We are thrilled to announce publication of the 2013 Edition of Dissertation and Scholarly Research: Recipes for Success. Our new edition offers fresh content and more than 200 new references from reliable sources. Each section of the book has been updated to provide practical, actionable guidance for every phase of dissertation development, and is supported by continuous updates and direct links to new resources on our companion website: www.dissertationrecipes.com.
Recipes is a bestselling guide to writing your dissertation (5 star ranking on Amazon). The 2013 edition of Recipes offers students and faculty a comprehensive, up-to-date, user-friendly text that explains clearly how scholarly research is created, evaluated and, disseminated. While the text has been extensively revised, some things have not changed. Understanding quality research to become excellent consumers and producers of research continues to be the focus, leading to in-depth understanding of academic inquiry. Using a workbook approach rich in tools, templates, frameworks, examples, and hard-won lessons from experience, Recipes continues to provide easy to navigate processes for crafting issues and ideas into research and results. Whether you are just considering doctoral study, already in a doctoral program, working to develop and complete your dissertation, or mentoring doctoral students, you will find Recipes a key ingredient in your success as a doctoral learner.
For regular visitors to dissertationrecipes.com, we are offering a discounted price on the new edition for purchases made from our publisher’ website. See the “Buy the Book” link on www.dissertationrecipes.com for details.
As always, we welcome your interest, comments, and suggestions.
What is power in statistics, and how does it relate to the size of my sample?
This is a common question we hear from doctoral students doing quantitative analysis in their research. They may know who they want to survey or interview, but selecting the size of the sample is a mystery. In this case, the tool to help you is power analysis.
Power analysis is a method to determine how large a sample is needed for statistical judgments that are accurate and reliable, and how likely the selected statistical test is to detect effects of a given size in a particular situation. We just added a new resource to our library that steps you through the basics and points you to additional references on this very useful statistical tool. You can find it here:
What is the difference between a statistically significant result and a meaningful result?
Most doctoral students are exposed to the standard canon of quantitative research methods – null hypothesis testing using statistical inference. Using a variety of different test methods, one can test hypotheses to determine whether or not relationships between variables exist, within a reasonable degree of error, and thereby whether or not a null hypothesis can be rejected or fail to be rejected.
While this sort of hypothesis testing is ubiquitous in research, a statistically significant result does not always equate with a meaningful result. Particularly in large samples, statistical significance in a tested relationship can be present even while the effects of the variables on each other are minor, even trivial. In such cases, we need a better approach to determine not just whether statistical significance is present, but whether the effects are sufficiently large to be important.
We have just added a resource to our “guides, tools, and worksheets” library that explores how measuring and reporting effect sizes can provide a stronger interpretation of a relationship between variables than the usual “p-value” approach in inferential statistics. You can find the discussion here: