Literature Review and ANOVA

Describe about the Mix Model Analysis?

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:

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.

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.

Quantitative Data Analysis

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.

When the study takes place, in early stage of that a small qualitative sample has taken. In that case, there found a most difference between senior and junior participants in case of their training of identifying and avoiding plagiarism, where it is found that junior members got benefit after attending various research seminars as part of their PhD course and aware about various referencing software and different technique, whereas most of the senior lecturer was unaware of the form of the formal anti-plagiarism training. According to the primary analysis it is observe that, most of the senior lecture avoid to look over the plagiarism and not interested in the in perusing students who plagiarise, but at another side when younger lecturers gets into plagiarism case, they solve the case themselves rather than to go to Internet to check. Generally it relates with the different searching skill of different lecturer.          

When we need to test the independence between the given two variables then we use the tests for independence. For this purpose, we have to see the chi square test for independence in detail which is given as below:

We use this statistical hypothesis test for independence of two categorical variables. The null hypothesis for the chi square test for the independence is given as there is no any association exists between the given two categorical variables. The main assumption for this test is that the both categorical variables should be from a single population. For checking this claim, we calculate the value for the test statistic and p-value associated with this test statistic and then we compare this p-value with the given level of significance or alpha value and then take the decision regarding the null hypothesis whether the given two categorical variables are independent or not. We can use some other tests for this purpose. There are some non parametric tests are available for the independence of variables.

In the survey it is founds 681 cases of the plagiarism and academic misconduct. The 681 student case shows approximately 15% of total student. A sample of this shows exact result to within +/-3.5%with 95% confidence.

Here, we have to find the five journal articles regarding the statistical analysis including the qualitative and quantitative analysis. We have to find these articles from the EBSCO or Google Scholar. Let us see the abstracts for these five articles given below:

Qualitative Data Analysis

Researcher study the diversity in Smartphone usage for checking some claims regarding study of Smartphone usage. For this purpose, the researcher used the data of 255 users. By using this data regarding the Smartphone, researcher conducts the comprehensive study of Smartphone use and its diversity. Researcher found that the average number of interactions per day varies from 10 to 200. Researcher also found that the average amount of data usage or received per day varies from 1 to 100 MB. Researcher concluded that the mechanisms to improve user experience or energy consumption will be more effective if user learn and adapt to user behaviour.  Researcher also concluded that the 90^th percentile error with the adaptation is less than the half compared to predictions which are based on the average behaviour across users.

This article explains the large scale analysis for the Smartphone usage in the real life. For this study, the researcher considers the different variables or parameters for this study regarding the Smartphone use in the real life. For this experiment, the researcher uses the data of 77 participants from a European country over 9 months. For this period, researcher collects real data of actual usage and other data regarding the variables under study.

For this article, the researcher studies the facets of simplicity for the Smartphone interface and researcher constructed a structural model for this study. Researcher measures the simplicity perception for a Smartphone user interface. The research incorporated the visual aesthetics, information design and the task complexity into an extended construct of simplicity. The researcher studies the three distinct domains of the interaction between the human and computer and related areas. Finally researcher constructed the model involving the six main components such as reduction, organization, component complexity, coordinative complexity, dynamic complexity and visual aesthetics. After some statistical hypothesis test, the researcher accepted the null hypothesis that the user satisfaction was positively affected by simplicity perception and that the relationship between the two constructs was very strong. Also, researcher draws some other conclusions regarding this study and finds out the different aspects regarding the Smartphone usage.

For this article, researcher conduct a large scale study of Smartphone user and then researcher measures the energy consumption characteristics of 17300 BlackBerry Smartphone users. Researcher studies the three main distinct types of Smartphone’s such as opportunistic chargers, light-consumers and the nighttime’s chargers. Then researcher did some statistical data analysis and draws some conclusions regarding the energy consumption characteristics of each user type. Researcher also studies some other aspects regarding the different aspects of BlackBerry Smartphone’s.

For this article, the researcher study the communication system and the use of internet or data used for the communication system. Also researcher compares the different communication systems and finds out the difference between these communications systems. Researcher study the different aspects of communication systems through various media and suggests that how the internet is effective communication system than other communication system. For this purpose, researcher collects the data for different communication system regarding the efficiency of communication system, time needs for completing the communication, etc. Then researcher studies these different aspects regarding different communication system and then suggests that how the internet is more efficient over other communication systems.

Tests of Independence

For this section, we have to discuss two articles from the above five articles in the section 2. Let us see two articles in detail given as below:

Researcher study the diversity in Smartphone usage for checking some claims regarding study of Smartphone usage. For this purpose, the researcher used the data of 255 users. By using this data regarding the Smartphone, researcher conducts the comprehensive study of Smartphone use and its diversity. Researcher characterise this study according to the intentional user activities such as interactions with the device and the applications used by the user. Researcher study the impact of these activities of user on the network and the energy usage. Researcher finds immense diversity among the users. Researcher found that all users differ by the one or more than one orders of magnitude. Researcher found that the average number of interactions per day varies from 10 to 200. Researcher also found that the average amount of data usage or received per day varies from 1 to 100 MB. Researcher concluded that the mechanisms to improve user experience or energy consumption will be more effective if user learn and adapt to user behaviour.  Researcher found that the qualitative similarities exist among the users that facilitate the task of learning user behaviour. Researcher found that the variable relative application popularity follows an exponential distribution with different parameters for different users. Researcher also concluded that the 90^th percentile error with the adaptation is less than the half compared to predictions which are based on the average behaviour across users.

We can get more information about this article on the following link:

This article explains the large scale analysis for the Smartphone usage in the real life. For this study, the researcher considers the different variables or parameters for this study regarding the Smartphone use in the real life. Researcher uses two main contextual variables such as places and social context of condition of use of Smartphone applications. Researcher found the strong dependencies between the phone usage and these two contextual variables. For this experiment, the researcher uses the data of 77 participants from a European country over 9 months. For this period, researcher collects real data of actual usage and other data regarding the variables under study. Researcher also study the key patterns used by the user for their Smartphone’s. After collecting this data, researcher did some statistical data analysis on large scale and draws some conclusions.

References:

1. David Freedman, Robert Pisani, Roger Purves, Statistics, 3rd ed., W. W. Norton & Company, 1997.

2. Morris H. DeGroot, Mark J. Schervish Probability and Statistics, 3rd ed., Addison Wesley, 2001.

3. Leonard J. Savage, The Foundations of Statistics, 2nd ed., Dover Publications, Inc. New York, 1972.

4. Robert V. Hogg, Allen T. Craig, Joseph W. McKean, An Introduction to Mathematical Statistics, 6th ed., Prentice Hall, 2004.

5. George Casella, Roger L. Berger, Statistical Inference, 2nd ed., Duxbury Press, 2001.

6. David R. Cox, D. V. Hinkley, Theoretical Statistics, Chapman & Hall/CRC, 1979.

7. Peter J. Bickel, Kjell A. Doksum, Mathematical Statistics, Volume 1, Basic Ideas and Selected Topics, 2rd ed. Prentice Hall, 2001.

8. T. S. Ferguson, Mathematical Statistics: A Decision Theoretic Approach, Academic Press, Inc., New York, 1967

9. Harald Cramér, Mathematical Methods of Statistics, Princeton, 1946

10.Laubach RS, Koschnick K. Using Readability: Formulas for Easy Adult Materials. Syracuse, NY: New Readers Press, 1977.