The post The Importance of Research Part 1: The Research Problem and Background appeared first on Statistics Solutions.

]]>First, let’s discuss the Research Problem. The Research Problem drives a study, and targeted, in-depth research is needed to fully develop the Research Problem. Research is important here because you need to know what researchers have done in the area on the topic to help set up and define a problem that exists in the research. You must answer the question, “what do we already know from the research?” to set up the issue of what we do not know. There is no magic number for how many sources it takes to show what is known from the research. Instead, you must make a convincing and comprehensive display of what is known based on the existing research. This helps to highlight what is not known and to adequately define the Research Problem. I often see students unable to adequately set up their Research Problem because they have not sufficiently researched their topics. They cannot convincingly show what is known about the topic and rush to the conclusion that there is little research in this area.

The Background also relies on sound research and a comprehensive approach. Whereas the Research Problem is designed to set up what is not known (i.e., a problem) in the research, the Background gives the reader a broad and often chronological view of the topic and the research that has been conducted on it to the present. Obviously, you cannot cover all the research on a topic in the Background, and you are not expected to. What is expected is that you cover major trends in the research and major conclusions, as well as capture how research on the topic has evolved over time. This leads the reader to the current state of the research and, ideally, to the Research Problem and the need for your study.

This blog only reiterates the importance of research. It does not provide information on how to conduct it. However, do not cut corners when conducting research, because it may cost you more time in the long run by having to go back and fill in gaps. Additionally, if you are not a strong researcher or if you have questions about or need help conducting research, ask a research librarian at your school for help. Part 2 of this blog covers the importance of research to the Literature Review and the Discussion.

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]]>The post Love Your Dissertation appeared first on Statistics Solutions.

]]>**Incentivize Daily Writing Time**

It’s no secret that consistent writing leads to dissertation progress. Each day, you should be sitting down to write for at least 30 minutes. Start anywhere, write anything. This will help creative juices to start to flow and before you know it, 30 minutes will turn into 60 and that will turn into a completed chapter. To get yourself in a writing routine, use an incentive to motivate a daily writing routine. For example, reward yourself with 10 minutes of leisure TV time for every 30 minutes spent writing.

**Verbalize Your Writers Block**

Remember that feeling of excitement you used to get every time someone asked about your dissertation topic? Locate that feeling and talk about it! Speak with your colleagues, mentors, friends, and family – reconnect with the excitement you feel for the subject and your motivation won’t be far behind.

**Recognize Your Effort**

Understand that the dissertation process can be isolating and lonely and leave you feeling overwhelmed at times. Expecting rejection, committee feedback, and the occasional chapter rewrite will allow you to not get discouraged when you experience a set-back. Instead, recognize how hard you’ve worked and be proud of the progress you’ve made. There is a clear finish line and you can reach it with dedication and consistency.

We hope February’s Newsletter gets you in the mood to create, write, and make dissertation progress!

Best,

The Statistics Solutions Team

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]]>The post Reject the Null or Accept the Alternative? Semantics of Statistical Hypothesis Testing appeared first on Statistics Solutions.

]]>Let’s say, for example, that you were conducting a study with the following research question: “is there a difference in the IQs of arts majors and science majors?” The null hypothesis would state that there is no difference between the variables that you are testing (e.g., “there is no difference in the IQs of arts majors and science majors”). The alternative hypothesis would state that there is a difference (e.g., “there is a difference in the IQs of arts majors and science majors”). Typically, the researcher constructs these hypotheses with the expectation (based on the literature and theories in their field of study) that their findings will contradict the null hypothesis, and in turn support the alternative hypothesis. For instance, in our IQ example we may expect to see a difference between arts majors and science majors. Generally, it is difficult to justify conducting a study if you have no reason to believe that differences or relationships exist between your variables. Thus, studies are set up to provide evidence that the null hypothesis is “wrong,” and that the alternative hypothesis is “correct.”

Setting up the null and alternative hypotheses is usually a pretty simple task. However, students often run into trouble when they finish their analysis and must present their results using the “null/alternative” language. Confusion may arise over what words to use and how statements should be phrased. For your dissertation, some of this may come down to your reviewers’ preferences. However, below are some basic guidelines you may follow.

First, let’s assume you ran your analysis and your results were significant (e.g., arts majors and science majors had different IQ levels). In this case, it is generally appropriate to say “the null hypothesis was rejected” because you found evidence against the null hypothesis. This statement is often sufficient, but sometimes reviewers want you to go further and also make a statement about the alternative hypothesis. In this case, you could say “the alternative hypothesis was supported.” Personally, I would **avoid saying** “the alternative hypothesis was *accepted*” because this implies that you have proven the alternative hypothesis to be true. Generally, one study cannot “prove” anything, but it can provide evidence for (or against) a hypothesis. Additionally, the concept of challenging or “falsifying” a hypothesis is stronger than “proving” a hypothesis (for more in-depth discussion on this philosophy of science see Popper, 1959). Again, it is worth noting that your reviewers may have different preferences on the exact language to use here.

Now let’s consider the flip side and assume your results were not significant (e.g., there was no significant difference in IQ between arts majors and science majors). Here you could say “the null hypothesis was not rejected” or “failed to reject the null hypothesis” because you did not find evidence against the null hypothesis. You should **NOT say** “the null hypothesis was *accepted*.” Your study is not designed to “prove” the null hypothesis (or the alternative hypothesis, for that matter). Rather, your study is designed to challenge or “reject” the null hypothesis. People often compare this idea in statistical hypothesis testing to how verdicts are made in criminal court cases. If the prosecution does not have strong enough evidence that the defendant committed the crime, the defendant is judged as “not guilty” rather than as “innocent.” In other words, the court can provide evidence of guilt, but it cannot prove innocence. In the same way, a statistical test cannot prove the null hypothesis, but it can provide evidence against it. As for the alternative hypothesis, it may be appropriate to say “the alternative hypothesis was not supported” but you should **avoid saying** “the alternative hypothesis was *rejected*.” Once again, this is because your study is designed to reject the null hypothesis, not to reject the alternative hypothesis.

These are just some general tips to help guide the writing of your statistical findings. However, always defer to the requirements of your reviewers and your school when in doubt.

**References**

Popper, K. (1959). *The logic of scientific discovery*. London: Hutchinson.

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]]>The post The Chi-Square Test in Structural Equation Modeling appeared first on Statistics Solutions.

]]>The chi-square test is unique among possible the measures of fit in SEM because it is a test of statistical significance. The chi-square value and model degrees of freedom can be used to calculate a *p*-value (done automatically by most SEM software). This tests the null hypothesis that the predicted model and observed data are equal. Because you want your predictions to match the actual data as closely as possible, you do not want to reject this null hypothesis. In other words, a nonsignificant result for this test indicates good model fit.

This differs from virtually all other measures of model fit, which consist of a single value that must be compared against a cutoff or benchmark that has been established by statistics scholars. For example, a typical benchmark for the comparative fit index (CFI) is .90, meaning that values of .90 or greater indicate good fit, and values less than .90 indicate poor fit. The problem here is that these benchmarks are not all universally agreed upon. For the CFI, some scholars suggest a benchmark of .90 (e.g., Schumacker & Lomax, 2010), but others may suggest a stricter benchmark of .95 (e.g., Hu & Bentler, 1999).

Given the subjectivity of evaluating fit based on benchmarks, it may seem like the chi-square test should be the most objective and useful metric. However, this is not the case. In fact, the chi-square test may actually be the LEAST useful metric for model fit. The reason why the chi-square test is not very useful is because of its sensitivity to sample size. The larger the sample size, the greater the chances of obtaining a statistically significant chi-square. And given that most scholars agree that SEM should only be conducted with large sample sizes (usually meaning hundreds of participants), the chi-square test is all but guaranteed to be significant, even at higher significance cutoffs (e.g., .01 or .001). Because the chi-square test will be significant no matter what, it does not provide any useful information, and other measures of fit need to be considered.

As a final note, it is worth mentioning that the chi-square statistic itself (along with its degrees of freedom) can be a useful measure of model fit; it is just the significance test that ends up being useless. Some scholars recommend using the chi-square divided by the degrees of freedom (χ^{2}/*df*) as a measure of model fit, with values of 5 or less being a common benchmark.

**References**

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. *Structural Equation Modeling, 6*, 1-55.

Schumacker, R. E., & Lomax, R. G. (2010). *A beginner’s guide to structural equation modeling* (3rd ed.). New York, NY: Routledge Academic.

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]]>The post Testing Normality in Structural Equation Modeling appeared first on Statistics Solutions.

]]>First, it is possible to test for multivariate normality using a quantile (Q-Q) or probability (P-P) plot, which can be done though the Analyze > Descriptive Statistics menu in SPSS (see our previous blog on this topic for more details). Similarly, you can conduct a quantile plot of Mahalanobis distances to test for normality (the steps for calculating Mahalanobis distances in SPSS are outlined here). If you use Intellectus Statistics to conduct your analysis, the Mahalanobis distances method will automatically be performed for you. For all of these methods, a plot is produced, and the points on that plot should follow a relatively straight line. Marked deviations from a straight line suggest that the data are not multivariate normal.

If you are conducting your analysis in AMOS, the built-in test for normality involves the calculation of Mardia’s coefficient, which is a multivariate measure of kurtosis. AMOS will provide this coefficient and a corresponding “critical value” which can be interpreted as a significance test (a critical value of 1.96 corresponds to a *p*-value of .05). If Mardia’s coefficient is significant, (i.e., the critical ratio is greater than 1.96 in magnitude) the data may not be normally distributed. However, this significance test on its own is not a practical assessment of normality, especially in SEM. This is because tests such as these are highly sensitive to sample size, with larger sample sizes being more likely to produce significant (non-normal) results. In SEM, where your sample size is expected to be very large, this means that Mardia’s coefficient is almost always guaranteed to be significant. Thus, the significance test on its own does not provide very useful information. In light of this, it is recommended that the significance tests be used in conjunction with descriptive statistics, namely the kurtosis values for individual variables (Stevens, 2009). Kurtosis values greater than 3.00 in magnitude may indicate that a variable is not normally distributed (Westfall & Henning, 2013).

There are many ways to test for normality, and these are just a few of the most popular methods used in support of SEM analysis.

**References**

Stevens, J. P. (2009). *Applied multivariate statistics for the social sciences* (5th ed.). Mahwah, NJ: Routledge Academic.

Westfall, P. H., & Henning, K. S. S. (2013). *Texts in statistical science: Understanding advanced statistical methods.* Boca Raton, FL: Taylor & Francis.

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]]>The post How to determine the appropriate sample size for structural equation modeling appeared first on Statistics Solutions.

]]>Most researchers agree that SEM requires “large” sample sizes, but what exactly does this mean? A number that gets tossed around a lot is 300 (see Comrey & Lee, 2013; Tabachnick & Fidell, 2013), but a one-size-fits-all answer like this probably will not fly with most reviewers. As there is no single correct or universally-accepted calculation or method for determining sample size for SEM, researchers and students alike often rely on “rules of thumb.” For instance, some statistics scholars have recommended using the ratio of observations to estimated parameters (*N*:*q*) as a guide. Specifically, Kline (2015) recommended that the *N:q* ratio should be 20 to 1, or 20 observations (participants) for each estimated parameter in the model. Others have suggested that the *N:q* ratio can be as low as 10 to 1 (Schreiber et al., 2006) or 5 to 1 (Bentler & Chou, 1987). Clearly, there is a lot of variance and uncertainty even in guidelines proposed by SEM scholars.

So, what if your reviewers require some kind of hard calculation (rather than rules of thumb) to determine your sample size? There are some easy-to-use online tools that have academic support (for an example see Daniel Soper’s sample size calculation tool based on the work of Westland, 2010). However, Monte Carlo simulation is becoming an increasingly preferred method (for an in-depth discussion, see Wolf et al., 2013). In short, the Monte Carlo simulation method allows you to construct a model to your exact specifications and then test the model on thousands of “random” datasets of varying sample sizes. This lets you see approximately how often the effects in your model will be significant (i.e., statistical power) in a sample of any given size. The main advantage of this method is that it allows you to determine an appropriate sample size for the specific model you are testing. However, this method requires a high level of expertise in specific statistical software (such as Mplus) to conduct properly. Keep an eye out for future blogs where we may cover Monte Carlo methods in more detail!

**References**

Bentler, P. M., & Chou, C. P. (1987). Practical issues in structural modeling. *Sociological Methods & Research, 16*(1), 78-117.

Comrey, A. L., & Lee, H. B. (2013). *A first course in factor analysis*. Psychology Press.

Kline, R. B. (2015). *Principles and practice of structural equation modeling*. Guilford publications.

Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. *The Journal of Educational Research, 99*(6), 323-338.

Soper, D.S. (2018). A-priori Sample Size Calculator for Structural Equation Models [Software]. Available from http://www.danielsoper.com/statcalc

Tabachnick, B. & Fidell, L. (2013). *Using multivariate statistics.* Boston: Pearson Education.

Westland, J.C. (2010). Lower bounds on sample size in structural equation modeling. *Electronic Commerce Research and Applications, 9*(6), 476-487.

Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample size requirements for structural equation models: An evaluation of power, bias, and solution propriety. *Educational and Psychological Measurement*, *73*(6), 913-934.

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]]>The post Dominate Your Dissertation in 2020 appeared first on Statistics Solutions.

]]>Inside Higher ED says setting deadlines early in the dissertation process is imperative to sustaining long-term motivation but making sure those deadlines are doable and flexible is key. Ask for feedback early on because the sooner you communicate with your committee about your work, the smoother collaboration and editing will be. Finally, remember that your dissertation is *your* work and you are the expert but it’s crucial to find out what your committee wants and expects from your work so open lines of communication are critical.

Before you dive into your dissertation this year, ask yourself how your journey can improve. What would help you complete your dissertation and how can you make that happen? The journey to completing your degree starts today, with the help of our dedicated team of dissertation experts.

*What is dissertation assistance?*

Dissertation assistance comes in the form of keeping you engaged and motivated to write with the backing and assistance of a mentor. Not only will our mentors help shape your chapter(s) until they’re submission-ready, but they will also help you address feedback so you can rest assured that there won’t be any surprise roadblocks along the way.

*How do I get dissertation assistance?*

The first step to getting dissertation assistance is to schedule your free initial dissertation consultation. During our call, we’ll discuss your project needs, talk about turn-around times, and project assistance cost. Scheduling your appointment is quick and easy! Simply select a date and time using the calendar to your right to reserve your spot.

The post Dominate Your Dissertation in 2020 appeared first on Statistics Solutions.

]]>The post Graduate Next Year appeared first on Statistics Solutions.

]]>**Find Support**

If your 2019 New Year’s resolution was to finally complete your dissertation and you’re still stuck in a loop of countless revisions, it’s time to set a resolution that will actually stick. The American Psychological Association suggests that asking for support is key to ensuring one sticks with a New Year’s goal. With dissertation consulting from Statistics Solutions you’ll not only receive a team of seasoned mentors capable of helping you through each dissertation phase, but you’ll also receive friendly and consistent reminders to stay on track.

**Break it Down**

Harvard Medical School suggests breaking big New Year’s goals into small, manageable steps in order to not get discouraged and quit early on. For example, if the goal is to move onto the results chapter but you don’t know where to start – finding tutoring around that section is a good place. Search for and join free webinars or tutoring sessions on campus. We offer free Qualitative Methodology and Results Help Sessions along with free Quantitative Results Help Sessions online, and once you’re ready to take the plunge, our Accelerated Quantitative Results program can provide a draft of your chapter 4 in just 1-hour.

**Reward Yourself**

The same article from Harvard Medical also suggests that it’s important to give yourself recognition for healthy changes, even if you haven’t reached the end goal yet. If you sit down to write consistently, be proud of yourself (even if the chapter isn’t perfect yet)! Look for support from your peers and loved ones. Stay motivated and proud of yourself.

**Learn from the Past**

If last December your goal was to finish your dissertation and graduate in 2019, do something different in 2020. Reach out for help, change up your writing routine, explore online tutorials and webinars – Make 2020 your year of successful change. Make 2020 your graduating year! You’ve got this, we believe in you.

*Looking to make a positive change now?*

**Schedule Your Consulting Appointment**

Link to APA article: https://www.apa.org/helpcenter/resolution

Link to Harvard Medical School article: https://www.health.harvard.edu/staying-healthy/seven-steps-for-making-your-new-years-resolutions-stick

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]]>The post Turn Rejection into Motivation appeared first on Statistics Solutions.

]]>The 5 professors that made up her defense committee found her approach to her failures refreshing and humorous, while Kirby described the process of digging up the rejection letters and putting the skirt together as cathartic and therapeutic. How will you feel about your rejections on your defense day? Failure is necessary for growth and some can even use rejection as a motivator. As a society, many of us have become so used to instant gratification and persevering through a grueling processes such as getting your PhD can, at times, seem impossible. Nothing worthwhile comes easy and, by this point you’ve already overcome the rejection letters, so we are here to motivate you, and help you continue to persevere towards your goal of completing your dissertation.

Ultimately, going for your dreams of becoming a PhD gives you the opportunity to face your fears and see how strongly you persevere. Throughout the dissertation process, there will be a lot of rejections and it takes guts and confidence to carry on. Having a dedicated team of dissertation professionals keeps the pressure from being completely on your shoulders, so if you’ve been struggling, reach out and learn about the resources available to you. What have been some of the toughest rejections you’ve experienced on your dissertation journey thus far?

Link to the original article.

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