The most important part of writing a fascinating paper is to develop a great thesis statement. You see, your thesis statement is the spine for your entire paper. It’s the glue that holds your paper together. The more complex, specific, and interesting, the better your paper will be. Your thesis is the glue for your paper. Make sure your thesis doesn’t divert into different directions. Stay focused on one main theme to keep your paper organized so that your readers maintain their interest on the topic. Streamline the flow of your thesis by mentioning relevant data. Also make sure to focus on the correctness and specificity of the data. A unidirectional thesis is the key to an interesting paper.

# Monthly Archives: March 2012

# Develop a Strong Opinion about your Topic

Writing a great thesis statement means you need to develop a strong opinion about your topic. This is how radio talk show hosts keep their audiences they spew strong opinions that attract listeners and phone calls.If you’re not sure how to form a strong opinion about your topic, start reading through journal article abstracts. Check out Google Scholar and read through thesis statements pertaining to your topic. Jot down any strong opinions that look interesting to you.

# How to write a thesis statement

After you have found a general subject and have read a general article for background, you must next decide how to write a thesis statement on the topic you have chosen.This truth, stated in a simple sentence, provides you with a thesis statement. It is a statement of your opinion, a conclusion that, from what you have read, you have reason to believe can be proven, but that you are scholar enough to discard or alter later if you uncover facts that prove it invalid.

# Read a general article

After you have decided on a subject, the next step is to read a general, authoritative article from a trusted source. It is always best to start with an overview and then work your way down to details. Time and time again, this approach proved to be the most effective way to start writing a research paper. One of the immediate benefits of doing this is that you will immediately understand whether you want to pursue that topic or not. You will also learn what information to look for next. Look for the ideas that prompt you to ask why or how they are true or in what specific way they must be true. These ideas will provide a basis upon which you will formulate a temporary thesis and temporary outline.

# Finding your Research Subject

Your choice of a suitable research subject is often determined by whether or not you are interested in reading or investigating. Investigating the primary sources puts you in the role of a detective and is best paired with the subject that you have developed an intimate preference. Working with secondary sources is allows you to see the larger picture by analyzing what others have said about your topic.

The typical research paper involves the latter; you are expected to do a fair share of library research. Many people, who are wondering how to write a research paper, also need to be critical on the topic choice. Not only it should be interesting, it also must be suitable for academic level. Some subjects are not worth investigating; they are too trivial, merely factual, or too routine. Others are often too new or current for a conclusive study.

# The Peer Review Process

In the peer review process, a paper is submitted to a journal and evaluated by several reviewers. (Reviewers are often individuals with an impressive history of work in the area of interest, that is, the specific area that the article addresses). After critiquing the paper the reviewers submit their thoughts to the editor. Then, based on the commentaries from the reviewers, the editor decides whether to publish the paper, make suggestions for additional changes that could lead to publication, or reject the paper.

The primary purpose of peer review is to ensure that the papers published are valid and unbiased.

# The Iterative Process of Research

Ultimately, the key to a successful research project lies in iteration: the process of returning again and again to the research questions, methods, and data, which leads to new ideas, revisions and improvements. It is easy to think of research as a step-by-step 1-2-3 process, but it is important to be flexible and open to changes. Oftentimes, by discussing the research project with advisers and peers, one will find that new research questions need to be added, variables need to be omitted, and other changes are required. As a proposed study is examined and reexamined from different perspectives, it may begin to transform and take a different shape. This is to be expected and is a component of a good research study. In addition, it is important to examine study methods and data from different vieblogoints to ensure a comprehensive approach to the research question. In conclusion, there is no one formula for developing a successful research study, but it is important to realize that the research process is cyclical and iterative.

# Statistical Significance

Researchers cannot simply conclude that there is a difference between two groups in a well-constructed study. This difference must be due to the manipulation of the independent variable. No matter how well a researcher designs the study, there always exists a degree of *error* in the results. This error can be due to individual differences both within and between experimental groups, or the error can be due to systematic differences within the researcher’s sample. Irrespective of its source, this error acts as a kind of noise in the data. It affects participants scores on study measures even though it is not the variable of interest. Statistical significance is aimed at determining the probability that the observed result of a study was due to the influence of the independent variable rather than by chance. A result is statistically significant at a certain level. For example, a result might be significant at p<.05. represents the probability that the result was due to chance, and .05 represents a 5% probability that the result was due to chance.Â Therefore, p<.05 means that inferential statistical analysis has indicated that the observed results have over a 95% probability of being due to the influence of the independent variable. The 5% cutoff is generally thought of as the standard for most scientific research. Note that it is theoretically impossible to ever be entirely certain that ones results are not due to chance, as the nature of science is one of falsification, not immutable proof.

# Correlation

Correlation is one of the most often used (and most oftenÂ *mis*used) kinds of descriptive statistics.Â It is perhaps best described as â€œa single number that describes the degree of relationship between two variables.â€Â If two variables tend to be â€œcorrelated,â€ it means that a participantâ€™s score on one tends to vary with a score on the other.Â For example, peopleâ€™s height and shoe size tend to be positively correlated.Â This means that for the most part, if a given man is tall, he is likely to have a large shoe size.Â If short, he is likely to have a smaller shoe size.Â Correlation can also be negative. For example, the temperature outside in Fahrenheit may be negatively correlated with the number of hot chocolates sold at a local coffee shop.Â This is to say that as the temperature goes down, hot chocolate sales tend to go up.Â Although causality may seem to be implied in this situation, it is important to note that on a statistical level,Â **correlation does not imply causation.**Â A good researcher knows that there is no way to assess from *correlation alone*Â that a causal relationship exists between two variables. In order to assert that â€œX caused Yâ€, a study should be experimental, with control groups and random sampling procedures.Â Determining causation is a difficult thing to do, and it is a common mistake to assert a cause-and-effect relationship when the study methodology does not support this assertion.

# Data Preparation and Analysis – Descriptive Statistics

Descriptive statistics describe the data but do not draw conclusions about the data.Â Descriptive statistics are normally applied to a single variable at a time.Â They can tell the researcher the central tendency of the variable, meaning the average score of a participant on a given study measure.Â The researcher can also determine the distribution of scores on a given study measure, or the range in which scores appear.Â Finally, descriptive statistics can be used to tell the researcher the frequency with which certain responses or scores arise on a given study measure.Â For example, in our imaginary study about the effectiveness of corrective lenses on economic productivity, the researcher might observe that the average dollars-per-week (DPW) of a person with corrected vision is $500, whereas the average DPW for a person without corrected vision is $450.Â A good researcher will know that this is not enough information to conclude that vision correction has an effect on economic productivity. Inferential statistics are necessary to draw conclusions of this kind.Â Descriptive statistics might also tell the researcher that the distribution of DPW is $351-$640 for the whole sample, and that the average DPW is $445 for the sample.