The term One Sample Proportion test or in other words, the One Proportion test is mainly used when there is a requirement for estimation of the proportion revolving around a wide range of target population. The one-sample proportion test SPSS is conducted to compare the proportion relating to a specific target or any significant reference value. It can also calculate a specific range of values all of which are likely to include all the calculations of the targeted population proportion. In other words, the hypothesis test for proportion is called a hypothesis of inequality. The hypothesis test for proportion is typically a correlation analysis where the procedure focuses on calculating the overall data sample size. Here, the statistical power for testing a single proportion by applying two ways in which one is either the exact test or the other is the approximate z-tests. Apart from this, there is one more also, the two proportion z test in SPSS generally allows the user to have a comparison between the two of proportions to find out if both of them are the same or not.   

      

There are mainly two types of proportion present – one sample proportion hypothesis test and the two sample proportion hypothesis test with one factor and two factors, respectively where both these tests assume the data set related binomial distribution. Both these tests are applied in the SPSS data analysis and Chi-Square test regarding the sample population survey data sets and their random values. This one-sample proportion test when used in the SPSS data analysis procedure conducts a comparison of the observed proportion to a theoretical one when there are only two categories. The one-sample proportion test is usually applied for determining whether the collected sample which came from a particular population along with specific means. Both the one sample proportion test and two sample proportion hypothesis test are almost similar but in two sample proportion hypothesis test, there are two hypotheses – one is the null hypothesis (H0) used for performing this T-Test where the proportions are the same, and equal so there can be a hypothesised proportion. On the other side, there presents the alternate hypothesis (H1) used for the T-Test has two proportions that are neither the same nor equal where there is a two-tailed hypothesised proportion. In the null hypothesis, the targeted population proportion is greater than the one-tailed hypothesised proportion. Different from the null hypothesis, in the alternate hypothesis, the targeted population proportion is very less as compared to the one-tailed hypothesised proportion.   

             

The one sample proportion test SPSS has its different confidence in the intervals which are used by the user when dealing with any single proportion (ˆp) and along with a critical value which will be used is “z∗”. The one sample proportion test SPSS is simply denoted as “ˆp” and “1−ˆp” is denoted as “ˆq. z∗” which is said to be the critical value as found on a table. When the hypothesis is null of the population proportion (p) which equals the hypothesised value H0: p = p0. As an example, if anyone wants to present any new method of teaching for students who are fighting to learn English language grammar so that they can bring improvement in their grammar skills with full depth and details on the national average. In this scenario, the pupils will be the sample population who will receive the newly introduced teaching technique and this means the population will be on the national average score. Here, the hypothesis test for the proportionate test mainly tells if that proportion is going equal to the targeted value or not. The means of the targeted population is not known always, but sometimes it becomes hypothesised for the correlation analysis.   

         

Step 1: Click the “Analyze” option > then go to the “Compare Means” option > then click on the “One-Sample T Test...” on the main menu which will present the One-Sample T Test dialogue box   

Step 2: Then transfer the dependent variable, “dep_score”, into the “Test Variable(s):” box just by selecting it which can be done by clicking on it and after that clicking on the “Right arrow” button

     

Step 3: Enter the population mean which is going to be compared with the sample against, in the “Test Value:” box, just by changing the current value of "0" to "4". After that keep the “Estimate effect sizes” option checked

   

Step 4: After this step click on the “Options” button which will present the One-Sample T Test: Options dialogue box, and then the default 95% confidence intervals must be kept the same and then turn the “Exclude cases analysis by analysis” into the “–Missing Values–” is checked 

  

Step 5: After that click on the “Continue” button which will take you back to the One-Sample T-Test dialogue box   

Step 6: Click on the “OK” button to finally generate the resulting output   

Step 7: These whole steps show the “Compare Means > One-Sample T Test...” procedure presents how to analyse the collected data by using the one-sample t-test in the SPSS Statistics software when all the assumptions have not been violated.

The one-sample proportion test is usually applied for determining whether the collected sample which came from a particular population along with specific means. The one sample proportion test using SPSS help is simply denoted as “ˆp” and “1−ˆp” is denoted as “ˆq. z∗” which is said to be the critical value as found on a table. There are two hypotheses – one is the null hypothesis (H0) used for performing the T-Test where the proportions are the same, and equal so there can be a hypothesised proportion. On the other side, there presents the alternate hypothesis (H1) used for the T-Test has two proportions that are neither the same nor equal where there is a two-tailed hypothesised proportion. Here, the hypothesis test for the proportionate test mainly tells if that proportion is going equal to the target value or not. The means of the targeted population is not known always, but sometimes it becomes hypothesised for the correlation analysis.