A worked PSYC-FPX3700 Assessment 3 statistical critique, evaluating the analysis choices inside one published psychology study and sizing its conclusions to what the numbers support. Searches like "psyc fpx 3700 assessment 3 assignment example", "psycfpx3700 assessment 3 sample" and "psyc-fpx3700 assessment 3 example" land here.
What a finished PSYC-FPX3700 Assessment 3 statistical critique looks like
The critique on the page spends most of its length inside two sections of somebody else's article. It begins by reporting the study's design and analysis accurately, the variables, the groups, the test used and the values reported, so the evaluation that follows lands on a description the authors would accept. Then the questions start, one decision at a time: was that the right test for these variables, were the assumptions addressed anywhere, is the effect size reported and if not what does its absence hide, and does the sample support the generalization made. Multiple comparisons and selective reporting are examined where the paper's tables invite it. The closing rewrites the study's own conclusion at the strength the analysis actually licenses, which is usually a narrower sentence than the abstract carries.
How a PSYC-FPX3700 Assessment 3 example is structured
Five sections, and the evaluation is held back until the third. The first identifies the article and states its research question and its headline conclusion in the authors' own terms. The second describes the analysis neutrally: design, variables and their measurement levels, sample size, the tests run and the values reported, written from the results section rather than from the abstract. The third section evaluates test selection, asking what the variables and design called for and whether the paper's choice matches, with an alternative named where it does not. The fourth section evaluates reporting and interpretation: assumptions addressed or not, effect sizes present or absent, confidence intervals, multiple tests without correction, and any place where the discussion says more than the results paragraph did. The fifth section credits what the analysis does establish and then restates the finding at its defensible strength.
The analysis described before it is judged
Design, tests and reported values are set out neutrally first, which settles the accuracy criterion before any evaluation begins.
Test selection judged against the design
The critique names what the variables and groups called for and says whether the authors chose it, proposing the alternative where they did not.
Absences counted as findings
A missing effect size, an unaddressed assumption or an uncorrected set of comparisons is treated as evidence about the analysis, not overlooked.
Discussion checked against the results
The critique compares what the authors concluded with what their own numbers reported, which is where overreach usually appears in published work.
A conclusion restated at defensible strength
The final move rewrites the study's claim to fit its analysis, which is the single sentence graders in this course look hardest at.
Where marks go in PSYC-FPX3700 Assessment 3
Critiques go wrong in two directions and both cost the evaluation criterion. One is deference, where the paper is summarized admiringly and the critique amounts to noting that the sample could have been larger. The other is indiscriminate attack, a list of generic complaints that would apply to any study in any field and therefore say nothing about this one. A separate failure is the abstract-only critique, which cannot comment on test selection because it never saw the results section, and which shows immediately in how vague the method description is. Another is arithmetic without judgment, a recital of the values the paper reported with no assessment of whether the analysis suited the question. Marks also go for statistical claims made without reference to the article's own tables.
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PSYC-FPX3700 Assessment 3 questions, answered
How do I critique statistics I have not been taught?
Work from the logic rather than the formula. You can ask whether the variables suit the test, whether the sample supports the claim, whether an effect size is reported and whether the discussion outruns the results, all without being able to run a mixed model yourself. Where a technique is beyond you, say what it is for and evaluate what you can.
Which article should I choose if the choice is mine?
Something with a simple design and a full results section you can actually read. A two-group comparison or a correlational study reported in detail gives you far more to evaluate than an elaborate modeling paper you can only admire. Open access helps, since you need the tables and not just the abstract, and a modest recent study is usually a better subject than a famous one.
What if the study's analysis looks entirely sound?
Then say so and evaluate the interpretation instead, which is where most sound analyses still stretch. Check whether the discussion stays inside the design, whether the sample supports the population being addressed, and whether an association is described anywhere as an effect. A critique that finds the statistics defensible and the conclusion slightly oversized is a real result and scores well.