account
https://t.me/Research100stock
https://t.me/Research100stock
👇👇👇👇👇👇👇👇
join and learn more
https://t.me/star_research_c/strong>onsultancy
?" target="_blank" rel="nofollow">strong>/strong>onsultancy
??" target="_blank" rel="nofollow">="nofollow">cy
?trong>?🙏??🙏🙏🙏🙏🙏

RESEARCH PROPOSAL WRITING TEMPLATE

Title:

Your title should answer the question: What is your research about? It should be a short,
concise phrase. Narrow down to the specific area to manage in terms of resources

CHAPTER ONE: INTRODUCTION

1.1 Background of the study

✍️ The background explains in detail several components of the experiment that must be
included in any proposal.

✍️ After reading the introduction, the reader should conclude why the researcher is conducting the research and how this research will affect the
academic community and society at large.

✍️ It is sufficient to grab the reader’s attention, introduce the topic at hand, and provide a
brief definition of the theory from which the study is based.

✍️ The background section
outlines why you chose your topic.

✍️ Background section should include:

* what prompted your interest in the topic
*  relevance to previous research (literature)
* what your research will contribute to the research and the field.

1.2 Statement of the problem

The “Statement of the Problem” is an imperative part of the proposal.

That is, in order for
research to be conducted; one must notice a problem in the existing literature that has
not been previously addressed.

For this section, the following questions should be answered:

• Why does this research study need to be conducted?
• What specific issues does this study raise that have not been observed in other literature pertaining to the topic?

Answering these questions will allow readers to understand why this particular study is
important and how the study will attempt to answer new, never-before asked questions.


In this section state how the problem is very serious.

Refer literature Quote research
conducted previously.

Include policy issues with regards of your title and your personal experiences and observation.


1.3 Basic Research questions
• What are the research questions you are trying to find the answer to?
• Frame 3-5 basic research questions

1.4 Objective of the research.
1.4.1 General objective
1.4.2 Specific objectives
1.5 Delimitation of the study

Delimitation means scope of your study. Indicate the areas you are going to study and
the issues to be included.

1.6 Limitation of the study
In this section explain the problems that you will face while conducting the research.

1.7 Organization of the study
Indicate in order the chapters and their detail information under each chapter.

1.8 Definition of key terms
It is essential that one defines the central ideas or concepts of the research study.

Therefore, carefully define each concept/variable that will be used in the study, citing other research studies as much as needed.



For college and University students (Degree, Masters and PhD programs), if you need support on academic tasks such as:


# title selection
# title description and concept note
# research  proposal
# Research for graduation
# Mini research
# business  plan
# project  proposals
# Data analysis (SPSS, STATA, MATLAB, R, AMOS...) and others,

contact on
0920256875


Telegram Account

https://t.me/Success_Research_Consultant

Multiple regression assumptions diagnostic

How can we test regression assumptions? Before performing regression analysis, it is important to check regression assumptions like multicollinerity, normality, linearity, autocorrelation, and homoskasticity.

1. Normally distribution
It refers to the normal distribution of residuals or error terms.
It can be tested using either graphical methods by histogram, predicted probability (p-p) plots and plotted points or statistical methods by using kurtosis and skewness values.
Statistical methods are better than graphical methods.
A data is normally distributed when the skewness and kurtosis values are between -2 and 2.

2. Multicollinearity
It refers to the relationship between independent variables or predictors.
That is, predictors should not be highly correlated.
It can be checked by using either correlation coefficient between independent variables, using Tolerance and variance inflation factors (VIF) values of variables or eigenvalue values.
That is, there is no multicollinearity among independent variables (multicollinearity assumption is not violated) if the correlation coefficient is less than 0.8 or tolerance is above 0.1 and VIF is below 5.

3. Linearity
It refers to the linear relationship between independent and outcome variables.
In a scatter plot, linearity can be checked if points conform to a diagonal fitted line.
If the assumption of multicollinearity and normality are not violated, don't worry about linearity.

4. Homoscedasticity
Homoscedasticity refers to the constant variance of error terms.
Its opposite is heteroscedasticity.
If it fails (heteroscedastic), additional predictors are required to explain results, or transform the data using logarithm, square root, ....
Homoscedastic if residuals evenly distributed between -2 and 2.

5. Autocorrelation
It refers to independence of observation.
The independent variable is said to be autocorrelated when the current value of Y is dependent on its previous value.
It can be checked by using Durbin-Watson test (DW).
If DW = 2 or approaches to 2, there is no autocorrelation.

እናማክራለን

For college and University students (Degree, Masters and PhD programs), if you need support on academic tasks such as:
# article reviews
# term papers
#  Case studies
# project work
Research related tasks
# title selection
# title description and concept note
# research  proposal
# Research for graduation
# Mini research
# business  plan
# project  proposals
# Data analysis (SPSS, STATA, ...) and others,

Training on basic statistical software's

    🗝GIS
               🗝  STATA
                                 🗝 SPSS
                                       🗝 R
                                             🗝 XLSTAT
🪜And other related software's...... also  any other Questions please contact us via
☎️
+251912688642
+251912688642

Telegram Account
https://t.me/Research100stock
https://t.me/Research100stock
👇👇👇👇👇👇👇👇
join and learn more
https://t.me/star_research_consultancy
🙏🙏🙏🙏🙏🙏🙏🙏🙏