How to perform a one way multilevel analysis with SPSS

Factor multilevel analysis is such that it needs to have a treatment, each treatment has several levels, and each level has several replicates.

SPSS (Statistical Product and Service Solutions) software. Originally, the full name of the software was "Statistical Package for the Social Sciences", but with the expansion of SPSS products and services and the increase in service depth, SPSS officially changed the full name of English to "Statistical Products and Services Solutions" in 2000, marking a major adjustment in the strategic direction of SPSS. SPSS is the general name of a series of software products and related services for statistical analysis operations, data mining, analysis and decision support tasks launched by IBM, and is available in Windows and Mac OS X versions.

The one-way ANOVA SPSS steps are as follows:

Operating tools: win10 computer.

Operating software: SPSS analysis tool.

Operational Version:

1. First of all, open the SPSS analysis tool through the shortcut, and the data view will be displayed by default.

2. Switch to the variable view, and then add six variables, namely name, m, c, e, s, and r, where the name is a string type, and the others are numeric types.

3. Return to the data view and insert the corresponding data into the six variable columns.

4. Click the Analysis menu, and then select Classification --- Systematic Clustering.

5. Open the system clustering analysis window and move the variables m and c to the variable box.

6. Click the statistics button on the right to open the system cluster analysis: statistics window, select the centralized plan, and then click Continue.

7. Click the graph button to open the graph settings window, check the pedigree chart, and then click Continue.

8. Then click the Method button to open the System Clustering Analysis: Methods window, select Wald's method as the clustering method, and then click Continue.

9. Finally, click the OK button in the system clustering analysis window, and then generate the system clustering analysis results and graphical display.

SPSS automatically calculates the F-statistic, and if the associated probability p is less than the significance level a, the null hypothesis is rejected and the mean of each population is considered to be significantly different at different levels of the control variable, and vice versa, i.e., there is no difference.

Homogeneity test of variance: analysis of whether the overall variance of each observed variable is equal at different levels of control variables. Using the variance homogeneity test, the null hypothesis is that "there is no significant difference in the variance of the observed variables at each level, and the idea is the same as the ANOVA in the two-independent samples T-test of SPSS".

The concomitant probability is greater than the significance level, so the overall variance is considered to be equal.

The differences between the two types of parties are the same

The basic steps of the two types of ANOVA are the same, but the decomposition of the variation is different, for the data designed in groups, the total variation is decomposed into intra-group variation and between-group variation (random error), that is: SS total = between SS groups + within the SS group, while for the data designed in the compatibility group, the total variation is decomposed into the treatment group variation and random error in addition to the compatibility group variation, that is: SS total = SS treatment + SS compatibility + SS error.

If you want to do one-way ANOVA, please have a good understanding of the methods and principles of one-way ANOVA (written in statistical books) before proceeding with relevant operations.

Conditions for one-way ANOVA:

1) Each population obeys a normal distribution.

2) The variance of 2 is the same for each population.

3) Samples drawn from each population are independent of each other.

ANOVA, a method of hypothesis testing of multiple (more than two) treatment averages, whereas univariate refers to only one experimental factor in that experiment. One-way ANOVA was used to determine the superiority and disadvantage of this experimental factor for each treatment.

To put it simply, if there is only one influencing factor in the experiment, and there are multiple different levels of processing, the final data can be analyzed using one-way ANOVA. The f-number is used to judge significance.

For example, the results show that the f-value is compared to the significance level f, if it is greater than the f-value of significance, then p is less than the probability of significance, f>f(, then p<, indicating a significant difference between treatments.

1. First of all, hit the SPSS software, open and click "Analysis" - "Compare Average" - "One-Factor ANOVA".

2. In the pop-up "One-way ANOVA" tab, select "Weight" into the list of strain variables, and "Feed Type" into the factors.

3. Click "Multiple Comparison After the Fact" on the right, select "LSD" in the pop-up tab, and then click Continue.

4. Then click "Options" on the right, select "Descriptive" and "Variance Homogeneity Test" in the pop-up tab, and click OK.

5. In the results, what we need to see is the homogeneity test of variance, and we can see p=> in the table of "one-factor homogeneity test", which means that the variance is homogeneous, and the one-way ANOVA method can be used.

Regression analysis is usually used to study the degree to which multiple factors influence an outcome, and linear regression and logit regression are common.

What is the difference between linear regression analysis and logistic regression? The following describes the data types, prerequisites, analysis, and application scenarios.

1.The data types are different.

Linear regression requires the dependent variable to be a quantitative variable and logistic regression requires the dependent variable to be a categorical variable, if it is a binary logistic regression analysis, the amount of the avid variable is required to be a dichotomous variable, and no sail can only be 0 and 1, such as whether to buy, 1 means yes, 2 means no, multi-categorical logistic regression analysis, the dependent variable requires to be a categorical variable and disordered, such as "playing hail football", "playing basketball" and "playing badminton", etc. Ordinal logistic regression analysis requires that the dependent variables be categorical and orderly, such as "unwilling", "willing", "very willing", and so on.

2.The prerequisites are different.

Linear regression requires the dependent variable to obey a normal distribution, but logistic regression does not, and linear regression requires a linear relationship between the independent variable and the dependent variable, while logistic regression does not require a linear relationship between the independent variable and the dependent variable.

3.Analyze the relationship differently.

Linear regression analyzes the relationship between the entire causal face and the independent variable, but logistic regression analyzes the relationship between the probability of the dependent variable taking a certain value and the independent variable. For example, binary logistic regression analysis analyzes the relationship between the probability of a dependent variable of 1 and the independent variable.

4.The application scenarios are different.

In practical life, linear regression is generally used on the basis of quantitative statistical methods, and is often used for quantitative data, such as housing prices, logistic regression analysis is more suitable for classification problems, such as the occurrence of something, whether the loan is in default, etc., linear regression can generally solve linear problems, and logistic regression can solve nonlinear problems.

3. Operate both.

Linear regression analysis.

Path of Operation: General Methods Linear Regression.

Logistic regression analysis.

Operation Path: Advanced Methods Binary Logit Multi-Categorical Logit Ordered Logit

Multivariate ANOVA is the analysis of variance performed on whether an independent variable is affected by one or more factors or variables. SPSS tune.

The "univariate" process is used to test whether there is a difference in the mean of the dependent variable between different combinations of levels due to different factors. The role of each factor can be analyzed in this process.

It is also possible to analyze the interactions between factors, as well as the covariance, and the interactions between the factor variables and the covariates. The process requires that the dependent variable is randomly sampled from a multivariate normal population with the same variance for each element in the population.

However, the mean comparison results can also be selected by the homogeneity test of variance. The dependent and covariates must be numeric variables, and the covariates and dependent variables are not independent of each other. Factor variables are categorical variables, which can be numeric or character-based variables up to 8 in length.

The fixed factor variable is the factor of response treatment; A random factor is a factor that is randomly drawn from a population.

Example] to study the effects of different temperatures and humidity on the developmental period of armyworms, and the experimental data are shown in Table 5-7. To analyze whether there were significant differences in the effects of different temperature and humidity on the developmental duration of armyworms.

The analysis results of SPSS for Windows are clear, intuitive, easy to learn and use, and can directly read Excel and DBF data files, and has been promoted to a variety of computers with various operating systems, and it is known as the three most influential statistical software in the world together with SAS and BMDP.

There is an unwritten rule in the international academic community that in international academic exchanges, all calculations and statistical analyses completed with SPSS software do not need to explain the algorithm, which shows its great influence and high credibility.