Analysis Workshop: Measuring a Hunger Pang

Part 1 — The Big Picture

This week you learned that drive-reduction theory runs need, drive, behavior, homeostasis, that the hypothalamus helps regulate hunger as ghrelin signals hunger and leptin signals fullness, and that learned, social, and cultural pulls shape eating too. Psychology only counts a claim like that as knowledge once it is measured. In this workshop you turn a fuzzy feeling, hunger, into an operational definition, measure it on yourself across one day, put one number on its link with the hours since you last ate using the correlation coefficient r, and design the experiment that could test whether a food cue makes people want to eat even when they are full. A chatbot then answers questions about your work, and you catch where it goes wrong.

Time budget: Plan for about 90 minutes.

Part 2 — The Task

Guiding question: Does your hunger rise steadily with the hours since you last ate, how would you measure hunger honestly, and what would it take to test whether a food smell makes people want to eat even after a full meal?

Before you begin — hypothesis: Before your first check-in, write one sentence in this form: If I rate my hunger at eight check-ins across one day, then my hunger rating will ___ as the hours since I last ate increase because ___.

Workshop data: your-hunger-check-ins

Source: Your own hunger ratings and hours since your last meal or snack, recorded at eight check-ins across one ordinary day in this workshop; no outside file is needed.

• Rows: 8 check-ins per day, one for each check-in from 1 to 8; the definition table has 5 rows, and the check-in table has 9 rows, the 8 check-ins plus one row for r
• Columns: Check-in number from 1 to 8, hours since you last ate to the nearest half hour, and a hunger rating from 1 to 7

Part 3 — Materials & Procedure

Tools and materials

• Google Sheets (free) or Excel
• A clock or phone that shows the time
• A document or notebook for your written answers

Ways of working

• operationalize-a-construct
• design-a-correlational-or-experimental-check
• compute-and-interpret-r

Procedure

1. Fill in the first scaffold table, your operational definition, before your first check-in: the 1-to-7 hunger scale with its two end labels, the rule for counting hours since you last ate, and the eight check-in times you plan to use across one ordinary day. Tool: A document or notebook for your written answers. Check: The first table is complete before the first check-in.
2. At each of your eight check-ins, look at the time, work out how many hours have passed since you last ate a meal or snack, rounded to the nearest half hour, and write it in the second scaffold table. Tool: A clock or phone that shows the time. Check: Eight values for hours since you last ate, one per check-in.
3. At the same moment, rate how hungry you feel from 1 to 7 using your scale, before you decide whether to eat, and write the rating beside the hours in the second table. Eat as you normally would; the workshop only records what happens. Tool: A document or notebook for your written answers. Check: Eight hunger ratings, one beside each value for hours.
4. Open a blank sheet. Type Hours in cell A1 and Hunger in cell B1, then type your eight values for hours in A2 through A9 and your eight ratings in B2 through B9. Tool: Google Sheets (free) or Excel. Check: Two columns of eight numbers under their headings.
5. In an empty cell, type =CORREL(A2:A9,B2:B9) and record the result to two decimals in the last row of the second table. If every rating or every value for hours is the same, the formula shows an error because r cannot be computed when one variable never changes; record that and explain it in your first analysis answer. Tool: Google Sheets (free) or Excel. Check: A value between negative 1 and positive 1, or an error message if one column never changes.
6. Select A1 through B9 and insert a scatter chart with hours since you last ate across the bottom and hunger rating up the side. Title both axes. Tool: Google Sheets (free) or Excel. Check: Eight dots, one per check-in, that you can describe in words.
7. Fill in the third scaffold table, your experiment design, then answer the analysis questions and run the critique step in Part 6. Tool: A document or notebook for your written answers. Check: Three completed tables, five written answers, and a critique.

Fallback route

When to use it: Use this route if you cannot run the check-in log, for example if no clock or spreadsheet is available, or if keeping track of eating or hunger is uncomfortable for you for any reason; you do not need to say why.

Write the operational definition in the first table as if you were planning the check-ins, and leave the second table's hours and ratings blank. Then use the model data below in place of your own check-ins: compute r for the ten pairs with the same CORREL formula, make the scatter chart, and answer the first analysis question about these data. For questions 2 and 3, describe the pattern you would predict for a hunger check-in log and explain it with drive reduction, the hunger hormones, and learned or cultural pulls. Complete the design table, the remaining questions, and the critique step. Submit the same items for equal credit.

This route earns the same credit as the primary route.

An illustrative paired dataset this course uses for practicing r, constructed for teaching rather than collected from real students.

Fallback model data

Hours of sleep	Quiz score (of 20)
5	11
6	14
6.5	12
7	15
7	13
7.5	16
8	17
8	14
8.5	18
9	17

Part 4 — The Scaffold

Write the operational definition first, because a feeling you have not defined as a procedure is not yet measurable.

Student scaffold table 1

Part of the definition	Your entry
The hunger scale and its two end labels	1 means not hungry at all; 7 means as hungry as I have ever felt
How I count hours since I last ate	Hours since the end of my last meal or snack, rounded to the nearest half hour
My eight check-in times
What one hunger rating measures, in one sentence
What hours since I last ate measures, in one sentence

Record each check-in as it happens, because the pairs of hours and ratings are what r summarizes.

Student scaffold table 2

Check-in	Hours since I last ate	Hunger rating (1 to 7)
1
2
3
4
5
6
7
8
Correlation r (two decimals)

Plan the experiment that could test a cause, because your check-in log can only show an association.

Student scaffold table 3

Design element	Your plan
The claim being tested	The smell of fresh-baked bread makes people who have finished a full meal want to eat more
Independent variable (IV) and its two conditions
Dependent variable (DV) and how it is measured
How participants are assigned to conditions
How participants are chosen, and who the results could apply to
One third variable you would hold the same in both conditions
What the result would be allowed to claim

Part 5 — Analysis Questions

1. Report your r to two decimals. Describe its direction and strength in words, and write one sentence that uses the phrase associated with. If r could not be computed, explain why in terms of your data.
2. Describe the pattern in your eight dots: did your hunger rise as the hours since eating grew, which check-in broke the pattern most, and do the dots look closer to a straight line or a scattered cloud?
3. Explain your result with this week's ideas. Trace one check-in through need, drive, behavior, homeostasis, and say how ghrelin and leptin help regulate the hunger you rated, using hedged wording. Then name one learned, social, or cultural pull, such as a usual mealtime or a shared meal, that could have shaped a rating.
4. Your log is correlational: you measured hours and hunger but manipulated nothing. Name one third variable, such as your usual meal times, how active the day was, or the time of day, that could raise both the hours since eating and your hunger, and explain how the experiment in your third table would rule out a third variable like that.
5. Choose one and answer it in 3–4 sentences. (a) If you rated your hunger at the same eight times on another ordinary day, would you expect similar ratings? Say what that tells you about the reliability of your measure, and whether a reliable measure is automatically valid. (b) Could your hunger rating capture something other than hunger, such as boredom, stress, or a craving set off by a food ad? Explain the validity problem and suggest one change that would make the measure more valid.

Part 6 — AI-Critique Moment

Choose one equal-credit critique route. Route A: ask an approved AI tool to critique your completed work, then check its response against your own evidence. Route B is the no-AI equivalent printed below.

Approved tools: Claude, Gemini, ChatGPT, Grok, Microsoft Copilot.

Prompt to paste:

Over one day I rated my hunger from 1 to 7 at eight check-ins and wrote down how many hours had passed since I last ate. I've pasted my completed tables above, including my correlation r between hours since eating and hunger rating and my plan for an experiment on whether the smell of fresh bread makes people who have finished want to eat more. Please answer five things in short sentences. First, does my correlation prove that the hours without food caused my hunger ratings to rise? Second, in my planned experiment, which is the independent variable and which is the dependent variable? Third, if I want my results to apply to all adults in my country, should I use random assignment or random sampling? Fourth, when I rated my hunger twice a few minutes apart I gave the same number both times, so is my hunger scale valid? Fifth, is drive-reduction theory a theory or a hypothesis, and is my prediction that my hunger rating will rise with the hours since I last ate a theory or a hypothesis?

Test the response for these likely failures:

• Chatbots often say a positive r proves that hours without food caused the hunger ratings to rise, or that a hormone alone caused the rise.
• Chatbots often call the desire-to-eat rating the independent variable because it is the main measure, or call hours since eating the independent variable in the check-in log.
• Chatbots often say random assignment is what lets the results apply to all adults in the country.
• Chatbots often say a hunger scale that gives the same number twice is therefore valid.
• Chatbots often call the student's specific prediction a theory, or say drive-reduction theory is only a hypothesis.

Write 3–5 sentences naming one thing the chatbot got right and at least two places it went wrong or overreached, quoting its words and correcting each with evidence from your own tables or this week's course definitions.

Equivalent route (no AI required)

No AI account is required for this workshop. Instead of the chatbot, critique this flawed analysis, printed here in full: "My hunger ratings rose as the hours since I last ate went up, and my correlation was strongly positive, which proves that going without food caused my hunger, so ghrelin alone controls when I eat. In my experiment, the desire-to-eat rating is the independent variable and the bread smell is the dependent variable. I will randomly assign people to the bread-smell and no-smell groups so that my results apply to every adult in the country. When I rated my hunger twice a few minutes apart I gave the same number, so my hunger scale is valid. Drive-reduction theory is only a hypothesis, while my prediction that my hunger will rise with the hours since I ate is a theory." Mark it up: quote each flawed sentence, name what is wrong (cause read from a correlation, the IV and DV swapped, random assignment confused with random sampling, reliability confused with validity, or theory confused with hypothesis), cite the evidence from your tables or the course definitions, and write a corrected sentence beneath it. This route assesses the same objectives (Objective 6 and Objective 3) as the chatbot critique, takes comparable effort (about 30 minutes and 200–300 words), is scored by the same scoring criteria for the same credit, and its own submission artifact is the marked-up flawed analysis, posted in place of the chatbot share link and report.

Part 7 — What to Submit

• All three completed scaffold tables
• Your scatter chart with titled axes, or a labelled sketch of it
• Your hypothesis sentence and written answers to all five analysis questions
• The chatbot critique report with its share link, or the equivalent route's marked-up flawed analysis

Deliverable:

A short Analysis Workshop report: three completed tables (your operational definition of hunger, your eight check-ins with r, and an experiment design), a scatter chart with titled axes or a labelled sketch, a hypothesis sentence and written answers to five analysis questions, and a critique of a chatbot's answers about your work, or, on the equivalent route, a marked-up correction of the printed flawed analysis. Submit in any accessible file type or as a link; a recorded response may be submitted as audio or video with a transcript, or as text. Choose a written, recorded, or visual form for your answers; every form is graded by the same scoring criteria.

Rubric

The rubric below shows how each submitted part is evaluated; bands are full, partial, or none.

Workshop evaluation criteria

Criterion	Full	Partial	None
Operational definition of hunger	The first table gives a hunger scale with both end labels, a rule for counting hours since eating, the check-in times, and one-sentence statements of what a rating and the hours each measure.	A definition is given, but the scale's end labels, the counting rule, the check-in times, or one of the two one-sentence statements is missing or vague.	No operational definition is written.
Data, r, and its interpretation	Eight check-ins or the fallback data are recorded, r is computed and reported to two decimals or its error is explained, the scatter chart or sketch has titled axes, and the interpretation says associated with, gives direction and strength, and names a third variable.	The data and r are present, but the chart, the direction and strength, or the third variable is missing, or the sentence slips into cause.	No check-ins or correlation are recorded, or the values are invented.
Experimental design check	The design table puts the manipulated smell condition as the IV and the measured desire to eat as the DV, uses random assignment for the conditions and random sampling for who could be studied, holds a third variable constant, and states what the result could claim.	The design is attempted, but the IV and DV are swapped, or assignment and sampling are confused, or the claim goes beyond the design.	No experiment design is submitted.
Drive-reduction and hunger reasoning and the limitation	Questions 2 to 5 describe the pattern, trace a check-in through need, drive, behavior, homeostasis, describe ghrelin and leptin with hedged wording and one learned, social, or cultural pull, connect the third variable to the experiment, and answer the chosen limitation question with an accurate account of reliability or validity.	Most questions are answered, but the loop is out of order, ghrelin and leptin are swapped or said to cause hunger, reliability and validity are blurred, or a reason is missing.	The analysis questions are missing or do not address the measurement.
Critique of the chatbot or of the printed flawed analysis	On either route, at least two errors are quoted and corrected with evidence from the student's own tables or the course definitions, and one correct point is acknowledged; both routes earn this band equally.	On either route, errors are named but corrected without evidence, or only one error is caught.	No critique appears, or pasted output is submitted without any checking.

Quality Gate

• The operational definition is written before the first check-in, and it names the scale with both end labels, the rule for counting hours, and the check-in times.
• Every value for hours, rating, and the correlation comes from your own log and formula, or from the fallback model data, never from memory or a chatbot.
• The scatter chart puts hours since eating across and hunger rating up, with both axes titled.
• Every sentence about r says associated with and names a third variable; no sentence claims that the hours without food, or a hormone, caused the hunger.
• The design table names the IV, the DV, random assignment, and random sampling, each in its correct role.
• No statistic, rating, or source is invented anywhere in the submission.
• The critique quotes the chatbot or the flawed analysis and corrects at least two errors with evidence from your tables or the course definitions.