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a) Literature review and ANOVA
This study consists of 700 undergraduate students from the six different faculties and from four institutes. The total number of valid questionnaires after the exclusion of missing values was 692. The questionnaire including three sections is given to students and data is collected through this questionnaire. Respondent asked to check different scales for option. Researcher used simple statistical techniques for analysis of this data. Researcher used the mean and standard deviations for the given information. After this study, researcher found that there were 198 students (28.6%) adopted performance goal and 494 students (71.4%) adopted mastery goal. Researcher also used the Cronbach’s alpha test based on reliability.
For drawing conclusions about the fact, we need to perform some analysis. We perform this analysis based on the data available for us. There are two types of data such as quantitative data and qualitative data. if the data is quantitative, then we perform a quantitative statistical analysis and if the data is qualitative, then we perform the qualitative data analysis. But for some study, both type of data analysis needed because the study or experiments consist of both types of data in it. This type of analysis or models is called as mixed model data analysis. We have to see the quantitative and qualitative data analysis methods in this section. Also, we have to take a review of the ANOVA test and test for independence. Let us see the quantitative and qualitative data analysis in short given as below:
1. Quantitative data analysis:
If we have available the data in the quantities or numbers, then we use the quantitative data analysis. For this quantitative data analysis, there are different statistical methods. We can use the descriptive statistics for describing the data variables or parameters. We can also use the inferential statistical tests for proving some claims regarding the variables under study. In descriptive statistics, we study the mean, mode, median, standard deviation, range, kurtosis, etc. While in inferential statistics, we use the different statistical hypothesis tests for checking or testing the null hypothesis or the claim stated by the researcher. Selection of proper test is important because if we do not select the appropriate hypothesis test, then we cannot get the desired results regarding the hypothesis or claim under study.
2. Qualitative data analysis:
If the variable gives the data in the qualitative format or in attributes, then we perform the qualitative data analysis. There are several methods are available for qualitative data analysis. We can use some non parametric tests for this type of data sets. We can also use some attribute analysis for this types of test. For the qualitative data, we need to categorize this data according to different categories under study. Then we need to label these data sets. That is, organization of data is very essential in the qualitative data analysis.
In both type of data analysis, some main steps are very important. These steps are given as collection of data, classification of data, analysis of data and interpretation of data.
3. ANOVA: Analysis of Variance
In the analysis of variances, we test the null hypothesis that whether all the population means are same or not. In other words, we check whether there is any significant difference in the given populations. We use this test of analysis of variance when we are given a more than two samples. For two samples, we can use the z test or t test. For this ANOVA, we calculate the test statistic F and p-value regarding it and then compare this p-value with the given level of significance and then we take the decision regarding the null hypothesis whether we have to reject or not reject the null hypothesis that all population means are same.
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