| Course | PHLT 8032 SPSS Revealed |
|---|---|
| Week | Week 6 |
| Paper type | Peer replies |
| Length | About 520 words, 3 pages |
| Format | Discussion reply, APA 7 citations |
| School | Walden University |
| Program | PhD in Public Health |
| Updated | October 2026 |
Free sample paper for PHLT 8032 Week 6
Week 6 Discussion: Responses to Two Classmates
Two Peer Replies on Reading the Right Row: A Linear-by-Linear Result Quoted for Influenza Vaccination by District, and a BMI Comparison Whose Case Processing Summary Shows Many Respondents Left Out
Reply to Dana
Dana wrote: "Flu vaccination differs significantly by district, Linear-by-Linear Association = 6.84, p = .009."
Dana, your crosstab is clear, and putting row percentages beside the counts makes it easy to see that vaccination is highest in Central and lowest in River. My question is about the row you quoted. The Linear-by-Linear Association in the Chi-Square Tests table treats the row variable's codes as ordered and asks whether vaccination rises or falls steadily from code 1 to code 4. Our district variable uses the codes 1 to 4 for North, Central, River and South, and in Week 2 we all set it as nominal, the level Stevens (1946) reserved for categories whose numbers only name. If the county had numbered the districts in a different order, the linear-by-linear value would change while the vaccination rates stayed exactly the same, which suggests it is not answering your question. The Pearson Chi-Square row tests whether vaccination differs across the four districts without assuming any order, and that matches what you wrote in your opening sentence. Field (2018) notes that SPSS prints every row by default and leaves the choice to the analyst. Does your conclusion hold with the Pearson row? It may well, given the spread in your percentages, and the footnote beneath the table will also tell you whether any expected counts fall below 5.
Reply to Marcus
Marcus wrote: "Current smokers had a lower mean BMI than never smokers, F(2, 1,183) = 5.12, p = .006."
Marcus, comparing BMI across smoking groups is a good use of this week's procedures, and reporting the degrees of freedom made one thing visible that I think is worth a sentence in your post. Your error degrees of freedom imply about 1,186 respondents in the analysis, and your Case Processing Summary shows 1,186 included out of 1,845, so roughly 36% of respondents were left out. That is far more than the 183 respondents the cleaning log left without a valid BMI. One likely cause is the One-Way ANOVA dialog: if BMI, activity minutes and income were entered together as dependent variables with the listwise option ticked under Options, anyone missing any one of the three drops out of every result, including the BMI comparison. Income alone was refused by 164 respondents, and refusal was more common among adults 65 and older. If that is what happened, the comparison now describes a younger group than the survey sample, and a group chosen by a rule that has nothing to do with smoking or weight. Sterne et al. (2009) point out that an analysis restricted to complete cases is unbiased only when the missing answers are unrelated to the outcome, which is hard to argue for body measures. Could you rerun with analysis-by-analysis exclusion, then add one sentence stating how many respondents the BMI comparison excludes and why? That would let readers judge whose data your F test actually describes.
References
Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5th ed.). SAGE.
Sterne, J. A. C., White, I. R., Carlin, J. B., Spratt, M., Royston, P., Kenward, M. G., Wood, A. M., & Carpenter, J. R. (2009). Multiple imputation for missing data in epidemiological and clinical research: Potential and pitfalls. BMJ, 338, Article b2393. https://doi.org/10.1136/bmj.b2393
Stevens, S. S. (1946). On the theory of scales of measurement. Science, 103(2684), 677-680. https://doi.org/10.1126/science.103.2684.677
The Walden PHLT 8032 Week 6 instructions
The sixth discussion usually asks students to respond to at least two classmates' posts about their output. Faculty look for engagement with the specific analysis, not general encouragement. A strong reply identifies one issue in the classmate's output, explains what the relevant row or table assumes, and asks a question that lets the classmate check it. Quoting the claim being discussed keeps the reply anchored. Many prompts expect one scholarly source per reply, and the source should support the point being made rather than decorate a compliment. Tone matters: a question that leaves room for a reason the replier did not know about reads as collegial, while a verdict without reasoning reads as criticism. Each reply typically runs about two hundred words, and the two should address different issues.
How the PHLT 8032 Week 6 example is put together
Each reply begins with a short quotation of the classmate's reported result. The first reply praises the row percentages, then explains that the Linear-by-Linear Association asks whether vaccination rises steadily from district code 1 to code 4, which has no meaning for nominal districts, and that renumbering the districts would change the statistic while the rates stayed the same. It points to the Pearson row and the expected-count footnote. The second reply notes that the error degrees of freedom imply 1,186 respondents, far below the 183 the cleaning log left without a BMI, and suggests that a dialog excluding cases listwise across BMI, activity and income removed them. It cites the conditions under which complete-case results are unbiased and asks for a rerun with analysis-by-analysis exclusion.
Reading the PHLT 8032 Week 6 rubric
Replies earn credit when they engage a specific, checkable feature of the classmate's work. A reply that names the exact output row, explains what it assumes and invites the classmate to compare it with the right row scores well; general praise or a vague suggestion to check assumptions scores poorly. Graders look for accuracy in the statistical point itself, so the explanation of why an ordered test does not suit a nominal variable must be right. The second reply is read for whether it connects an excluded count to who was excluded, which is the interpretive point. A relevant source cited in each reply supports the participation grade, and tone is part of it too: questions that leave room for the classmate's reasoning are rewarded over corrections delivered as verdicts.
PHLT 8032 Week 6 help from the desk
Peer replies fall flat when they compliment the post and add a general tip. Pick one feature of the classmate's output that affects what the result means, such as which row of a test table was quoted or how many cases were dropped. Quote their claim, credit what is done well, then explain in two or three sentences what the issue assumes and why it matters here. Ask a question they can answer by looking at their own output. Cite a source that supports that specific point. Keep each reply to one main issue, and make the two replies address different ones so the thread gains two distinct contributions. Responses to classmates in your own section's discussion can be drafted to this model.
Get a PHLT 8032 Week 6 example written to your instructions
Paste in the Week 6 discussion prompt, the rubric, and the classmate posts you are replying to, with names removed. Two replies, each anchored to a specific row or table, come back in 24-48h, free as a first request. When a post has no output attached, say what it reported and the replies work from that.
More PHLT 8032 papers and PhD in Public Health samples
- PHLT 8032 Week 1: SPSS Setup Walkthrough
- PHLT 8032 Week 2: Variable Definition Paper
- PHLT 8032 Week 3: Data Cleaning Log
- PHLT 8032 Week 4: Descriptive Statistics Write-Up
- PHLT 8032 Week 5: Chart Rationale Paper
- PHLT 8032 Week 7: Mann-Whitney Procedure Write-Up
- PHLT 8032 Week 8: Tau-b Association Run
- PHLT 8032 Week 9: APA Results Tables
- PHLT 8032 Week 10: SPSS Syntax Audit
- PHLT 8032 Week 11: Final Analysis Report
- PHLT 8006 Week 6: Seven-Study Evidence Table
- HLTH 9101 Week 6: Key Term Audit
- HLTH 8815 Week 6: Aging Framing Critique
- HLTH 8745 Week 6: Cardiac Depression Peer Reply
PHLT 8032 Week 6 questions, answered
Should a peer reply point out an error directly?
It can, but a question usually works better and grades better. Asking why a particular row was chosen lets the classmate find the problem, and it leaves room for a reason you did not know about. If the error is clear, the reply can say what the row assumes and let that speak. Declaring the analysis wrong, without the reasoning, tends to read as a verdict rather than engagement.
Do replies need their own citations?
Usually. Many sections expect one scholarly source per reply, and nearly all expect at least one somewhere in the thread. The source should support the specific point being made, such as what a row of the output assumes. A citation attached to a general compliment adds length without substance, and graders notice when a source has no evident connection to the sentence it follows.
Can a reply work from a post with no output attached?
Then the reply asks for it or works from what the post reports. A question about which table a figure came from is still useful when the table is missing, since it invites the classmate to show the evidence. The reply can also point out what the reported figure would need in order to be interpretable, such as the number of cases it rests on.
Where can I find a free PHLT 8032 Week 6 sample paper?
The two complete Week 6 replies are reproduced here, one on the chi-square row quoted for a nominal variable and one on a BMI comparison that lost over a third of respondents, each with a source.
Why ask a question instead of stating the error?
A question lets the classmate find the problem in their own output and leaves room for a reason the replier did not know about. It also reads as engagement rather than a verdict, which discussion rubrics reward.