Writing Homework Help

In this section of your AR report you will summarize the results of your data analyses.

Specific Academic Writing Guidelines for the Results Section:

This section of the report should:

Not include raw data. Only include the results of the analyses.

Not include identifying information, such as student, school, or district names.

Use neutral, unbiased language.

Use research terminology (e.g., mean rather than average).

Not make any claims or draw conclusions about the data. (Save that for the Conclusions section)

• Be written in past tense.
• Explain the results of the analyses in both narrative and with tables/figures. However, don’t duplicate information.  The narrative and graphics should work together to illustrate the results.
• Use APA 7th ed. format for student paper submissions.  Check the APA Resources Module for rules about formatting tables and figures in APA.
• Organization and Specific Instructions:

Begin with an introductory paragraph that

Restates the research question or purpose statement.

• Makes a simple connection between the research question and analyses performed.
• Drawing that connection should lead into a preview for the Results section.

If you included more than one research question, address just the first question. Then, return to this step and begin again, using your second research question.

• Summarize the descriptive analyses

Descriptive analyses are required for all AR studies in this course, even if the data collected is qualitative.

• Include all three aspects of descriptive statistics* (or as directed by your professor): central tendency, dispersion, frequency. Suggestions include:

If Using Survey Data: Follow the direction* provided by your professor. Report out mean, percentages, and/or frequencies.

If Using Pre-Test Data:

Review the mean, median, and modal scores for your pre-test data. Which score(s) best describe how your students were performing? For example, for your data, is it more helpful to say that the mean (i.e., average) number of homework assignments submitted was 3.7, or that most students submitted 6 assignments (which is the modal score)? If you can make an important or interesting comment about two, or even all three measures, include them all!  

1. Review measures of dispersion. In other words, how much did your students vary in their performance?  Was the lowest score 35 and the highest score 98, suggesting a great deal of variability, or did everyone score between 72 and 85, suggesting they were fairly similar? If you are familiar with standard deviation and variance, describe one or both of these analyses as well. 

Review the frequency distribution for your pre-test data. What important or interesting trends do you see? If needed, consider regrouping your data.  For example, if you divided pre-test scores into groups of ten (e.g., the number of students scoring from 51 – 60, 61 – 70, etc.) and don’t see a trend, what would happen if you grouped your data by groups of 20 (e.g., 51 – 60, 61-80, and 81-100)?  Describe the frequency distribution(s) in narrative and with a table or histogram.

Frequency distributions are a great way to describe qualitative data. For example, if you analyzed interview data according to five different codes, you could report the frequency of responses that fell under each code.  For example, you might say that six students made a comment about not having enough time to complete homework assignments, four students made a comment about…. As another example, you might say that within all the interviews there were 53 comments about not having enough time, 40 comments about….  This frequency data can be described in narrative and with a table or histogram. 

1. Quantitative surveys (e.g., a Likert-style questionnaire) should be summarized descriptively as well. First, describe patterns for the entire survey. For example, you might say, the mean score for the entire survey was 4.2, with 1 corresponding to Strongly Disagree and 5 corresponding to Strongly Agree.  Then, describe patterns you see for individual items.  For example, you might say something like: The lowest scoring item was Homework helps me prepare for tests, with a mean of 2.3 and mode of 2. 
2. If Using Post-Test Data
3. Same as above
4. For any data set, when you make comparisons…
5. Highlight comparisons between pre and post data if you are looking at test scores. Bar charts might be especially helpful, but also include a sentence or two of commentary.  (Don’t include a table or figure without discussing it).

Highlight comparisons between groups of students [or teachers] (e.g. the control and experimental groups; boys and girls; native English speakers and English Language Learners; struggling students, on-target students, and advanced students (many times struggling students will show more or less growth than advanced students), and so on. Bar charts and pie charts might be helpful to illustrate your comparisons, but again, include commentary as well. 

Summarize additional analyses needed for your study, one at a time. Here are a few resources:

1. General Guidelines: https://www.statisticshowto.datasciencecentral.com/reporting-statistics-apa-style/ (Links to an external site.)

Directions for reporting a t-test: http://www.csic.cornell.edu/Elrod/t-test/reporting-t-test.html (Links to an external site.)

1. Example statement to summarize t-test results:

“A paired-samples t-test was conducted to compare hours of sleep in caffeine and no caffeine conditions. There was a significant difference in the scores for caffeine (M=5.4, SD=1.14) and no caffeine (M=9.4, SD=1.14) conditions; p = 0.005. alpha = 0.05.

Here is an additional model:

1. A {paired} {two-sample} t-test compared the mean for student scores on {the pretest} {the XYZ assessment for the control group} with the mean for student scores on {the post-test} {the XYZ assessment for the experimental group}.  As can be seen in Table # below, the t-test produced a p-value of {#####} which {is} {is not} significant at an alpha level of 0.05.  Thus, the difference in mean scores for the {pretest and posttest} { control and experimental groups] {is} {is not} statistically significant. 

Remember, don’t make a claim yet.  Just report the results of the t-test and whether or not that result is significant.  In the Conclusions section you will discuss the implications of that significant or non-significant result.

1. Remember, self-evaluate your Results section using the grading rubric!

HERE (Links to an external site.) is a website that does a nice job of explaining what the various descriptive statistics results mean.