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SPSS & STATA – Effective Data Analysis Tools!

To reach a conclusion in a research program, a scholar has to go analyze and work with heavy sets of data. There are many data analysis tools, available at research centers, universities and colleges that help researchers during their task. SPSS and STATA are two among tens of others important data analysis tools.

SPSS – Statistical Package for the Social Sciences

It is a popular software application, designed by SPSS Inc., and owned by IBM. The IBM SPSS Statistics is not only a helpful tool for social science, but also for financial markets, health, medical, and various others areas or subjects. It is such a handy tool that even a non-researcher can analyze his/her pieces of data in this program. One can efficiently perform statistical analysis, data management, case selection, reshaping of file, derive data, and the documentation of data, cross tabulation, linear regression, factor analysis, with the user-friendly SPSS application.

STATA

Transfigured with the words Statistics and Data, STATA is a very useful tool for researchers. Produced by the StataCorp in the year 1985, the application is employed for data management, data analysis, statistical analysis, linear and multiple regression analysis, and graphic simulation in the research of economics, political science, biomedical, and social science. However, it has some limitations, as it opens only one dataset at one point of time; it stores its data and results in its virtual memory, as a result, it slows down, when analyzing large sets of data.

Conversely, many data analysts and frequent researchers find these tools unnecessary. Many of them assert and support using commonplace spreadsheet application, as MS Excel. Sometimes, the results and data released by these applications can complex and confusing. These programs are based on spreadsheet applications. And, if you have proficiency in Excel program, then you do not need to go with these tools.

Common Mistakes in Data Analysis and how to avoid them

Data analysis may seem like a tough job but it has to be accomplished to perfection if you want to submit your PhD dissertation and get your degree. By conducting the right data analysis, you are much closer to getting the correct results and this will help you get better grades in your PhD Dissertation. However, data analysis can be a challenging task and if not executed with proper planning, it can result in many mistakes, leaving your research non-conclusive.

Some of the common mistakes in data analysis include:

  • Not having a defined goal for the study and conducting the study in a biased manner
  • Using an unsystematic approach and conducting an analysis without basically understanding the problem
  • Using the wrong evaluation technique
  • Ignoring significant factors that can change the results
  • Not paying attention to the errors that might occur while data inputs
  • Conducting an extremely complex analysis
  • Not presenting the results in a proper way
  • Omitting limitations that are a crucial part of the study

In order to avoid these mistakes, you must create a checklist for yourself and read it thoroughly and follow it religiously before you start your data analysis:

  • Have you clearly stated the goals of your study?
  • Have you maintained an unbiased perspective towards your study?
  • Have you followed all the steps of the research systematically without skipping any step?
  • Do you have a clear understanding of the problem at hand before you begin the analysis?
  • Have the chosen the best possible evaluation technique for your analysis?
  • Is your analysis correct statistically?
  • If you have made any errors in input, will it cause a drastic change in the results?
  • Have you explained the results graphically as much as possible? It is easy for the reader to understand your analysis?
  • Have you documented the limitations of the study clearly

Data analysis can be a challenging task and therefore, a step by step clearly defined process will help you to avoid any errors and provide better results.

How to improve at data analysis

Data analysis is the foundation of any research paper, dissertation or thesis. It formulates the base around which the thesis is created and therefore, it has to be strong otherwise the entire research can be non-valid. The results of data analysis have to be error-free if you want to get the right results and prove your hypothesis right or wrong. Even a small mistake in the data analysis can ensure that your entire research is wrong, thus making it difficult for you to get your degree.

Many students who do not come from a statistical background find it even more difficult to face the challenges of data analysis. The data collected is quite large sometimes and therefore, it overwhelms the students and they tend to make even more mistakes than they normally would. Plus, the software that students are expected to use for data analysis is quite rigorous and difficult to comprehend in a short span of time. So students who have never used these software before find themselves completely lost and depressed since they are unable to analyse their data with the speed that is expected at the college or university.

Tips to improve your knowledge and skills in data analysis:

Practice is the key to perfection: In statistics, the more you practice, the better you will get at it. Keep practicing data analysis with dummy data or raw data to help you gauge the software and understand the challenges you face.

Read and research: With the age of the internet, it is extremely easy to find tutorials and videos that will help you understand various tests and their uses.

Double-check each test: When working on your thesis data analysis, you must be completely sure about the analysis that you have conducted. Check all the test twice and ensure that you have not made any silly mistake.

Descriptive Statistics – An Easy Way to Describe Your Data

When we talk about dispersion and measures of central tendency, we talk about descriptive statistics. These measures are termed as descriptive statistics simply because you may use them to describe your data. While it may be easy to collect your research data, describing it in a meaningful manner can be a challenging task for many students. However, descriptive statistics is not terrifying at all, provided you understand its measures and learn to use them in an appropriate manner. In fact, descriptive statistics makes your complex data sets quite simple to comprehend.

Measures of central tendency include mean, median and mode. All these measures help you summarize your lists of scores and describe them through a single number. Is it not amazing to tell your readers about your entire data with a single number? Measures of central tendency really make your task easier by performing a few calculations. For example, you might need to describe the weights of people in your exercise group to an instructor. One way to do this is by telling about every single person and his/her weight. This would make the instructor do some relative calculations in their mind so they may figure out the general weight of people in your group.

Another method is to use the measures of central tendency and give a single average weight figure to your instructor. This is far easier to understand for the person requiring specific and relevant information. Some similar calculations can be done for cases where dispersion needs to be described. In such cases, measures of dispersion can be used. The measures of dispersion include variance, range and standard deviation. These help you understand the spread of scores when you have certain figures with you. Thus, all measures of descriptive statistics help in simplifying your task of describing your data. Overcome your fear and make use of these valuable tools for your research.

Is it wise to get external help for data analysis?

Data analysis is the foundation of your research. If you go wrong with collecting data and analysing it, you can end up missing the target in your dissertation. Statistics is a tough subject for those who come from a different background. If you have never worked on the software before, you might find it overwhelming to complete the data analysis in time. It is advisable to consult experts and get professional help for data analysis if you cannot manage it on your own. Depending on the time constraints and the affordability, you can get a host of services in terms of data analysis, statistics, etc.

What to expect from data analysis services?

If you hire a company who is expert in statistical analysis, you can get an excellent data analysis on your study. You will get to interact with expert statisticians with years of experience who can guide you in your study and help you reach your objective within the given time. For those who have never worked on statistical software, you can also get software assistance in your dissertations.

Different tests that you can get guidance on:

  • Regression/Correlation analysis
  • Chi Square test
  • Z test
  • T Test
  • ANOVA
  • Mann Whitney Test
  • Hypothesis test

With good test results, you are in a much better position to reach the right conclusion in your study. No matter what form of data you provide, the experts from the company will work with your data and get you results that will help you reach the conclusion of your dissertation. Data analysis is an extremely crucial step in your dissertation. If you pay careful attention to this and seek assistance from the right company, your dissertation will stand out from your peers and you will have a better chance of scoring well.

Good luck!