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Multinomial logistic regression 🍏Multinomial logistic regression is a statistical model that extends binary logistic regression to handle categorical dependent variables with more than two categories. 🍏In multinomial logistic regression, the dependent variable is categorical with three or more unordered categories, and the independent variables can be either categorical or continuous. 🍏The goal of multinomial logistic regression is to model the probability of each category of the dependent variable as a function of the independent variables. 🍏The model estimates a separate set of coefficients for each category of the dependent variable, which represent the change in log-odds of being in that category for a one-unit change in the independent variable, holding all other variables constant. 🍏The model assumes that the relationship between the independent variables and the dependent variable is linear on the log-odds scale. 🍏The model estimates the parameters using maximum likelihood estimation, which involves finding the values of the coefficients that maximize the likelihood of observing the data given the model.http://t.me/nicereaserch

🍏Multiple regression assumptions How can we test regression assumptions? Before performing regression analysis, it is important to check regression assumptions like multicollinerity, normality, ... 1. Normally distribution Can be tested using either graphical methods by histogram, plotted lines 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 can be checked using either correlation coefficient between independent variables or by using Tolerance and VIF values of variables. That is, there is no multicollinearity among independent variables (multicollinearity assumption is not violated) if the correlation coefficient is less than 0.9 or tolerance is above 0.1 and VIF is below 10. http://t.me/nicereaserch

paired t-test 🍏 paired t-test: is a statistical test used to compare the means of two related groups or conditions. It is also known as a dependent t-test. 🍏paired t-test: is used when the same group of participants is measured twice, under two different conditions, and the researcher wants to compare the means of a continuous variable between the two conditions. 🍏For example, suppose a researcher wants to compare the effectiveness of two different teaching methods on student performance. The same group of students is selected and randomly divided into two groups, and each group is taught using a different teaching method. 🍏The researcher then measures the student's performance using a continuous variable, such as a test score, before and after each teaching method. The paired t-test would be used to compare the mean test scores before and after each teaching method. 🍎The paired t-test assumes that the mean difference between the two conditions is normally distributed. It also assumes that the differences are independent and have a constant variance. 🍎The paired t-test calculates a t-value and a p-value, which indicate the significance of the difference between the two conditions. 🌍If the p-value is less than a predetermined significance level (usually 0.05), the result is considered statistically significant, indicating that there is a significant difference between the two conditions. If the p-value is greater than the significance level, the result is considered not statistically significant, indicating that there is no significant difference between the two conditions. 🌍The paired t-test is a useful statistical tool for comparing the means of related groups or conditions, particularly in research studies where the same group of participants is measured under different conditions. However, it is important to ensure that the assumptions of the test are met before conducting the analysis.https://t.me/nicereaserch

Parametric tests and non-parametric tests 🌍Parametric tests and non-parametric tests are two broad categories of statistical tests that are used to analyze data. The main difference between them is that parametric tests make assumptions about the data being analyzed, while non-parametric tests do not. 🌍Parametric tests assume that the data being analyzed follow a specific probability distribution, usually the normal distribution. 🌍They also assume that the data has a specific level of measurement, such as interval or ratio data. Examples of commonly used parametric tests include the t-test, ANOVA, and linear regression. 🌍 Parametric tests are generally more powerful than non-parametric tests, which means they are better able to detect small differences or changes in the data, but are only appropriate if the assumptions are met. 🍏Non-parametric tests, on the other hand, make fewer assumptions about the data being analyzed. They are used when the data does not meet the assumptions of parametric tests, such as when the data is not normally distributed or when it has ordinal or nominal measurement scales. 🍏Examples of non-parametric tests include the Mann-Whitney U test, the Kruskal-Wallis test, and the Wilcoxon signed-rank test. 🍏Non-parametric tests are generally considered less powerful than parametric tests, but they are more robust to violations of assumptions. 🍏It is important to choose the appropriate type of test depending on the nature of the data being analyzed and the research question being addressed. 🍏If the data is normally distributed and the assumptions of parametric tests are met, then parametric tests are generally preferred because of their greater statistical power. 🍏However, if the data is not normally distributed or if the assumptions of parametric tests are violated, then non-parametric tests should be used instead.https://t.me/nicereaserch

NULL HYPOTHESIS vs ALTERNATIVE HYPOTHESIS In statistical hypothesis testing, the null hypothesis (H0) and the alternative hypothesis (H1 or Ha) are two competing statements about a population parameter or the relationship between variables. The null hypothesis represents the absence of an effect, while the alternative hypothesis suggests that there is a difference, relationship, or effect present. More specifically: 1. Null Hypothesis (H0): The null hypothesis assumes that there is no significant difference or relationship between variables, or that any observed difference is due to chance. It is often stated as a statement of equality, such as "there is no difference" or "the mean is equal to a specific value. "there is no effect.. " "there is no association..." Example If the research question is:- Does a new teaching method improve students' test scores?" The Ho would be:- There is no significant difference in test scores between students who receive the new teaching method and those who do not." Researchers generally try to disprove the null hypothesis in favor of the alternative hypothesis. 2. Alternative Hypothesis (H1 or Ha): The alternative hypothesis is the statement that contradicts or opposes the null hypothesis. It suggests that there is a significant difference or relationship between variables, or that the observed difference is not due to chance. It can be directional (one-sided) or non-directional (two-sided). A directional alternative hypothesis specifies the direction of the expected effect "the mean is greater than" "the mean is less than" while a non-directional alternative hypothesis simply states that there is a difference or relationship without specifying the direction. Or the mean is different... Example If the research question is:- "Does a new teaching method improve students' test scores?" The alternative (Ha) would be stated:- Directional alternative hypothesis: "Students who receive the new teaching method will have significantly higher test scores compared to those who do not." Non-directional alternative hypothesis: "There is a significant difference in test scores between students who receive the new teaching method and those who do not." But you don't state as directional or non directional alternative hypotheses in your research. Which hypothesis can I use? In choosing which hypothesis to state in your study, it depends on your research question or objective. If you have a specific expectation or a prior belief about the direction of the effect, you may choose a directional alternative hypothesis. On the other hand, if you simply want to determine whether there is a difference or relationship between variables without specifying a direction, a non-directional alternative hypothesis would be appropriate. Moreover, You may or may not state both null and alternative hypotheses for similar research questions in your research. However, in many research studies, the alternative hypothesis tends to be of more interest because it represents the claim or effect that the researcher wants to establish or support. The researcher rejects the null hypothesis if the p value is significant (P‹ 0.05)

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Important links for SPSS multi nominal logistic regression explanation 1. https://www.youtube.com/watch?v=dW7bcSTgWX8 introduction SPSS 2. https://www.youtube.com/watch?v=d5bMpAOE7jY data entry SPSS mallku 3. https://www.youtube.com/watch?v=DFFEqJqvVw0 how to enter questionaries’ into SPSS in Amharic 4. https://www.youtube.com/watch?v=MZ00VGyuD4o how to enter questionaries’ into SPSS in Amharic 5. https://www.youtube.com/watch?v=X994eM7zz2o full SPSS SPSS training in English 6. https://youtu.be/vm40rOYRgU4 data entry in Amharic 7. https://www.youtube.com/watch?v=IXTlP1SRIvQ data entry SPSS in Amharic 8. https://www.youtube.com/watch?v=YKUq_9gi2A0 data editing SPSS in Amharic 9. https://www.youtube.com/watch?v=3rGxCYUXloE 10. https://www.youtube.com/watch?v=HL33Ekq3LXA how to transfer non normal to normal SPSS l in Amharic 11. https://www.youtube.com/watch?v=WAx2pkINOB4 One way ANOVA analysis SPSS in Amharic 12. https://www.youtube.com/watch?v=g_mouDtbpIY How to check goodness / chi-square test SPSS in Amharic 13. https://youtu.be/dC00M8mDs3g chi-square test SPSS SPSS in Amharic 14. https://youtu.be/WhO4nfJaQUw chi-square test in Amharic 15. https://www.youtube.com/watch?v=3AFmSeO-9-w&pp=ygU_aG93IHRvIGtub3cgcC0gdmFsdWUgIG11bHRpbmltaW5hbCBsb2dpc3RpYyAgaW4gc3BzcyBpbiBhbWhhcmlj generate respondent code SPSS in Amharic mallku 16. https://www.youtube.com/watch?v=sWYwqykkVh8 to test data normality SPSS in Amharic melaku 17. https://youtu.be/HhhWxhIBRNE for multinomial log by SPSS data assumption in Amharic 18. https://www.youtube.com/watch?v=LRob3KVtlU0 multinomial log by SPSS data assumption in English 19. https://www.youtube.com/watch?v=xLJTmaisLOQ data analysis SPSS in Amharic 20. https://www.youtube.com/watch?v=N83t9_qJp-g analysis of descriptive by SPSS in Amharic 21. https://www.youtube.com/watch?v=vq2IxOxts54 multinomial log by SPSS data assumption in English 22. https://www.youtube.com/watch?v=TXvEQpDHcO0 how to select cases in SPSS in Amharic Melaka 23. https://www.youtube.com/watch?v=vm40rOYRgU4&pp=ygU1ZGF0YSBpbnRlcmluZyBpbiBzcHNzIGZvciBtdWx0aW5vbWluYWwgbG9nIGluIGFtaGFyaWM%3D data entry SPSS explanation in Amharic 24. https://youtu.be/LRob3KVtlU0 multinomial regression SPSS in Amharic 25. https://youtu.be/JcCBIPqcwFo in English multinomial regression mln SPSS introduction 26. https://www.youtube.com/watch?v=wV4_BIKl0cA How to interpret SPSS in Amharic

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