How many hand-coded account files a regression and a two-group test need, settled in G*Power before collection, is what this GB701 Unit 4 memo decides. Searches like "gb 701 unit 4 assignment example", "gb701 unit 4 sample" and "gb701 unit 4 example" land here.
What a finished GB701 Unit 4 sampling and power memo looks like
A three-page memo to the candidate's doctoral mentor, with two G*Power protocol screens in an appendix. It opens with the question the data must answer: whether e-invoicing adopters pay faster once terms, disputes, tenure and purchase volume are held constant. Primary analysis means a multiple regression testing one predictor among five. G*Power 3.1, F tests, Linear multiple regression: Fixed model, R-squared increase, run a priori with an f-squared of .04, alpha of .05 and power of .80, returns a total of 199. The secondary two-group comparison, the t test for two independent means with d of .40 and an allocation ratio of 1.75, returns 78 and 137, or 215. A sampling section adopts the larger figure, adds an 8 percent allowance and draws 234 accounts stratified by terms.
How a GB701 Unit 4 example is structured
The memo is built so each number in it traces back to a decision a reader can challenge. Effect size comes first and is argued from money rather than convention: the finance team judged a six-day reduction the smallest difference worth the portal fee, and with the spread in the ERP extract that difference corresponds to about d of .40 and, once the covariates absorb their share, an f-squared near .04. Cohen's benchmarks appear only as context. Alpha and power are stated with their consequence in plain terms, a one-in-five chance of missing a real six-day effect. What other choices would cost appears in a sensitivity table, 395 accounts at an f-squared of .02 and 55 at .15. The sampling section explains stratification by terms, citing the Unit 3 finding that net-45 accounts hold most of the late tail, and why proportional allocation keeps the sample self-weighting.
Two tests, two G*Power runs
The regression and the group comparison are powered separately, each with its own protocol screen showing test family, statistical test, type of analysis, inputs and outputs. The memo reports the noncentrality parameters, 7.96 and 2.82, and the actual power, .8016 for both, so either run can be reproduced exactly.
Where six days came from
Finance staff priced the e-invoicing portal against the cost of carrying receivables and named six days as the break-even improvement. The memo converts that figure into standardized effect sizes openly, arithmetic shown, instead of choosing a medium effect because a textbook calls it typical.
Allowance for files that cannot be coded
Some accounts will have switched invoicing method mid-year and others will have incomplete dispute records. Drawing on a small pilot pull, the memo expects about 8 percent of files to be unusable and draws 234 so that at least 215 remain.
Stratified by terms, allocated proportionally
Net-45 accounts make up 24 percent of the frame, so 57 of the 234 draws come from that stratum and 177 from net-30. Proportional allocation keeps estimates self-weighting, which spares later units any weighting adjustment.
What the memo declines to promise
Power applies to effects of the planned size. The memo states that a smaller true effect could be missed, that archival records rule out random assignment, and that any difference found will be an association adjusted only for measured covariates.
Where marks go in GB701 Unit 4
Power sections in GB701 that report a sample size without its inputs are among the first things an instructor questions, because an unexplained N cannot be checked. Choosing a medium effect by convention, with no argument from the business problem or prior research, draws a pointed comment at doctoral level. Misnaming the G*Power procedure, for example running the overall R-squared test when a single predictor is the focus, changes the answer and is marked as an error of substance. Memos that forget unusable records, or treat power as a property of data already held, show the planning came afterward. Credit regularly follows a sensitivity table showing how the sample would change under other assumptions. The sampling design needs its own justification, since a frame, a stratification variable and an allocation rule are three separate decisions.
Get a GB701 Unit 4 example written to your instructions
Tell us the analysis your study plans, the variables involved and any effect size your section or prior research suggests, and include the Unit 4 prompt with its rubric. Laid out for checking, the G*Power inputs and outputs arrive in a custom memo within 24-48h. Your first one is free.
GB701 Unit 4 questions, answered
Which G*Power test fits a single predictor in a regression?
For testing whether one predictor adds explained variance beyond the others, the F tests family offers Linear multiple regression: Fixed model, R-squared increase, with the number of tested predictors set to one and the total number of predictors entered separately. The R-squared deviation from zero test asks a different question, whether the model as a whole explains anything, and can return a very different sample size.
What if the available data fall short of the power analysis?
Say so and report a sensitivity analysis, which gives the smallest effect the available sample can detect with adequate power. Committees generally prefer an honest sensitivity statement to a power analysis reverse-engineered to match the data on hand. The limitation then belongs in the discussion of any null result, where it matters most to a reader.
Is 80 percent power always the target?
It is a convention, not a rule. Eighty percent accepts a one-in-five chance of missing a real effect of the planned size. Where a missed effect would be costly, 90 percent may be justified, at the price of a larger sample. Name the choice and its consequence, since instructors usually mark the reasoning rather than the figure itself.