Data Analysis

Data Analysis

Data Analysis

Introduction

Research on teacher effectiveness has progressed through three distinct stages, mirroring data accessibility and appearing empirical approaches. Initial studies relied on cross sectional data aggregated at the level of schools or even school districts. This approach associated mean school check tallies to aggregate measures of educator proficiency. Most explicit measures of educator qualifications like experience and education had little effect on scholar achievement. In compare, implicit assesses of educator quality (i.e., the average presentation of one-by-one educators) differed significantly over teachers. These studies lacked controls for the former accomplishment of students attending distinct assemblies of schools. If localities allotted educators with more powerful credentials to schools with better prepared scholars, then the approximated return to teacher credentials would be overstated.

Analysis

Q1: Relationship between students' math achievement and teachers' experience and their salary

To find out that is there any relationship between this two variable, Chi-square test has been performed.

Ho: there is a relation between these two variable

H1: there is not any relation between these to variable and these two variables are independent



Chi-Square Tests

Value

df

Asymp. Sig. (2-sided)

Pearson Chi-Square

1.423E3a

1302

.011

Likelihood Ratio

561.750

1302

1.000

Linear-by-Linear Association

19.172

1

.000

N of Valid Cases

137

a. 1410 cells (100.0%) have expected count less than 5. The smallest anticipated enumerate is .01.

Symmetric Measures

Value

Asymp. Std. Errora

Approx. Tb

Approx. Sig.

Interval by Interval

Pearson's R

.375

.080

4.707

.000c

Ordinal by Ordinal

Spearman Correlation

.361

.079

4.504

.000c

N of Valid Cases

137

a. Not presuming the null hypothesis.

b. Uvocalise the asymptotic benchmark error presuming the null hypothesis.

c. Based on normal approximation.

As it can be seen the p value in this case is less than leveled significance which is 0.05. Hence it can be concluded that these two variables are independent and there is not any relation between these two variables.

What is the best predictor (pick one!) to explain students' math achievement scores?

To find out the answer, scatter plot and correlation coefficient value can be used.

Scatter plot

Scatter plots are alike to line graphs in that they use horizontal and upright axes to contrive facts and figures points. However, they have a very exact purpose. Scatter plots show how much one variable is affected by another. The connection between two variables is called their correlation.

Scatter plots generally comprise of a large body of data. The nearer the data points arrive when contrived to making a directly line, the higher the correlation between the two variables, or the more powerful the relationship.

If the data points make a directly line going from the origin out to high x- and y-values, then the variables are said to have a affirmative correlation. If the line moves from a high-value on the y-axis down to a high-value on the x-axis, the variables have a contradictory correlation.

From above scatter plot it can be seen that there is much more strong relation between salary and student math score than math score and teacher experience.

Correlations

The association between two variables reflects the degree to which the variables are related. The most widespread measure of association is the Pearson Product instant association (called Pearson's correlation for short). When measured in a population the Pearson Product Moment correlation is ...