Sentence Stimulus Norming

• Working with single-word stimuli without sentence context (use lexical database tools directly)
• Designing non-linguistic stimuli (visual search arrays, tones)
• Analyzing existing normed materials without creating new ones

Research Planning Protocol

Before executing the domain-specific steps below, you MUST:

1. State the research question -- What specific question is this analysis/paradigm addressing?
2. Justify the method choice -- Why is this approach appropriate? What alternatives were considered?
3. Declare expected outcomes -- What results would support vs. refute the hypothesis?
4. Note assumptions and limitations -- What does this method assume? Where could it mislead?
5. Present the plan to the user and WAIT for confirmation before proceeding.

For detailed methodology guidance, see the research-literacy skill.

⚠️ Verification Notice

This skill was generated by AI from academic literature. All parameters, thresholds, and citations require independent verification before use in research. If you find errors, please open an issue.

Cloze Probability Norming

What Is Cloze Probability?

Cloze probability is the proportion of people who complete a sentence fragment with a particular word (Taylor, 1953). It is the standard measure of a word's predictability in context and is a critical control variable in nearly all sentence processing research.

Procedure

1. Create sentence fragments: Truncate each sentence immediately before the critical word
2. Present fragments one at a time to participants
3. Instruct: "Please complete each sentence with the first word that comes to mind. Write only one word."
4. Score: For each item, cloze probability = (number of completions matching the target word) / (total number of respondents)

Design Parameters

Scoring Conventions

• Exact match: Only the target word counts (standard)
• Morphological variants: Decide a priori whether "run" and "running" count as the same completion. Standard practice: count only the exact form (Staub et al., 2015)
• Spelling errors: Accept obvious misspellings of the target
• Blank/nonsense responses: Exclude from the denominator (participant did not engage)

Cloze Probability Benchmarks

Online vs. Lab Norming

Recommendation for online norming: Include 10-15% catch trials (sentences with obvious completions, e.g., "The dog chased the ___") and exclude participants who fail > 20% of catch trials.

Plausibility and Naturalness Ratings

When to Collect

• When cloze probability alone is insufficient (e.g., both conditions have low cloze but differ in plausibility)
• When manipulating semantic fit or thematic role plausibility
• When verifying that "anomalous" conditions are genuinely perceived as odd

Rating Scale Design

Instructions Template

"You will read a series of sentences. For each sentence, please rate how natural or plausible it sounds on a scale from 1 to 7, where 1 means 'very unnatural / makes no sense' and 7 means 'perfectly natural / makes complete sense.' There are no right or wrong answers; we are interested in your intuition."

Critical Design Considerations

• Within-list design: Each rater sees only one version of each item (Latin square). Raters should never see multiple conditions of the same item, or they will rate contrastively rather than absolutely.
• Filler items: Include filler sentences spanning the full rating range. This prevents range restriction.
• Order effects: Randomize item order per participant.

Acceptability Judgments

When to Collect

• When manipulating syntactic structure (grammaticality, island constraints, movement dependencies)
• When testing formal linguistic predictions about sentence well-formedness
• For factorial designs crossing syntactic factors (e.g., 2x2 designs testing island effects; Sprouse et al., 2012)

Rating Methods

Recommendation: Use 7-point Likert as the default. It provides sufficient sensitivity for most research questions and has been shown to replicate formal linguistic judgments as reliably as magnitude estimation (Sprouse & Almeida, 2012; Sprouse, 2011).

Sample Size for Acceptability

Lexical Controls

Variables That Must Be Controlled Across Conditions

Every critical word manipulation must control for confounding lexical variables. The target word and its condition-matched alternatives should be equated on the following:

Frequency Database Selection

Key recommendation: Use SUBTLEX log frequency values. They explain more variance in lexical decision and naming times than older norms (Brysbaert & New, 2009).

How to Match Across Conditions

1. Select critical words for each condition
2. Retrieve lexical metrics from SUBTLEX-US and norming databases
3. Compute condition means for each metric
4. Test for differences: Run t-tests or ANOVAs across conditions on each lexical variable
5. Criterion: No significant differences (p > 0.20 is a reasonable threshold; some use p > 0.30) on any controlled variable
6. If matching fails: replace items or add the unmatched variable as a covariate in the analysis

Latin Square Counterbalancing

Purpose

In a within-item design, each item appears in all conditions, but each participant sees each item in only one condition. A Latin square assigns items to conditions across participant lists.

Construction

For a design with k conditions and n items (where n is divisible by k):

1. Divide items into k groups of n/k items each
2. Create k lists; in each list, assign each item group to a different condition
3. Each participant receives one list
4. Result: every item appears in every condition across participants; each participant sees an equal number of items per condition

Example: 2-Condition Design

With 40 items and 2 conditions (A, B):

Requirements

Filler Items

Purpose

Fillers prevent participants from noticing the experimental manipulation and adopting strategies.

Design Parameters

Filler Construction Tips

• Use fillers from different syntactic constructions than your targets
• Include some fillers with comprehension questions (for reading studies) to maintain attentive reading
• If targets are semantically anomalous, include some fillers that are also slightly odd (but in different ways) so anomaly is not a cue

Practice and Warm-Up Items

Online Norming Considerations

Platform Recommendations

Quality Control for Online Studies

Common Pitfalls

1. Not norming cloze probability: Claiming words are "predictable" or "unpredictable" based on experimenter intuition rather than empirical cloze norms. Always collect cloze data (Taylor, 1953).

2. Too few raters per item: With N < 20 raters for cloze, individual item estimates are unstable. A word with true cloze of 0.50 could yield observed cloze of 0.20-0.80 with only 10 raters. Use minimum 30 raters (Bloom & Fischler, 1980).

3. Not controlling word frequency: Frequency is the strongest single predictor of reading time. A 1 log-unit difference in SUBTLEX frequency corresponds to ~30-40 ms in gaze duration (Brysbaert & New, 2009; Rayner, 1998). Always match or control.

4. Using the wrong frequency database: HAL and Kucera-Francis norms are outdated. SUBTLEX-US explains significantly more variance in behavioral data (Brysbaert & New, 2009).

5. Showing raters multiple conditions of the same item: This introduces contrastive evaluation. Raters must see each item in only one condition (Latin square for norming too).

6. Insufficient filler items: A 1:1 target-to-filler ratio makes the manipulation transparent. Use at least 2:1 fillers to targets (Schütze & Sprouse, 2014).

7. Not piloting the norming study: Always pilot with 5-10 participants to catch unclear instructions, ambiguous items, and timing issues before running the full norming sample.

8. Ignoring age of acquisition: AoA effects are independent of frequency (Kuperman et al., 2012). Failing to control AoA can introduce confounds, especially for studies comparing concrete vs. abstract words.

Minimum Reporting Checklist

Based on Schütze & Sprouse (2014) and current psycholinguistic standards:

• Number of items per condition
• Cloze probability values: mean, SD, and range per condition (if collected)
• Cloze norming details: N raters, population, procedure, scoring criteria
• Plausibility/acceptability ratings: scale type, N raters, mean and SD per condition
• Lexical control variables: list each controlled variable, database source, and condition means
• Statistical test confirming conditions do not differ on controlled variables
• Latin square design: number of lists, items per list per condition, participants per list
• Filler-to-target ratio and description of filler types
• Number of practice/warm-up items
• For online norming: platform, pay rate, attention check procedure, exclusion criteria and N excluded
• Full item list (in supplementary materials or online repository)

References

• Andrews, S. (1997). The effect of orthographic similarity on lexical retrieval: Resolving neighborhood conflicts. Psychonomic Bulletin & Review, 4, 439-461.
• Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59, 390-412.
• Baayen, R. H., Piepenbrock, R., & Gulikers, L. (1995). The CELEX lexical database (CD-ROM). Linguistic Data Consortium, University of Pennsylvania.
• Bard, E. G., Robertson, D., & Sorace, A. (1996). Magnitude estimation of linguistic acceptability. Language, 72, 32-68.
• Bloom, P. A., & Fischler, I. (1980). Completion norms for 329 sentence contexts. Memory & Cognition, 8, 631-642.
• Brysbaert, M., & New, B. (2009). Moving beyond Kucera and Francis: A critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English. Behavior Research Methods, 41, 977-990.
• Brysbaert, M., & Stevens, M. (2018). Power analysis and effect size in mixed effects models: A tutorial. Journal of Cognition, 1, 9.
• Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014). Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods, 46, 904-911.
• Coltheart, M., Davelaar, E., Jonasson, J. T., & Besner, D. (1977). Access to the internal lexicon. In S. Dornic (Ed.), Attention and performance VI. Hillsdale, NJ: Erlbaum.
• Kuperman, V., Stadthagen-Gonzalez, H., & Brysbaert, M. (2012). Age-of-acquisition ratings for 30,000 English words. Behavior Research Methods, 44, 978-990.
• Kutas, M., & Hillyard, S. A. (1984). Brain potentials during reading reflect word expectancy and semantic association. Nature, 307, 161-163.
• Lund, K., & Burgess, C. (1996). Producing high-dimensional semantic spaces from lexical co-occurrence. Behavior Research Methods, Instruments, & Computers, 28, 203-208.
• Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124, 372-422.
• Rayner, K. (2009). Eye movements and attention in reading, scene perception, and visual search. Quarterly Journal of Experimental Psychology, 62, 1457-1506.
• Schütze, C. T., & Sprouse, J. (2014). Judgment data. In R. J. Podesva & D. Sharma (Eds.), Research methods in linguistics. Cambridge University Press.
• Sprouse, J. (2011). A test of the cognitive assumptions of magnitude estimation: Commutativity does not hold for acceptability judgments. Language, 87, 274-288.

See references/lexical-databases-guide.md for detailed instructions on accessing and querying lexical control databases.