Your research study will be due no later than the due date posted in Webcourses and will be submitted in Webcourses (listed as “Research Study”) by one member of the group.
1. To submit your study using Webcourses, link to “assignment” in Webcourses during the submission period. Click on “Research Study.” Upload your study and SPSS raw data file (not the output files) and submit (note: make sure that all files are uploaded before you submit). Again, only one person will submit the paper in the assignment dropbox–just make sure that names of all group participants are listed on the front page of the paper. Note:
o You can submit your research study any time within the timeframe listed in Webcourses. However, it MUST be submitted no later than the deadline.
o You can only submit your paper once (i.e., you can’t submit it before the due date, decide you want to change something, and submit again on the due date).
o If you are using data that you (or someone else) collected, you MUST submit a copy of the IRB approval letter on or before the deadline that the study is due. This can be submitted at the time you submit your research study or it can be submitted earlier. An electronic copy of the approval letter is preferred. Please remember that there are abundant secondary data resources available (that don’t require IRB) as well as wonderful opportunities for working with faculty members that have collected data.
o You must submit your research study as a Word file or PDF.
2. Your research study must also be submitted for review through turnitin.com. You will see this a report in the assignment submission with a score.
o Please don’t plagiarize or self-plagiarize (if you don’t know what self-plagiarism is, please take some time to find out and make sure to avoid it—this can occur when working on a topic that you’ve written papers on in the past)! Take a few minutes to review the resources on how to cite and quote properly (accessible from ‘resources’ on the course content home page). Remember that just including a reference may not be enough to ensure you haven’t plagiarized. You must also paraphrase and write in your own words. If in doubt, put it in quotes to indicate that someone else said it!!
Additional Tips and Instructions for Completing your Research Study
The following list of tips and instructions results from reviewing many research studies over many years. I realize the list of tips is lengthy, however the list has been generated based on what students have struggled with in the past. I encourage you to read the tips so that you can avoid pitfalls that others have experienced. Please do not hesitate to ask should you have questions.
1. Read and understand the rubric and the requirements listed. If you are unsure about anything, please do not hesitate to ask the professor.
2. Organize your paper. Liberally use headings and subheadings, and make it easy for the reader to find information. Using primary headings that correspond to the major headings in the rubric (introduction, methods, results, conclusion) and subheadings as needed beyond the primary headings may be helpful as you consider how to organize the paper.
Tips and Instructions for the Introduction and Methods Sections
3. You are NOT required to include a literature review and there is no requirement on how many, if any, references you include. As seen in the rubric, only five points of your total grade are allotted to “introductory material” that provides the framework of your study. Budget your time wisely, and concentrate your energy appropriately.
4. Research questions. Your research questions should be just that—questions (not statements)—and should reflect the inferential procedures that will be used to answer the questions. The PowerPoint slides have examples of questions and the book also has templates. If you report your research questions multiple times throughout your research study, please be consistent in how they are stated—wording of questions early in the paper should be the same later in the paper. If you have questions, please ask.
5. All studies will have some type of instrumentation even if you are using data that has already been collected. For example, if you are using test scores, include a section on instrumentation that talks about what the test is, what it measures, the types of items, the number of items, etc. If you are using a secondary dataset, how was the information previously collected (what website or other public source did the information come from, did the researchers use a survey, etc.)? If you have questions, please ask.
6. Operational definitions. It is sometimes difficult to know which terms to operationally define. Concentrate on your research questions and pull out terms in the research question that need to be operationally defined. For many studies, many terms (beyond those presented in the research questions) are important and could potentially be defined. For the purposes of this assignment, please concentrate on operationally defining only the terms in your research questions. If you have questions, please ask.
7. Research design. All studies will have a research design (e.g., experimental or non-experimental design), even if you did not collect the data. This component pulls from your research methods class, and if you have trouble determining what your design is, please ask.
8. Sampling method. All studies will have a sampling method, even if you did not collect the data. Some studies may have collected data from a population rather than a sample. If so, indicate that you have population level data. If your data was collected on a sample of people from a larger population, indicate what sampling method was used (e.g., convenience, simple random sample, stratified sample…). If you have questions on this for your particular study, please ask.
Tips and Instructions for the Descriptive and Inferential Results Sections
9. Measurement scales. Understand the measurement scales of the variables in your dataset. Share your data with the professor and/or arrange a face-to-face meeting to discuss the data and the measurement scales. Understanding the measurement scale of your variables is essential for computing every statistic (either descriptive or inferential). By nature of the criteria in the rubric, using the incorrect measurement scale will result in a loss of points, and you don’t want that to happen.
10. Selecting variables for descriptive stats. You are welcome to use as many different variables as you choose when generating your descriptive statistics. However, when interpreting any one descriptive statistic, you need to interpret information from just one Comparisons of variables from different years (e.g., how cumulative GPA has changed from 2010 to 2012) are fine, but to be eligible to earn all points, there should also be interpretation of just one. This is because you will not always have two variables for which you can compare results. It’s important to master your skills in making interpretations without comparisons between variables.
11. Using statistical software to generate output. Although all of the results that are presented can be calculated by hand, another purpose of this study is to allow you to practice skills in using statistical software and interpreting the results from that software. In reality, journal editors and reviewers (as well we theses or dissertation committee members) will be expecting to see that you can use statistical software to create figures, charts, graphs, as well as compute other descriptive and inferential statistics—thus I want you to practice this and get more comfortable with using software for stats. Therefore, the results that are presented for both the descriptive and inferential stats should be generated from a statistical software package (e.g., SPSS). When you interpret the inferential procedures, there should not be reference to critical values—you should be reviewing the p value in relation to your alpha level to make a decision on whether to reject or fail to reject the null hypothesis.
12. Interpreting descriptive results. Please interpret your descriptive data rather than just restate the information. An example of restating only would be: “The mean was 15 and the median was 8.” Interpretation would be, for example, “the location of the mean relative to the median suggests the distribution may be positively skewed,” or applying the Empirical Rule to interpret the mean and standard deviation.
13. Interpreting and reporting descriptive and inferential. The descriptive statistics should be reported and interpreted separately from the inferential statistics. As seen in the rubric, the “RESULTS” section is split into two parts: 1) descriptive statistics; and 2) inferential statistics. Your research study should have two very clearly delineated sections under your “results” heading—one that reports results of descriptive statistics and one that reports inferential statistics. You are welcome to include charts and graphs along with your results for your inferential procedures, however any charts and graphs that are included as pieces of the inferential statistics procedures will NOT be graded as descriptive statistics. Why is this important? It’s important to be able to use descriptive statistics independently of inferential statistics so you can do just that—describe what’s going on and separate that from tests of inference. For example, many research studies use descriptive statistics to report and describe characteristics of the sample (e.g., reporting average age, number of male and female participants, and similar descriptive statistics). The variables that they use to describe the sample may or may not have anything to do with the variables included in the inferential statistics.
14. Selecting variables for inferential statistics. You are not required to use the same variables for the inferential statistics as those that were used for descriptive statistics. You are welcome to do so, however it is not required that any variables used for the descriptive statistics be used for the inferential statistics (and vice versa). Keep in mind also that if you are using secondary data, it may or may not have the variables of interest included in the data file. You may not always be able to ‘connect’ the pieces of your research study. Please remember that the purpose of the research study is to provide the opportunity to practice and showcase your skills in using the skills learned throughout the semester, not to write a Nobel prize winning piece of research (although that’s great if that happens!). When your research study is being graded, you will be graded on how well you applied your skill (e.g., used appropriate variables, computed appropriate statistics, interpreted correctly the results, etc.)—NOT how well you were able to help the reader understand how the results that you found from your frequency table relate to the results you found in your boxplot, etc.
15. **Inferential procedures. You will compute, report, and interpret results from two different inferential procedures. The inferential procedures that can be used include: a) one sample t test; b) independent t test; c) dependent t test; d) chi square goodness of fit test; e) chi square test of association; f) correlation (there are a number of different types of correlation coefficients covered—please see the respective module for which correlation is appropriate given the measurement scale of your variable).
16. Hypothesis decisions. When you interpret the inferential procedures, there should not be reference to critical values—you should be reviewing the p value in relation to your alpha level to make a decision on whether to reject or fail to reject the null hypothesis.
17. Interpreting inferential statistics. Copying and pasting output from SPSS is fine, however don’t forget to show off your skills in interpreting those results. For the inferential stats, for example, use the narrative to explain what you did and what you found. Including in your narrative the test statistic value, degrees of freedom (or sample size depending the statistical procedure), p value, and correct interpretation of it as well as post hoc power analysis are all important pieces as is evidence of testing the assumptions. The PowerPoint slides for the different inferential procedures have examples that may be helpful, and you are welcome to use those as models in writing up your results.
General Tips and Instructions
18. APA formatting. APA formatting will be used for creating the cover page, formatting page numbers and running head, formatting headings and subheadings, and citing references (within the manuscript and in the reference page). UAny graphs, figures, charts, or tables should be referenced in the narrative and should be included in the narrative where it is discussed and interpreted rather than an appendix.
19. Plagiarism and self-plagiarism. Review the resources within Webcourses for how to avoid plagiarism and understand the consequences for this. Self-plagiarism should be avoided. While you are encouraged to build on knowledge learned from papers submitted in previous classes you have attended, please do not copy from those papers (i.e., self-plagiarism). The example proposals available to you in Webcourses (or papers submitted by peers in previous semesters) are there for informative purposes only and copying or duplicating material from them is considered plagiarism.
20. Posting summary and response. The last page of the rubric provides instructions on posting a summary and responding to summaries that are posted by your peers. If you are working as a team, only one summary of your research study needs to be posted on behalf of your team. However, both people on the team will make individual posts to the summaries of two peers.
21. When to begin your research study. By the time you take the midterm, you will have all the knowledge you need to complete everything in the research study up to the inferential statistics. Why not get started early and alleviate that end of semester stress?
22. At any point you are welcome (and encouraged) to share what you have done and ask specific questions. There is no formative grading, however the professor is happy to field specific questions.