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Yes, we can. In this overview of accounting, math, and statistical analysis we hope to answer some of the most frequently asked questions we get from students who are confused about how to conduct statistical analysis or incorporate it into their research. Of course, many people are still going to be confused about the issue because our answers are generic and cannot answer individual questions about specific projects. That is why you can always contact us with more questions.
We tell our clients that we can handle requests for any type of statistical analysis, but do you know what the number one question we get from students about statistical analysis is? “What test should I use?” Many students think of this as something that they should know, but the reality is that many colleges and universities only give students a rudimentary background in statistics before expecting them to be able to understand the differences between different types of analysis and when to use each type. As a result, students do not even know where to start when given a research project and told to incorporate statistical analysis. They may have their research and all of the data that they need, but not know how to translate that into results for a paper.
Broadly speaking, statistics are used to show relationships. The goal of analysis is to demonstrate whether a relationship exists, and, in some instances the direction of that relationship. However, that broad answer does not tell you what type of test you should use to analyze your particular data set. We cannot tell you that without knowing information about your particular project, because the best way to show that relationship is often dependent upon factors such as the sample size, the research design, the distribution of the data, and even the type of variable. Data that is normally distributed is generally easier to analyze, allowing you to choose a parametric test. Data that has an abnormal distribution is more difficult to analyze and will probably require the use of a non-parametric test. If you read on for more information about the various types of statistical tests and when they are used, it may help you determine the appropriate type of analysis for your project.
There are multiple different statistical tests that can be used for statistical analysis. We have listed the most popular, along with how they are used. These tests can be broadly broken into two categories, parametric and non-parametric. Parametric tests can be broken down into three subcategories; correlational, comparison of means, and regression. Each sub-type of test has its own uses, along with several tests you can use, depending on the details of your data
Correlational tests are used to look for an association between variables. They are probably the easiest type of statistical analysis to conduct, and the ones most likely to be suggested for undergraduate coursework outside of mathematics, accounting, or statistics.
The Pearson Correlation is a test that looks at the strength of the association between two continuous, or normally distributed, variables.
The Spearman Correlation is another test that looks for the strength of association, but, unlike the Pearson Correlation it does not require that the variables be continuous; it can find the association between ordinal (or non-normally distributed) data. If you do not know whether your data is evenly or unevenly distributed, then you would select Spearman over Pearson.
The Chi-Square tests for the strength of the association between two categorical variables. Categorical variables are qualitative rather than quantitative. Examples of categorical variables would be eye color or dog breed.
Comparison of means tests look at the difference between the means of variables. While correlational tests try to establish how one variable is related to another variable, on an individual level, the comparison of means tests are used to look at how variables may impact groups.
The paired t-test looks at the difference between two related variables. These are often used for statistical analysis in experiments to compare the independent variable with the control group.
The independent t-test is used to test for the difference between two independent variables.
ANOVA tests test for the difference in the group after taking into account expected variance in the outcome.
Regression analysis is used to examine whether change in one variable can predict change in another variable. There are several different types of regression analysis, and the choice of which method to use is heavily dependent upon research design.
In simple regression, the test examines how change in the predictor (independent) variable predicts the level of change in the outcome (dependent) variable.
In multiple regression, there are not just two variables being examined. Instead, the test looks at how changes in the combination or two or more predictor variables predict the change in the outcome variable.
Non-parametric tests are used when the data is not normally distributed and does not meet the assumptions required for parametric testing. These tests are considered more difficult and are usually reserved for higher-level statistical analysis.
The Wilcoxon Rank-Sum Test is designed to look at the difference between two independent variables. It examines both the magnitude and the direction of the difference.
The Wilcoxon Rank-Sign Test is designed to look at the difference between two related variables, and also examines both the direction and magnitude of the difference.
The Sign Test is a simpler type of non-parametric analysis. It is designed to look at the difference between two related variables, but it only looks at the direction of the difference, not the magnitude.
While it is possible to hand-calculate some basis statistics and even products like Excel offer some fairly advanced statistical capabilities, once you get into complex statistics you need to be able to use some tools for statistical analysis. The most commonly used tools are SPSS, Stata, Minitab, and Excel.
SPSS is software that is specifically designed for statistical analysis. It is used for interactive or batched statistical analysis. It is one of the programs that is preferred by many colleges and universities, and you may be directed to use SPSS in your statistical analysis. It is relatively familiar for most users because it is very similar to Excel. However, SPSS can hold additional data information that Excel cannot, which can be crucial in data analysis and report. SPSS can send output to a separate window instead of being limited to same-worksheet calculations. SPSS does not require users to manually enter formulas for calculations. SPSS Syntax allows you to keep a record of all procedures. Finally, SPSS has a wider range of tools than Excel.
Stata is another software package specifically designed for data analysis, data management, and graphics. Oftentimes, the decision whether to use Stata or SPSS is dictated by the system used by your college or university. However, if you have the choice between statistical analysis tools, many users choose state because it has an easy to use point-and-click interface, can certify results, is set up to comply with FDA document guidelines, and is more intuitive for users.
Minitab began as a mini-version of OMNITAB, but this statistical analysis software quickly became capable of even very complex statistical analysis. Not as popular as SPSS with universities, Minitab is very popular with users because of the online tutorials that make it easier to use.
Finally, there is Excel. While many people do not think of Excel as statistical software, this spreadsheet workhorse is capable of handling basic statistical analysis. However, it was not designed to handle statistics and there are some areas where this is very noticeable. It is difficult to see how Excel is applying formulas, Excel does not handle missing data well, and it is difficult to discover mistakes in programming your analytics in Excel because calculations are not readily visible. Therefore, we do not suggest using Excel for complicated analytics.
The point of all of this analysis is to determine two things: is there a difference between results for different groups and, if so, is this difference statistically significant? When looking at two different groups, no matter how similar, one would expect to see a difference between them, regardless of the impact of any tested variables. Statistical significance tries to get at how much difference can be explained as the effect of chance. Difference that exceeds that amount is then considered statistically significant.
P-values are a tool that researchers use to determine whether results are statistically significant. P-values are the calculated probability of finding the observed results when the null hypothesis is true. In other words, the P-value looks at how likely it is that the results are random. P-value is also referred to as the probability value or asymptotic significance. There are different types of P values. One sided P values are used when a large change in an unexpected direction would not be relevant. This is unusual, so generally you use a two sided p value. P value is related to, but not the same as, significance level. The significance level, or alpha, refers to a pre-chosen probability. Your alternative hypothesis is the hypothesis you are actually investigating. If your P value is less than the significance level you have chosen, then you reject the null hypothesis and claim support for your alternative hypothesis. Traditionally, the significance level is 5%, but you can choose other levels; 1% and .1% significance levels makes it less likely that the results are random, but do not eliminate the chance that they are random, so they carry the risk of false confidence in the results.
If you are still confused about statistical analysis, take comfort in the fact that you are not alone. Statistics, math, and accounting are intimidating subjects for many students. Fortunately, expert help is at your fingertips. Our writers are well-versed in using statistical analysis in research projects and many of our writers come from backgrounds in statistics, math, and accounting. We can help you with any part of your project, demonstrating the right way to conduct a statistical analysis. In fact, we have been helping students improve their math skills since 1998.
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