🍎Panel logistic regression🍏
👍Panel logistic regression, also known as fixed-effects logistic regression or conditional logistic regression, is a statistical method used to analyze panel or longitudinal data where the dependent variable is binary (i.e., it takes on two possible values) and there are repeated observations on the same individuals over time.
👍Panel data refers to a dataset that contains observations on the same individuals or entities over multiple time periods. Examples of panel data include tracking individuals' health outcomes over time, analyzing financial data of companies over several years, or examining the voting behavior of individuals across multiple elections.
👍Logistic regression, on the other hand, is a statistical model used to estimate the probability of a binary outcome based on one or more independent variables. It is commonly used when the dependent variable is dichotomous, such as predicting whether a customer will churn or not, or whether a patient will respond to a particular treatment or not.
👍Panel logistic regression extends logistic regression to account for the panel structure of the data. It incorporates fixed effects, which capture individual-specific heterogeneity that is constant over time but may affect the outcome variable. By including fixed effects, panel logistic regression controls for unobserved individual-level characteristics that may be correlated with the dependent variable.
👍The fixed effects in panel logistic regression are typically included as dummy variables for each individual in the panel. These fixed effects capture the individual-specific intercepts and allow for the estimation of time-varying effects of the independent variables on the dependent variable.
👍Panel logistic regression can provide insights into how individual characteristics and time-varying factors influence the probability of the binary outcome. It is often used in various fields, including economics, social sciences, and public health, to analyze longitudinal data and understand the determinants of binary outcomes over time.
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👍Panel logistic regression, also known as fixed-effects logistic regression or conditional logistic regression, is a statistical method used to analyze panel or longitudinal data where the dependent variable is binary (i.e., it takes on two possible values) and there are repeated observations on the same individuals over time.
👍Panel data refers to a dataset that contains observations on the same individuals or entities over multiple time periods. Examples of panel data include tracking individuals' health outcomes over time, analyzing financial data of companies over several years, or examining the voting behavior of individuals across multiple elections.
👍Logistic regression, on the other hand, is a statistical model used to estimate the probability of a binary outcome based on one or more independent variables. It is commonly used when the dependent variable is dichotomous, such as predicting whether a customer will churn or not, or whether a patient will respond to a particular treatment or not.
👍Panel logistic regression extends logistic regression to account for the panel structure of the data. It incorporates fixed effects, which capture individual-specific heterogeneity that is constant over time but may affect the outcome variable. By including fixed effects, panel logistic regression controls for unobserved individual-level characteristics that may be correlated with the dependent variable.
👍The fixed effects in panel logistic regression are typically included as dummy variables for each individual in the panel. These fixed effects capture the individual-specific intercepts and allow for the estimation of time-varying effects of the independent variables on the dependent variable.
👍Panel logistic regression can provide insights into how individual characteristics and time-varying factors influence the probability of the binary outcome. It is often used in various fields, including economics, social sciences, and public health, to analyze longitudinal data and understand the determinants of binary outcomes over time.
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Assumptions of logistics regression
The main assumptions of logistic regressions are the following:
👉 For Binary dependent variable, the outcome variable must be binary (e.g., 0/1, Yes/No, Success/Failure).
👉 Independence of observations: Each observation should be independent of the others.
👉 Linearity of independent variables with the logit: The relationship between continuous predictors and the log-odds (logit) of the outcome must be linear. This does not mean the predictors must be linearly related to the outcome itself.
👉 multicollinearity among independent variables: Independent variables should not be highly correlated with each other. High multicollinearity inflates standard errors and weakens inference.
👉 Large sample size: Logistic regression requires a sufficiently large sample for stable estimates.
👉 Absence of strongly influential outliers: Extreme values can unduly influence model estimates.
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The main assumptions of logistic regressions are the following:
👉 For Binary dependent variable, the outcome variable must be binary (e.g., 0/1, Yes/No, Success/Failure).
👉 Independence of observations: Each observation should be independent of the others.
👉 Linearity of independent variables with the logit: The relationship between continuous predictors and the log-odds (logit) of the outcome must be linear. This does not mean the predictors must be linearly related to the outcome itself.
👉 multicollinearity among independent variables: Independent variables should not be highly correlated with each other. High multicollinearity inflates standard errors and weakens inference.
👉 Large sample size: Logistic regression requires a sufficiently large sample for stable estimates.
👉 Absence of strongly influential outliers: Extreme values can unduly influence model estimates.
Consultancy on academic tasks such as:
✍️ Research title selection and concept not description;
✍️ Research proposal draft;
✍️ Thesis or research evaluations;
✍️ Data analysis methods including SPSS, STATA, E-VIEWS, MATHLAB, R,
✍️ Quantitative data analysis and interpretation, and
✍️ any others questions related to research works, please contact us on Telegram.
✍ Data analysis using statistical softwares such as
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The main characteristics of a good sample
✍️ A good sample is the true representative of the population corresponding to its properties.
The population is known as aggregate of certain properties and sample is called sub-aggregate
of the universe.
✍️A good sample is free from bias, the sample does not permit prejudices th65e learning and pre-
conception, imaginations of the investigator to influence its choice.
✍️A good sample is an objective one, it refers objectivity in selecting procedure or absence of
subjective elements from the situation.
✍️ A good sample maintains accuracy. It yields an accurate estimates or statistics and does not
involve errors.
✍️A good sample is comprehensive in nature. This feature of a sample is closely linked with
true-representativeness. Comprehensiveness is a quality of a sample which is controlled by
specific purpose of the investigation. A sample may be comprehensive in traits but may not
be a good representative of the population.
✍️ A good sample is also economical from energy, time and money point of view.
✍️ The subjects of good sample are easily approachable. The research tools can be administered
on them and data can be collected easily.
✍️The size of good sample is such that it yields an accurate results. The probability of error
can be estimated.
✍️ A good sample makes the research work more feasible.
✍️ A good sample has the practicability for research situation.
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✍️ A good sample is the true representative of the population corresponding to its properties.
The population is known as aggregate of certain properties and sample is called sub-aggregate
of the universe.
✍️A good sample is free from bias, the sample does not permit prejudices th65e learning and pre-
conception, imaginations of the investigator to influence its choice.
✍️A good sample is an objective one, it refers objectivity in selecting procedure or absence of
subjective elements from the situation.
✍️ A good sample maintains accuracy. It yields an accurate estimates or statistics and does not
involve errors.
✍️A good sample is comprehensive in nature. This feature of a sample is closely linked with
true-representativeness. Comprehensiveness is a quality of a sample which is controlled by
specific purpose of the investigation. A sample may be comprehensive in traits but may not
be a good representative of the population.
✍️ A good sample is also economical from energy, time and money point of view.
✍️ The subjects of good sample are easily approachable. The research tools can be administered
on them and data can be collected easily.
✍️The size of good sample is such that it yields an accurate results. The probability of error
can be estimated.
✍️ A good sample makes the research work more feasible.
✍️ A good sample has the practicability for research situation.
If you need support related to:
⌚️Assignment / አሳይመንት
⌚️Research / ሪሰርች
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❤4
How to ensure reliability of data for a research?
In research, ensuring the reliability of data means making sure the data are consistent, accurate, and dependable over time and across measurements.
The main ways to ensure data reliability include the following.
✍️ Use Standardized Data Collection Methods:
Use well-designed questionnaires or instruments with clear instructions.
✍️ Ensure Instrument validation:
* Use validated and tested instruments (established surveys).
* Conduct reliability tests such as: Test–retest reliability (consistency over time), Inter-rater reliability (consistency between observers), Internal consistency (e.g., Cronbach’s alpha).
✍️ Train Data Collectors
# Provide proper training and guidelines to enumerators or researchers.
# Reduce interviewer bias by ensuring uniform understanding of questions.
✍️ Pilot Testing (Pre-testing)
@ Conduct a pilot study to identify unclear questions or errors.
@ Revise tools based on pilot feedback before full data collection.
✍️ Data Cleaning and Validation
Check for: Missing values, Outliers, duplicate entries
✍️ Use Multiple Data Sources (Triangulation)
Compare data from different sources, methods, or respondents.
✍️ Ethical and Honest Reporting
Avoid data fabrication or selective reporting.
Consultancy on academic tasks such as:
✍️ Research title selection and concept not description;
✍️ Research proposal draft;
✍️ Thesis or research evaluations;
✍️ Data analysis methods including SPSS, STATA, E-VIEWS, MATHLAB, R,
✍️ Quantitative data analysis and interpretation, and
✍️ any others questions related to research works, please contact us on Telegram.
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@success100research
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In research, ensuring the reliability of data means making sure the data are consistent, accurate, and dependable over time and across measurements.
The main ways to ensure data reliability include the following.
✍️ Use Standardized Data Collection Methods:
Use well-designed questionnaires or instruments with clear instructions.
✍️ Ensure Instrument validation:
* Use validated and tested instruments (established surveys).
* Conduct reliability tests such as: Test–retest reliability (consistency over time), Inter-rater reliability (consistency between observers), Internal consistency (e.g., Cronbach’s alpha).
✍️ Train Data Collectors
# Provide proper training and guidelines to enumerators or researchers.
# Reduce interviewer bias by ensuring uniform understanding of questions.
✍️ Pilot Testing (Pre-testing)
@ Conduct a pilot study to identify unclear questions or errors.
@ Revise tools based on pilot feedback before full data collection.
✍️ Data Cleaning and Validation
Check for: Missing values, Outliers, duplicate entries
✍️ Use Multiple Data Sources (Triangulation)
Compare data from different sources, methods, or respondents.
✍️ Ethical and Honest Reporting
Avoid data fabrication or selective reporting.
Consultancy on academic tasks such as:
✍️ Research title selection and concept not description;
✍️ Research proposal draft;
✍️ Thesis or research evaluations;
✍️ Data analysis methods including SPSS, STATA, E-VIEWS, MATHLAB, R,
✍️ Quantitative data analysis and interpretation, and
✍️ any others questions related to research works, please contact us on Telegram.
✍ Data analysis using statistical softwares such as
🗝SPSS
🗝 STATA
🗝 AMOS
🗝 R
☎️
+251920256875
+251920256875
Telegram Account
https://t.me/Success_Research_Consultant
https://t.me/Success_Research_Consultant
Telegram
@success100research
@rese100arch
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We consult you!!!
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Chi Square Test
✍️ Chi-square is a non parametric statistical data analysis method.
✍️ This test is used to test a hypothesis when the dependent variable is categorical (ordinal or nominal).
✍️ The categorical variable should have two or more categories or levels (eg. Poor, medium, high).
✍️ Its purpose is to test goodness of fit and to test independence (measure of association between two categorical variables with 2 or more levels).
✍️ Chi square test has no alternative parametric test.
✍️ Some of its assumptions are
🔷 Random observation (records).
🔷 Independent items (counted once).
🔷 Each group should have at least 10 items.
🔷 Overall number of items should be 50 and above.
✍️ The effect side (strength of relationship between categorical variables) of chi square can be measured using Phi Coefficient or Cramer's V. value.
References
1. Kothari C. (2008: 247-255)
2. Pallant J. (2010: 220- 247)
3. Cohen's d. (1988: 284-287)
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የማማከር አገልግሎት ያገኛሉ።
✍️ Chi-square is a non parametric statistical data analysis method.
✍️ This test is used to test a hypothesis when the dependent variable is categorical (ordinal or nominal).
✍️ The categorical variable should have two or more categories or levels (eg. Poor, medium, high).
✍️ Its purpose is to test goodness of fit and to test independence (measure of association between two categorical variables with 2 or more levels).
✍️ Chi square test has no alternative parametric test.
✍️ Some of its assumptions are
🔷 Random observation (records).
🔷 Independent items (counted once).
🔷 Each group should have at least 10 items.
🔷 Overall number of items should be 50 and above.
✍️ The effect side (strength of relationship between categorical variables) of chi square can be measured using Phi Coefficient or Cramer's V. value.
References
1. Kothari C. (2008: 247-255)
2. Pallant J. (2010: 220- 247)
3. Cohen's d. (1988: 284-287)
Success Research Consultant:
👍 Research manuals and guidelines,
👍 Sample proposals and research papers,
👍 Business plan guidelines and samples,
👍 Graduate Admission Test (GAT) for Masters and Doctorate programs,
👍 GAT exam models, questions and solutions (answers)
👍 Exit exam samples for graduate students,
👍 Primary school students' guides and textbooks,
👍 Secondary school student's guides and textbooks
👍 Statistical data analysis manuals,
👍 Regional and national exam questions and solutions (Grade 6, grade 8 and grade 12 students),
👍 Data analysis softwares (STATA, SPSS, R, E-VIEW, AMOS)
👍 And others,
Contact at 0920256875
Telegram channel
https://t.me/Success_Research_Consultant
@rese100arch
@success100research
Join and share the links.
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❤7👍1
SUCCESS RESEARCH CONSULTANT
ሰክሰስ የጥናትና ምርምር ስራዎች አማካሪ
Consultancy on academic tasks such as:
✍️ Research title selection and concept not description;
✍️ Research proposal draft;
✍️ Thesis or research evaluations;
✍️ Data analysis methods including SPSS, STATA, E-VIEWS, MATHLAB, R,
✍️ Quantitative data analysis and interpretation, and
✍️ any others questions related to research works, please contact us on Telegram.
✍ Data analysis using statistical softwares such as
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Consultancy on academic tasks such as:
✍️ Research title selection and concept not description;
✍️ Research proposal draft;
✍️ Thesis or research evaluations;
✍️ Data analysis methods including SPSS, STATA, E-VIEWS, MATHLAB, R,
✍️ Quantitative data analysis and interpretation, and
✍️ any others questions related to research works, please contact us on Telegram.
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+251920256875
Telegram Account
https://t.me/Success_Research_Consultant
https://t.me/Success_Research_Consultant
Telegram
@success100research
@rese100arch
የመመረቂያ ጽሁፍ ለምትሰሩ (Research, Thesis, Dissertation),
We consult you!!!
የማማከር አገልግሎት እንሠጣለን።
Share for others!!! ለሌሎች ያጋሩ!!
Telegram
Success Research Consultant
🔷 Business proposal
🔷 Proposal (Masters)
🔷 Research ማማከር
🔷 survey questionnaire ለማዘጋጀት እናማክራለን
🛑 ለተመራቂዎች ተማሪዎች ጥሩ ርዕሰ እንዴት መምረጥ እንዳለባችሁ እናማክራለን!
📩 Contact
👤 @rese100arch
📞 +251920256875
Telegram Channel
https://t.me/Success_Research_Consultant
🔷 Proposal (Masters)
🔷 Research ማማከር
🔷 survey questionnaire ለማዘጋጀት እናማክራለን
🛑 ለተመራቂዎች ተማሪዎች ጥሩ ርዕሰ እንዴት መምረጥ እንዳለባችሁ እናማክራለን!
📩 Contact
👤 @rese100arch
📞 +251920256875
Telegram Channel
https://t.me/Success_Research_Consultant
❤3
SUCCESS RESEARCH CONSULTANT
ሰክሰስ የጥናትና ምርምር ስራዎች አማካሪ
Consultancy on academic tasks such as:
✍️ Research title selection and concept not description;
✍️ Research proposal draft;
✍️ Thesis or research evaluations;
✍️ Data analysis methods including SPSS, STATA, E-VIEWS, MATHLAB, R,
✍️ Quantitative data analysis and interpretation, and
✍️ any others questions related to research works, please contact us on Telegram.
✍ Data analysis using statistical softwares such as
🗝SPSS
🗝 STATA
🗝 AMOS
🗝 R
☎️
+251920256875
+251920256875
Telegram Account
https://t.me/Success_Research_Consultant
https://t.me/Success_Research_Consultant
Telegram
@success100research
@rese100arch
የመመረቂያ ጽሁፍ ለምትሰሩ (Research, Thesis, Dissertation),
We consult you!!!
የማማከር አገልግሎት እንሠጣለን።
Share for others!!! ለሌሎች ያጋሩ!!
ሰክሰስ የጥናትና ምርምር ስራዎች አማካሪ
Consultancy on academic tasks such as:
✍️ Research title selection and concept not description;
✍️ Research proposal draft;
✍️ Thesis or research evaluations;
✍️ Data analysis methods including SPSS, STATA, E-VIEWS, MATHLAB, R,
✍️ Quantitative data analysis and interpretation, and
✍️ any others questions related to research works, please contact us on Telegram.
✍ Data analysis using statistical softwares such as
🗝SPSS
🗝 STATA
🗝 AMOS
🗝 R
☎️
+251920256875
+251920256875
Telegram Account
https://t.me/Success_Research_Consultant
https://t.me/Success_Research_Consultant
Telegram
@success100research
@rese100arch
የመመረቂያ ጽሁፍ ለምትሰሩ (Research, Thesis, Dissertation),
We consult you!!!
የማማከር አገልግሎት እንሠጣለን።
Share for others!!! ለሌሎች ያጋሩ!!
Telegram
Success Research Consultant
🔷 Business proposal
🔷 Proposal (Masters)
🔷 Research ማማከር
🔷 survey questionnaire ለማዘጋጀት እናማክራለን
🛑 ለተመራቂዎች ተማሪዎች ጥሩ ርዕሰ እንዴት መምረጥ እንዳለባችሁ እናማክራለን!
📩 Contact
👤 @rese100arch
📞 +251920256875
Telegram Channel
https://t.me/Success_Research_Consultant
🔷 Proposal (Masters)
🔷 Research ማማከር
🔷 survey questionnaire ለማዘጋጀት እናማክራለን
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📩 Contact
👤 @rese100arch
📞 +251920256875
Telegram Channel
https://t.me/Success_Research_Consultant
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Reliability and validity
🍎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
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🍎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_consultancy
🙏🙏🙏🙏🙏🙏🙏🙏
Telegram
ⓦⓔⓛⓒⓞⓜⓔ🎯
❤3😁1🙏1
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ET EXAM APKን እንዴት መጫን ይቻላል? (የደረጃ በደረጃ መመሪያ)
ET EXAM APKን በመጫን ለስኬት ጉዟችሁ ዛሬውኑ የመጀመሪያውን እርምጃ ውሰዱ!
ደረጃ 1፡ የET EXAM APK ፋይሉን ያውርዱ
ከታች ያለውን ሊንክ በመጫን የET EXAM APK ፋይሉን ወደ ስልካችሁ ያውርዱ።
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ደረጃ 2፡ ከማይታወቅ ምንጭ መተግበሪያዎችን ለመጫን ይፍቀዱ
የET EXAM APK ፋይሉን ማውረዱ ከተጠናቀቀ በኋላ፣ ወደ ስልካችሁ "ቅንብሮች" (Settings) ይሂዱ።
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ጠቃሚ ምክሮች፡
* የET EXAM APK መተግበሪያውን ሁልጊዜ ወቅታዊ (Updated) መሆኑን ያረጋግጡ።
* ማንኛውም ችግር ካጋጠመዎት፣ በዚህ ቴሌግራም ግሩፕ ላይ በመጠየቅ እርዳታ ማግኘት ይችላሉ።
ለስኬት የሚደረግ ጉዞ የሚጀምረው ከመጀመሪያው እርምጃ ነው! ET EXAM APKን ዛሬውኑ በመጫን የፈተና ዝግጅታችሁን ወደ አዲስ ደረጃ አሳድጉ! 🚀
መልካም የፈተና ዝግጅት!
ከታች ያለውን አፕ ይጫኑት! ወይም
👉 [የET EXAM APK የማውረጃ ሊንክ] (ለምሳሌ፡ www.etexam.com/etexam.apk)
በመጫን አፑን ማውረድና መጫን ይችላሉ!
ውድ የET EXAM ቴሌግራም ግሩፕ አባላት፣
የፈተና ዝግጅታችሁን ይበልጥ ቀልጣፋ እና ውጤታማ ለማድረግ የሚያስችል እጅግ አስደሳች ዜና ይዘንላችሁ ቀርበናል! ✨
የET EXAM APK በይፋ ተለቋል! 📱🎮
ይህ APK በቀጥታ ወደ ስልካችሁ በመጫን፣ የET EXAMን ልዩ ባህሪያት በሙሉ በምቾት እና በየትኛውም ቦታ እንድትጠቀሙ ያስችላችኋል።
ለምን ET EXAM APKን መጫን አለባችሁ? 🤔
* ቀልጣፋ ልምምድ (Efficient Practice): ሰፊውን የጥያቄ ባንክ እና የሞዴል ፈተናዎችን በቀጥታ ከስልካችሁ በመስራት የፈተና ፍርሃትን እንድታስወግዱ እና እራሳችሁን እንድትገመግሙ ይረዳችኋል።
* የፈተና ሞድ (Test Mode): ልክ እንደ እውነተኛ ፈተና በጊዜ ገደብ ውስጥ ጥያቄዎችን በመስራት የጊዜ አጠቃቀማችሁን እንድታሻሽሉ እና የፈተና ድባቡን እንድትለምዱ ያደርጋችኋል።
* የልምምድ ሞድ (Practice Mode): ያለ ጊዜ ገደብ ጥያቄዎችን መስራት እና ለእያንዳንዱ ጥያቄ ትክክለኛውን መልስ እና ዝርዝር ማብራሪያ ወዲያውኑ ማየት እንድትችሉ ያደርጋችኋል። ይህ በስህተታችሁ ላይ እንድትማሩ እና ግንዛቤያችሁን እንድታሳድጉ ይረዳችኋል።
* የውጤት ክትትል (Score Tracking): የፈተና ውጤቶቻችሁን በመመዝገብ መሻሻላችሁን እንድትከታተሉ እና ደካማ ጎኖቻችሁን በመለየት የበለጠ እንድታተኩሩ ያስችላችኋል።
ET EXAM APKን እንዴት መጫን ይቻላል? (የደረጃ በደረጃ መመሪያ)
ET EXAM APKን በመጫን ለስኬት ጉዟችሁ ዛሬውኑ የመጀመሪያውን እርምጃ ውሰዱ!
ደረጃ 1፡ የET EXAM APK ፋይሉን ያውርዱ
ከታች ያለውን ሊንክ በመጫን የET EXAM APK ፋይሉን ወደ ስልካችሁ ያውርዱ።
👉 [የET EXAM APK የማውረጃ ሊንክ] (ለምሳሌ፡ www.etexam.com/etexam.apk)
ደረጃ 2፡ ከማይታወቅ ምንጭ መተግበሪያዎችን ለመጫን ይፍቀዱ
የET EXAM APK ፋይሉን ማውረዱ ከተጠናቀቀ በኋላ፣ ወደ ስልካችሁ "ቅንብሮች" (Settings) ይሂዱ።
ከዛም "ደህንነት" (Security) ወይም "ግላዊነት" (Privacy) የሚለውን ይምረጡ።
እዚያም "ከማይታወቅ ምንጭ መተግበሪያዎችን መጫን" (Install from Unknown Sources) የሚለውን በመፈለግ ያብሩት።
ደረጃ 3፡ የET EXAM APK ፋይሉን ይጫኑ
የET EXAM APK ፋይሉን ካወረዱ በኋላ፣ በስልካችሁ "ፋይል አቀናባሪ" (File Manager) ወይም "የወረዱ" (Downloads) አቃፊ ውስጥ በመሄድ ይፈልጉት።
የET EXAM APK ፋይሉን ያግኙት እና ይጫኑት። ከዛም በስክሪኑ ላይ የሚታዩትን መመሪያዎች በመከተል መጫኑን ያጠናቅቁ።
ደረጃ 4፡ ET EXAM መተግበሪያውን ይክፈቱ እና መለያ ይፍጠሩ/ይግቡ
የET EXAM መተግበሪያው ከተጫነ በኋላ፣ መተግበሪያውን ይክፈቱ።
አዲስ ተጠቃሚ ከሆኑ "ይመዝገቡ" (Register) የሚለውን በመጫን አዲስ መለያ ይፍጠሩ።
ቀደም ብለው መለያ ከፈጠሩ "ይግቡ" (Login) የሚለውን በመጫን በመለያዎ ይግቡ።
እንኳን ደስ አላችሁ! 🎉
አሁን የET EXAM APK መተግበሪያውን ሙሉ በሙሉ መጠቀም ይችላሉ! ለፈተናዎ ዝግጁ ይሁኑ!
ጠቃሚ ምክሮች፡
* የET EXAM APK መተግበሪያውን ሁልጊዜ ወቅታዊ (Updated) መሆኑን ያረጋግጡ።
* ማንኛውም ችግር ካጋጠመዎት፣ በዚህ ቴሌግራም ግሩፕ ላይ በመጠየቅ እርዳታ ማግኘት ይችላሉ።
ለስኬት የሚደረግ ጉዞ የሚጀምረው ከመጀመሪያው እርምጃ ነው! ET EXAM APKን ዛሬውኑ በመጫን የፈተና ዝግጅታችሁን ወደ አዲስ ደረጃ አሳድጉ! 🚀
መልካም የፈተና ዝግጅት!
ከታች ያለውን አፕ ይጫኑት! ወይም
👉 [የET EXAM APK የማውረጃ ሊንክ] (ለምሳሌ፡ www.etexam.com/etexam.apk)
በመጫን አፑን ማውረድና መጫን ይችላሉ!
❤5👍1
SUCCESS RESEARCH CONSULTANT
ሰክሰስ የጥናትና ምርምር ስራዎች አማካሪ
Consultancy on academic tasks such as:
✍️ Research title selection and concept not description;
✍️ Research proposal draft;
✍️ Thesis or research evaluations;
✍️ Data analysis methods including SPSS, STATA, E-VIEWS, MATHLAB, R,
✍️ Quantitative data analysis and interpretation, and
✍️ any others questions related to research works, please contact us on Telegram.
✍ Data analysis using statistical softwares such as
🗝SPSS
🗝 STATA
🗝 AMOS
🗝 R
☎️
+251920256875
+251920256875
Telegram Account
https://t.me/Success_Research_Consultant
https://t.me/Success_Research_Consultant
Telegram
@success100research
@rese100arch
የመመረቂያ ጽሁፍ ለምትሰሩ (Research, Thesis, Dissertation),
We consult you!!!
የማማከር አገልግሎት እንሠጣለን።
Share for others!!! ለሌሎች ያጋሩ!!
ሰክሰስ የጥናትና ምርምር ስራዎች አማካሪ
Consultancy on academic tasks such as:
✍️ Research title selection and concept not description;
✍️ Research proposal draft;
✍️ Thesis or research evaluations;
✍️ Data analysis methods including SPSS, STATA, E-VIEWS, MATHLAB, R,
✍️ Quantitative data analysis and interpretation, and
✍️ any others questions related to research works, please contact us on Telegram.
✍ Data analysis using statistical softwares such as
🗝SPSS
🗝 STATA
🗝 AMOS
🗝 R
☎️
+251920256875
+251920256875
Telegram Account
https://t.me/Success_Research_Consultant
https://t.me/Success_Research_Consultant
Telegram
@success100research
@rese100arch
የመመረቂያ ጽሁፍ ለምትሰሩ (Research, Thesis, Dissertation),
We consult you!!!
የማማከር አገልግሎት እንሠጣለን።
Share for others!!! ለሌሎች ያጋሩ!!
Telegram
Success Research Consultant
🔷 Business proposal
🔷 Proposal (Masters)
🔷 Research ማማከር
🔷 survey questionnaire ለማዘጋጀት እናማክራለን
🛑 ለተመራቂዎች ተማሪዎች ጥሩ ርዕሰ እንዴት መምረጥ እንዳለባችሁ እናማክራለን!
📩 Contact
👤 @rese100arch
📞 +251920256875
Telegram Channel
https://t.me/Success_Research_Consultant
🔷 Proposal (Masters)
🔷 Research ማማከር
🔷 survey questionnaire ለማዘጋጀት እናማክራለን
🛑 ለተመራቂዎች ተማሪዎች ጥሩ ርዕሰ እንዴት መምረጥ እንዳለባችሁ እናማክራለን!
📩 Contact
👤 @rese100arch
📞 +251920256875
Telegram Channel
https://t.me/Success_Research_Consultant
❤3👍1
Research design
👍Research design is a comprehensive plan for data collection in an empirical research
project. It is a “blueprint” for empirical research aimed at answering specific research
questions or testing specific hypotheses, and must specify at least three processes:
(1) the data collection process,
(2) the instrument development process, and
(3) the sampling process.
🍏Broadly speaking, data collection methods can be broadly grouped into two categories:
positivist and interpretive.
👍 Positivist methods, such as laboratory experiments and survey research, are aimed at theory (or hypotheses) testing, while interpretive methods, such as action research and ethnography, are aimed at theory building. Positivist methods employ a deductive approach to research, starting with a theory and testing theoretical postulates using empirical data.
👍In contrast, interpretive methods employ an inductive approach that starts with data and tries to derive a theory about the phenomenon of interest from the observed data.
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_consultancy
🙏🙏🙏🙏🙏🙏🙏🙏
👍Research design is a comprehensive plan for data collection in an empirical research
project. It is a “blueprint” for empirical research aimed at answering specific research
questions or testing specific hypotheses, and must specify at least three processes:
(1) the data collection process,
(2) the instrument development process, and
(3) the sampling process.
🍏Broadly speaking, data collection methods can be broadly grouped into two categories:
positivist and interpretive.
👍 Positivist methods, such as laboratory experiments and survey research, are aimed at theory (or hypotheses) testing, while interpretive methods, such as action research and ethnography, are aimed at theory building. Positivist methods employ a deductive approach to research, starting with a theory and testing theoretical postulates using empirical data.
👍In contrast, interpretive methods employ an inductive approach that starts with data and tries to derive a theory about the phenomenon of interest from the observed data.
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_consultancy
🙏🙏🙏🙏🙏🙏🙏🙏
Telegram
ⓦⓔⓛⓒⓞⓜⓔ🎯
❤3
Classification of Research based on Application:
🍏a. Pure / Basic / Fundamental Research: As the term suggests a research activity taken up to look into some aspects of a problem or an issue for the first time is termed as basic or pure.
It involves developing and testing theories and hypotheses that are intellectually challenging to the researcher but may or may not have practical application at the present time or in the future. The knowledge produced through pure research is sought in order to add to the existing body of research methods. Pure research is theoretical but
has a universal nature. It is more focused on creating scientific knowledge and predictions for further studies.
🍏b. Applied / Decisional Research: Applied research is done on the basis of pure or fundamental research to solve specific, practical questions; for policy formulation, administration and understanding of a phenomenon. It can be exploratory, but is usually descriptive. The purpose of doing such research is to find solutions to an immediate issue, solving a particular problem, developing new technology and look into future advancements etc. This involves forecasting and assumes that the variables shall not change.
Key Differences between Basic and Applied Research
✍️a) Basic Research can be explained as research that tries to expand the already existing scientific knowledge base. On the contrary, applied research is used to mean the scientific study that is helpful in solving real-life problems.
✍️b) While basic research is purely theoretical, applied research has a practical approach.
✍️c) The applicability of basic research is greater than the applied research, in the sense that the former is universally applicable whereas the latter can be applied only to the specific problem, for which it was carried out.
✍️d) The primary concern of the basic research is to develop scientific knowledge and predictions. On the other hand, applied research stresses on the development of technology and technique with the help of basic science.
✍️e) The fundamental goal of the basic research is to add some knowledge to the already existing one. Conversely, applied research is directed towards finding a solution to the problem under consideration.
If you need support related to:
⌚️Assignment / አሳይመንት
⌚️Research / ሪሰርች
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🍏a. Pure / Basic / Fundamental Research: As the term suggests a research activity taken up to look into some aspects of a problem or an issue for the first time is termed as basic or pure.
It involves developing and testing theories and hypotheses that are intellectually challenging to the researcher but may or may not have practical application at the present time or in the future. The knowledge produced through pure research is sought in order to add to the existing body of research methods. Pure research is theoretical but
has a universal nature. It is more focused on creating scientific knowledge and predictions for further studies.
🍏b. Applied / Decisional Research: Applied research is done on the basis of pure or fundamental research to solve specific, practical questions; for policy formulation, administration and understanding of a phenomenon. It can be exploratory, but is usually descriptive. The purpose of doing such research is to find solutions to an immediate issue, solving a particular problem, developing new technology and look into future advancements etc. This involves forecasting and assumes that the variables shall not change.
Key Differences between Basic and Applied Research
✍️a) Basic Research can be explained as research that tries to expand the already existing scientific knowledge base. On the contrary, applied research is used to mean the scientific study that is helpful in solving real-life problems.
✍️b) While basic research is purely theoretical, applied research has a practical approach.
✍️c) The applicability of basic research is greater than the applied research, in the sense that the former is universally applicable whereas the latter can be applied only to the specific problem, for which it was carried out.
✍️d) The primary concern of the basic research is to develop scientific knowledge and predictions. On the other hand, applied research stresses on the development of technology and technique with the help of basic science.
✍️e) The fundamental goal of the basic research is to add some knowledge to the already existing one. Conversely, applied research is directed towards finding a solution to the problem under consideration.
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⌚️Research / ሪሰርች
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⌚️Term Paper / ተረም ፔፐር
⌚️Case study/ ኬዝ ስተዲ
⌚️Article Review
⌚️Mini research
⌚️Business plan
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🪜And other related software's...... also any other Questions please contact us via
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