Analysis Workshop: What Keeps You Checking?

Part 1 — The Big Picture

This week you learned that consequences shape voluntary behavior and that variable-ratio reinforcement, a reward after an unpredictable number of responses, keeps behavior going longest. Checking a phone fits that pattern: some checks bring a message you care about and most do not. Psychology only counts that idea as knowledge once it is measured. In this workshop you turn phone checking into an operational definition, measure it on yourself for one hour, put one number on the pattern with the correlation coefficient r, and design the experiment that could test a cause. 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: Do your phone pickups rise and fall with the notifications you receive, how would you measure that honestly, and what would it take to test whether notifications cause the checking?

Before you begin — hypothesis: Before you start the hour, write one sentence in this form: If I count my notifications and my phone pickups in twelve 5-minute blocks, then blocks with more notifications will have ___ pickups because ___.

Workshop data: your-phone-checking-log

Source: Your own tally of phone notifications and phone pickups during one hour of an ordinary activity, recorded in twelve 5-minute blocks in this workshop; no outside file is needed.

• Rows: 12 blocks per log, one for each 5 minutes of a 60-minute stretch; the definition table has 5 rows, and the log table has 13 rows, the 12 blocks plus one row for r
• Columns: Minutes since start, notifications that arrived in the block, and times you picked up your phone in the block

Part 3 — Materials & Procedure

Tools and materials

• Google Sheets (free) or Excel
• A timer, such as a phone or a clock with a second hand
• A paper tally sheet or a notes page for counting during the hour
• 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. Choose one hour of an ordinary activity, such as studying, working, or relaxing at home, when you would normally have your phone nearby. Fill in the first scaffold table, your operational definition, before you start: what counts as one pickup, what counts as one notification, the activity, and the 5-minute block schedule. Tool: A document or notebook for your written answers. Check: The first table is complete before the hour starts.
2. Start the timer. During each 5-minute block, make a tally mark for every notification that arrives and a separate tally mark for every time you pick up your phone. At the end of each block, copy both counts into the second scaffold table. Tool: A paper tally sheet or a notes page for counting during the hour. Check: Twelve blocks, each with a notification count and a pickup count.
3. Open a blank sheet. Type Minutes in cell A1, Notifications in cell B1, and Pickups in cell C1, then type your twelve block start times in A2 through A13 and your counts in B2 through C13. Tool: Google Sheets (free) or Excel. Check: Column A runs 0, 5, 10, and so on up to 55.
4. In an empty cell, type =CORREL(B2:B13,C2:C13) and record the result to two decimals in the last row of the second table. If every notification count or every pickup count 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.
5. Select B1 through C13 and insert a scatter chart with notifications across the bottom and pickups up the side. Title both axes. Tool: Google Sheets (free) or Excel. Check: Twelve dots, one per block, that you can describe in words.
6. 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 log an hour of your own phone use, for example if you do not use a phone, cannot keep it nearby, or if a privacy, work, or health reason makes the log unsuitable for you.

Write the operational definition in the first table for a reinforced habit you know well, such as checking a mailbox or an email inbox. Then use the model data below in place of your own counts: 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 phone-checking log and explain it with operant conditioning and the variable-ratio schedule. 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 habit you have not defined as a procedure is not yet measurable.

Student scaffold table 1

Part of the definition	Your entry
The activity during my hour
What counts as one pickup
What counts as one notification	Any buzz, sound, banner, or lit screen from an app, counted once each
How often I recorded	Every 5 minutes, twelve blocks from 0 to 55 minutes
What one pickup count measures, in one sentence

Record both counts block by block, because r pairs the notifications in each block with the pickups in the same block.

Student scaffold table 2

Minutes since start	Notifications in the block	Pickups in the block
0
5
10
15
20
25
30
35
40
45
50
55
Correlation r (two decimals)

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

Student scaffold table 3

Design element	Your plan
The claim being tested	Turning phone notifications off makes people pick up their phones less often
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 counts.
2. Describe your twelve blocks: which blocks had the most pickups, whether any pickups happened in blocks with no notifications at all, and whether the dots look closer to a straight line or a scattered cloud.
3. Explain your checking with operant conditioning. What is added after a pickup that pays off, and which schedule of reinforcement do those payoffs follow? Use that schedule to explain why pickups can happen even in a block with no notification.
4. Your log is correlational: you counted notifications and pickups but manipulated nothing. Name one third variable, such as having free time or waiting for an important message, that could raise both counts, and explain how the experiment in your third table would rule it out.
5. Choose one and answer it in 3–4 sentences. (a) If you logged the same hour again tomorrow, would you expect the same counts? Say what that tells you about the reliability of your measure, and whether a reliable measure is automatically valid. (b) Could a pickup count capture something other than a reinforced checking habit, such as using the phone as a clock? 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:

For one hour I counted, in twelve 5-minute blocks, how many phone notifications arrived and how many times I picked up my phone, and I've pasted my completed tables above, including my correlation r between notifications and pickups and my plan for an experiment on turning notifications off. Please answer five things in short sentences. First, does my correlation prove that notifications cause me to pick up my phone? 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 college students, should I use random assignment or random sampling? Fourth, my counts came out almost the same when I repeated the hour the next day, so is my pickup count valid? Fifth, is the idea that variable-ratio reinforcement keeps behavior going a theory or a hypothesis, and is my prediction that I will pick up my phone less with notifications off a theory or a hypothesis?

Test the response for these likely failures:

• Chatbots often say a positive r proves that notifications caused the pickups.
• Chatbots often call the pickup count the independent variable because it is the behavior of interest, or call notifications an independent variable in the one-hour log.
• Chatbots often say random assignment is what lets the results apply to all college students.
• Chatbots often say a pickup count that comes out the same twice is therefore valid.
• Chatbots often call the student's specific prediction a theory, or use theory and hypothesis as if they meant the same thing.

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 notifications and my pickups rose and fell together and my correlation was strongly positive, which proves that notifications cause me to pick up my phone. In my experiment, the pickup count is the independent variable and the notification setting is the dependent variable. I will randomly assign students to the notifications-on and notifications-off groups so that my results apply to every college student. When I logged a second hour, my counts came out almost the same, so my pickup count is valid. The idea that consequences shape behavior is only a hypothesis, while my prediction that I will check less with notifications off 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 objective (Objective 5 — compare the major models of learning and memory and apply them to everyday behavior, including why memory is reconstructive) 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 phone checking, your twelve blocks of counts 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 phone checking	The first table names the activity, says exactly what counts as one pickup and one notification, and gives the block schedule, and its one-sentence statement says what a pickup count measures.	A definition is given, but what counts as a pickup or a notification, or the schedule, is missing or vague.	No operational definition is written.
Data, r, and its interpretation	Twelve blocks of counts 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 counts or correlation are recorded, or the values are invented.
Experimental design check	The design table puts the manipulated notification condition as the IV and the measured pickup count 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.
Learning reasoning and the limitation	Questions 2 to 5 describe the blocks accurately, explain the checking with operant conditioning and the variable-ratio schedule, 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 schedule is misnamed, reinforcement and punishment are blurred, or reliability and validity are confused, 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 hour starts, and it says what counts as one pickup, what counts as one notification, and the block schedule.
• Every count and the correlation come from your own log and formula, or from the fallback model data, never from memory or a chatbot.
• The scatter chart puts notifications across and pickups up, with both axes titled.
• Every sentence about r says associated with and names a third variable; no sentence claims that notifications cause the pickups.
• The design table names the IV, the DV, random assignment, and random sampling, each in its correct role.
• No statistic, count, 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.