Reviewer 2 wants the mean age by treatment group recalculated with a stricter eligibility cutoff. You open SPSS. How long before the revised table is in your inbox?
If I remember the syntax — maybe an hour. If I have to re-click the menus, probably a weekend.
In Python, those values are variables. A named slot for each number, a formatted string for the table row, a comparison for the eligibility rule. Change the cutoff, rerun the script — the revised table generates itself.
So by Friday I can write code that spits out a formatted methods-section line without touching SPSS?
Exactly. Five short functions, each adding one layer. Day 3 formats one respondent label. Day 4 normalises a messy treatment-group name from the SurveyMonkey export. Day 5 builds the full statistics line with N and mean. Day 6 checks whether a respondent meets the pre-registered age minimum. Day 7 categorises an outcome score into three tiers. By Friday you have the raw pieces of the reproducible pipeline you'll finish in Week 4.
"Respondent R_042 — Age: 29.00"strip(), lower(), and replace()f"{mean:.2f}""below threshold", "at threshold", or "above threshold" with if/elif/elseGoal: by Friday you can format one respondent row entirely in code.
7 lessons this week
Reviewer 2 wants the mean age by treatment group recalculated with a stricter eligibility cutoff. You open SPSS. How long before the revised table is in your inbox?
If I remember the syntax — maybe an hour. If I have to re-click the menus, probably a weekend.
In Python, those values are variables. A named slot for each number, a formatted string for the table row, a comparison for the eligibility rule. Change the cutoff, rerun the script — the revised table generates itself.
So by Friday I can write code that spits out a formatted methods-section line without touching SPSS?
Exactly. Five short functions, each adding one layer. Day 3 formats one respondent label. Day 4 normalises a messy treatment-group name from the SurveyMonkey export. Day 5 builds the full statistics line with N and mean. Day 6 checks whether a respondent meets the pre-registered age minimum. Day 7 categorises an outcome score into three tiers. By Friday you have the raw pieces of the reproducible pipeline you'll finish in Week 4.
"Respondent R_042 — Age: 29.00"strip(), lower(), and replace()f"{mean:.2f}""below threshold", "at threshold", or "above threshold" with if/elif/elseGoal: by Friday you can format one respondent row entirely in code.