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How Data Analysis Matters

In the process of writing a research it is important for you to put in relevant and accurate data. You may wonder how and why the data is so important and do the evaluators actually go through the pain of cross checking, the answer is YES! They check and they know when you are bluffing. If you follow why it is important to have the right data analysis, you may find the exercise itself less daunting.

The data analysis helps you in creating a premise for your study. Without a proper analysis you may not be able to make your study strong. This in turn may lead to your study getting rejected.

Data analysis makes your process of writing the dissertation much easier because when you have facts on hand, all you have to do is explain them; the entire process becomes that much easier.

The process of data analysis contributes to every part of your study because the matter you put in should in any way be connected to your inferences.

The analysis of data not only helps in enhancing the study but also ensures that the topic on which you choose to do you dissertation, is thoroughly understood by you.

Finally the data analysis works as a tool to help you in explaining your point to those who go through your work and make an evaluation of it, if you get it right you are winning at your dissertation.

When you are out to showcase your work to the world by means of your dissertation project, make sure you give your best to it. The way you present your inferences and analysis speaks a lot about how much you have understood the topic that you chose to study. Making data analysis one of the most important factors of the dissertation study.

Are the Days for a Two Phase Research Over? Is It Time for Integrate Research Now?

Can we combine Quantitative and Qualitative Research even till date? We cannot say that with certainty, but can surely say that it has become more and more unnecessary. In most of the researches that are both qualitative and quantitative in nature, they work in two phases as the time required becomes double to conduct both the researches. In the current times, why has it become unnecessary? This is so because; the qualitative tools available in today’s time make such requirements redundant.

There are a lot of digital tools available in today’s times that easily integrate the advantages of both qualitative and quantitative research in a very beneficial way. Some of the main advantages these days are:

1. Lesser cost: The digital survey option can take up a double job of being a recruiting screener.
2. Faster: In the earlier times, in a two phase research there was a lot of time wasted in what we call as transition from one phase to another, in this time it is not required as the qualitative design doesn’t take any longer than a survey.
3. Better insights: When the digital tools are able to simultaneously give a qualitative perspective to a quantitative survey, it gives the advantage of a much deeper quantitative insight and identifies the qualitative contributors in research as well.

In today’s time, technology has enabled to link to any survey platform. The selection and screening of the respondents becomes possible and it is possible to experience the qualitative perspective of it either during the survey or at the end. The respondents who have been chosen can participate in any of the qualitative experiences. The most easily visible ones are,: Webcam, interviews, mobile interviews etc. So we can say that the quant qual integration trend is seen picking up at a rapid pace. Do not get left behind.

Can Writers Be Productive?

Jane was thorough with her statistical knowledge. She had worked on her research concepts before venturing into academic research. There was nothing that she lacked in terms of her skills to be a great researcher. But, it wasn’t working that ways. There was something that was needed to get the desired output from her efforts. Well, Jane was a great researcher but not a productive writer. She struggled with getting consistency in her writing initiatives. Her thorough concepts also did not help all the time. What could she do to become a productive writer? A writer who could actually produce result oriented documents. It may not require some special skills. Like I said, Jane already had those. It surely needs some discipline, consistency and meticulous planning. Let us look into some tips, in day to day life, that could actually surprise the researcher with the productivity in the efforts:

1. Design a writing plan week wise: Those who have studied management are not unfamiliar with the concept of Objective and Task Method. It does work in organisations but a researcher needs to become an organisation in itself. By which I mean, set for yourself realistic, achievable and clear objectives which are short term and aligned with your ultimate mission. It could be do get your Doctoral degree or get yourself an A class publication or may be something as distinct at being a market researcher. Do not stretch your plans beyond a week.

2. Do further breakdown in planning: Like how we follow the pyramid of vision, mission, goals, objectives and plans. We need to give ourselves daily challenges. What Jane didn’t do was this. Some day she over stretched herself and then for some days she stayed away from work all together. The quality of her work actually was dependent upon her concentration and mood of the effort. It reflected in her quality and finally her results. Follow a daily plan , a daily writing initiative and see that there is a smooth flow in your writing and that is a critical indicator of quality.

3. Do not victimize your writing to uncontrollable circumstances: Circumstances are dynamic. When I say that do follow your daily writing challenge, do work around your circumstances. As they change, as your other demands modify, do modify the way you give time to your writing. You cannot run away from uncontrollable circumstances but certainly, with some modifications, you can surely let it not have an impact on the consistency of your work.

Desire, Discipline and determination are the keys that work beyond the skills and lead the path to productive writing. Upgrade your skills and knowledge but also become a meticulous planner, rather than just a planner. Jane, gear up!!

Role of SPSS in Finding out the Missing Data

SPSS or statistical package for social science is a software wrap up used for statistical investigation and data analysis involved in all the research methodologies of the dissertation. SPSS has a wide role in the dissertation, it assists in finding missing data’s from the list of numbers, their acquisition and interpretation and at last analyzing data result.
All the above process are equally important, but the most imperative among all is finding missing data. As, if a single number or a group of numbers is missing from the list of numbers it can misconstrue the entire study. Therefore, let’s do a deep study of how SPSS helps in finding missing data –
• Adding and finding average of all the numbers in the series – SPSS has a quality of adding a series of data and finding the average of the same. The series also includes the missing numbers and tells about the missing quantity. For an instance-
1) Average marks of 6 PhD students = M1+M2+ M3+ M4+M5+M6/6
2) The mean of the marks obtained by 6 PhD students = Mean ( A1, B2, C3, D4, E5, F6)
Average marks of 6 students cannot be calculated if any of them is missing whereas mean is easy to be intended. Missing numbers can be analyzed from the main series if the series has a minimum of 4 numbers in it.
Find out how many numbers are present (let’s say 4) and write in the way as –: MEAN. 4(A1, B2, C3, D4, E5, F6)
• Missing numbers Vs non missing numbers –
a. Both the missing and non missing numbers are compared first.
b. “Frequency Command” is used to compare the missing and non missing numbers along with other variables.
• Distribution of missing numbers –
a. “Aggregate” command is used by SPSS to find out the frequency and hence the missing numbers from the series.
Thus, this statistical package is useful in finding out the missing numbers.

The Combination SPSS with R-Language Can Do Wonder

The R language is used to develop statistical software which has become very popular with statisticians and data miners. R-language is extensively used for advanced data analysis. It provides a broad array of statistical and graphical practices such as:

• Classical statistical tests
• Time-series analysis
• Linear and nonlinear modeling
• Classification
• Clustering

It is an intelligent idea to combine SPSS software and R-syntax. It will benefit a lot. If you are not convinced, we have listed seven truly good reasons why you should use SPSS and R-language. Please have a look.

1. The interface is quite simple.

The SPSS graphical user interface supports a number of statistical analysis, data preparation, and analytical modeling algorithms.

2. There is no additional learning required.

SPSS software can easily handle statistical analysis, data, and modeling so you don’t have to learn any other thing.

3. Data preparation is easy.

SPSS software can read spreadsheets, text input, SAS files and more. It takes less time for extracting, manipulating and transforming data because wizards with pre-built connectors access data easily.

4. There are more options for output.

There is a one click accesses to SPSS presentation-ready charts and graphs. You can publish results to Word, PDF, PowerPoint, Excel and other formats. These results can be viewed on number of platforms.

5. The performance is superior.

The combination of SPSS with R-language works best for different environments such as SAP, Netezza Hana, and Oracle.

6. Group effort.

When you use SPSS software and R-syntax, you will lose the “lone work” aspect of R. With custom dialogs, you can generate new functions that anyone can use.

7. Security.

SPSS software provides an intelligent structure for securing, centralizing, and automating your analytical assets so that you can get assured that your environment is not at risk.
Combining the strengths of R with SPSS software makes logical sense.