Use invarience analysis. Below is an example on how to do that.

Article When empathy hurts: Modelling university students’ word of m...

Multigroup CFA. See example here:

Article Assessing understandings of substance use disorders among No...

SPSS doesn't have SEM, you have to buy the add-on AMOS but I would rather recommend MPLUS.

best

John kåre

Or maybe you should just use ordinary regression to see if there is any association between teacher quality and student achievement.

Your data is not independent though, I suppose there is a few teachers and they are each connected to a group of students.

Ideally you should have student achievement at beginning and end of school year/ period. A teacher could have been lucky and have all the brighest students in his/her class. Change in achievement score would be better.

Jk

Dear Mohammed

The analyses have to be based on your hypotheses and questions.

Regards,

Your Research Question and assumed findings usually determine the direction and type of analysis. i.e Does teacher quality affect Student achievement ( is there a relationship between the two variable, etc etc.)

In case you are modelling the relationships (based on ypur RQs) and collect your using questionnaire survey, then you have to choose the analysis techniques based on the type of data you collect. If you collected likert data then you can select any non-parametric test tool such as PLS path modelling, otherwise if your study is more exploratory, you can use CB-SEM approach.

Dear Researcher

Your question consists of several issues.

A. Regarding the analysis of data of Structural Equation Modeling, please refer to:

1. Donna L. Schminkey Timo von Oertzen Linda Bullock(2016). Handling Missing Data With Multilevel Structural Equation Modeling and Full Information Maximum Likelihood Techniques. https://doi.org/10.1002/nur.21724.

Volume39, Issue4, 286-297. https://onlinelibrary.wiley.com/doi/full/10.1002/nur.21724

2. Data Analysis: Structure Equation Modeling (SEM)

January 2016, DOI: 10.1007/978-981-10-0053-9_4. In book: Expatriate Manager’s Adaption and Knowledge Acquisition.

https://www.researchgate.net/publication/301256051_Data_Analysis_Structure_Equation_Modeling_SEM

3. Rufus Lynn Carter (2006). Solutions for Missing Data in Structural Equation Modeling. Research & Practice in Assessment, Volume One: Winter 2006, 1-7,

https://files.eric.ed.gov/fulltext/EJ1062693.pdf

4. Paul D. Allison (2003). Missing Data Techniques for Structural Equation Modeling. Journal of Abnormal Psychology, Vol. 112, No. 4, 545–557.

https://statisticalhorizons.com/wp-content/uploads/Allison-2003-JAP-Special-Issue.pdf

B. The second issue is the sample size. In order to select an optimal sample size, several principles need to be satisfied:

5. General remark:

Research studies are usually carried out on sample of subjects rather than whole populations. The most challenging aspect of fieldwork is drawing a random sample from the target population to which the results of the study would be generalized.

6. The key to a good sample is that it has to be typical of the population from which it is drawn. When the information from a sample is not typical of that in the population in a systematic way, we say that error has occurred. In actual practice, the task is so difficult that several types of errors, i.e. sampling error, non-sampling error, Response error, Processing error,…

In addition, the most important error is the Sampling error, which is statistically defined as the error caused by observing a sample instead of the whole population. The underlying principle that must be followed if we are to have any hope of making inferences from a sample to a population is that the sample be representative of that population.

7. A key way of achieving this is through the use of “randomization”. There several types of random samples, Some of which are: Simple Random Sampling, Stratified Random Sampling, Double-stage Random Sampling... Moreover, the most important sample is the simple random sample which is a sample selected in such a way that every possible sample of the same size is equally likely to be chosen. In order to reduce the sampling error, the simple random sample technique and a large sample size have to be developed.

8. Specific remarks:

The following factors are highly affected the sample size and need to be identified:

• Population Size,
• Margin of Error,
• Confidence Level (level of significance) and
• Standard of Deviation.

9.Then, the sample size can be estimated by,

Necessary Sample Size = (z-score or t-value)2 * StdDev*(1-StdDev) / (margin of error)2 .

10.For more information, please refer to:

i. William G. Cochran, Gertrude M. Cox (1977). Experimental Design, Wiley.

ii. Snedecor, W.G. and Cochran,W.G. (1989). Statistical Methods, Blackwell.

ii.Geoffrey Keppel Professor Emeritus, Thomas D. Wickens (2004). Design and Analysis: A Researcher's Handbook (4th Edition). International Edition.

C. Regarding the question, how to develop an appropriate statistical analysis, the following points need to be studied:

11. How many random variables in your research. How many dependent and independent random variables.

12. What is the relation between the random variables, i.e. related or not.

13. Regression analysis required a linear relationship between the independent random variables and the dependent random variable.

14.If the sample size of your random variables >30 you can use most of statistical tests.

15. If the sample size of your random variables

The statistical methods to be used in analysing data from 2 different surveys (2 different populations) depends upon the objective(s) of the study/analysis. In the case of 2 different surveys (2 different populations) - e.g. teachers quality and Student Achievement, the following statistical methods can possibly be applied in analysis:

(1) Regression and correlation determination,

(2) Statistical Estimation of various characteristics of data,

(3) Statistical testing of hypothesis,

(4) Statistical modelling,

etc.

The sample size is mainly determined from

(1) Population Size,

(2) Precision of estimates wanted,

(3) Power of the test applied,

(4) Available time for survey

& (5) Fund available for survey.

The statistical methods to be used in analyzing data from 2 different surveys (2 different populations) depends upon the objective(s) of the study/analysis. In the case of 2 different surveys (2 different populations) - e.g. teachers quality and Student Achievement, the following statistical methods can possibly be applied in analysis:

(1) Regression and correlation determination,

(2) Statistical Estimation of various characteristics of data,

(3) Statistical testing of hypothesis,

(4) Statistical modelling,

etc.

The sample size is mainly determined from

(1) Population Size,

(2) Precision of estimates wanted,

(3) Power of the test applied,

(4) Available time for survey

& (5) Fund available for survey.

Dear Mohammed abdulrab,

Have you made any progress in the solving of the problem you have aimed at. Go slowly, think deeply, work out perfectly. Success is then certain.