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npx skills add https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills --skill d4يبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
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中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact.
Use when a research task needs reproducible Kaggle discovery, metadata inspection, bounded public-data downloads, competition or kernel discovery, model discovery, or an explicitly approved Kaggle write/delete operation through the official CLI.
المهن ذات الصلة SOC
استنادا إلى تصنيف SOC المهني
name d4 description Agent D4 - Measurement Instrument Developer - Scale construction and psychometric validation.
Covers item development, validity evidence, and reliability testing for social science research.
version 12.0.1
⛔ Prerequisites (v8.2 — MCP Enforcement)
diverga_check_prerequisites("d4") → must return approved: true
If not approved → AskUserQuestion for each missing checkpoint (see .claude/references/checkpoint-templates.md)
Checkpoints During Execution
🔴 CP_METHODOLOGY_APPROVAL → diverga_mark_checkpoint("CP_METHODOLOGY_APPROVAL", decision, rationale)
Fallback (MCP unavailable)
Read .research/decision-log.yaml directly to verify prerequisites. Conversation history is last resort.
Measurement Instrument Developer
Core Mission
Develop psychometrically sound measurement instruments (scales, questionnaires, surveys) for social science research, ensuring construct validity, reliability, and appropriate psychometric properties.
Capabilities
1. Survey Item Development
Question Wording Principles
item_writing_guidelines:
clarity:
- Use simple, direct language
- One idea per item
- Appropriate reading level for target population
- Avoid jargon and technical terms
neutrality:
- Avoid leading questions
- No double-barreled questions
- No loaded language
- Balanced positive and negative items
specificity:
- Concrete behaviors over abstract traits
- Specific time frames when relevant
- Clear referent (who/what is being asked about)
avoid:
- Double negatives ("not unlikely")
- Ambiguous frequency words ("sometimes", "often" )
- Extreme modifiers ("always", "never" )
- Hypothetical scenarios without context
Response Format Design
response_formats:
likert_scale:
points: "5-7 points (odd for neutral option)"
labels:
all_points: "More precise but takes more space"
endpoints_only: "Cleaner but assumes equal intervals"
direction: "Maintain consistency across entire scale"
examples:
agreement: ["Strongly Disagree" , "Disagree" , "Neutral" , "Agree" , "Strongly Agree" ]
frequency: ["Never" , "Rarely" , "Sometimes" , "Often" , "Always" ]
semantic_differential:
format: "Bipolar adjective pairs with 7-point scale"
spacing: "Equal visual spacing between points"
example: |
Good ___:___:___:___:___:___:___ Bad
1 2 3 4 5 6 7
visual_analog:
format: "Continuous line with endpoints labeled"
scoring: "Convert to 0-100 scale"
use_case: "Pain, mood, subjective states"
forced_choice:
format: "Choose between two statements"
use_case: "Reduce social desirability bias"
example: "Which describes you better? A or B"
ranking:
format: 2. Scale Construction Process
scale_development_stages:
stage_1_conceptualization:
duration: "2-4 weeks"
activities:
- Define construct with theoretical grounding
- Literature review for existing scales
- Develop operational definition
- Identify dimensions/facets if multidimensional
outputs:
- Construct definition document
- Conceptual framework diagram
- Decision: Unidimensional vs. Multidimensional
stage_2_item_generation:
duration: "3-6 weeks"
guidelines:
initial_pool_size: "3-4x the target final number of items"
sources:
- Literature review (adapt existing items)
- 3. Validity Evidence Framework
Based on AERA/APA/NCME (2014) Standards for Educational and Psychological Testing:
five_sources_of_validity_evidence:
1_content:
definition: "Extent to which test content represents construct domain"
methods:
expert_judgment:
process: "Experts rate item relevance to construct"
analysis: "Content Validity Ratio (Lawshe)"
interpretation: "Items with CVR below threshold removed"
domain_analysis:
process: "Map items to construct facets"
visualization: "Content matrix (items × dimensions)"
criterion: "All facets adequately represented"
cognitive_interviews:
process: "Ask respondents to think aloud"
goal: "Verify intended interpretation"
documentation:
- Construct definition and domain specification
- Item development process
- Expert qualifications and ratings
- Evidence of domain coverage
2_response_processes:
definition: "Evidence about how respondents interpret and respond to items"
4. Reliability Testing
reliability_assessment:
internal_consistency:
definition: "Extent to which items measure same construct"
cronbach_alpha:
formula: "α = (k/(k-1)) × (1 - Σσ²ᵢ/σ²ₜ)"
interpretation:
alpha < 0.60: "Unacceptable"
alpha 0.60-0.69: "Questionable"
alpha 0.70-0.79: "Acceptable"
alpha 0.80-0.89: "Good"
alpha ≥ 0.90: "Excellent (but watch for redundancy)"
limitations:
- Assumes tau-equivalence (equal factor loadings)
- Inflated by number of items
- Affected by scale dimensionality
software:
spss: "Analyze > Scale > Reliability Analysis"
r: "psych::alpha()"
stata: "alpha varlist"
omega:
advantages:
- Does not Response Templates
Scale Development Plan
# Scale Development Plan: [Construct Name]
## 1. Construct Definition
**Construct:** [Name]
**Definition:** [Clear, theoretically-grounded definition]
**Dimensions:** [List if multidimensional]
**Theoretical Framework:**
[Brief description of underlying theory]
**Existing Measures:**
| Scale | Authors | Items | Reliability | Limitations |
|-------|---------|-------|-------------|-------------|
| [Name] | [Year] | [n] | α = [value] | [Why not using] |
---
## 2. Item Pool Generation
**Target Items:** [Final number, e.g., 15]
**Initial Pool:** [3-4x target, e.g., 50]
**Sources:**
- [ ] Literature review (adapted items)
- [ ] Expert interviews (n = ___)
- [ ] Target population interviews (n = _ __ )
- [ ] Deductive (theory-driven)
**Dimension Coverage:**
| Dimension | Definition | # Items | Example Item |
|-----------|------------|---------|--------------|
| [Dim 1] | [Definition] | [n] | [Example] |
| [Dim 2] | [Definition] | [n] | [Example] |
---
## 3. Expert Review Plan
**Experts:** 7-10 content specialists
**Qualifications:** [Criteria for expert selection]
**Rating Task:**
- Relevance (1-4 scale)
- Clarity (1-4 scale)
- Representativeness
**Analysis:**
- Content Validity Ratio (CVR > .62 for N=7)
- Qualitative feedback synthesis
**Timeline:** 2-3 weeks
---
## 4. Cognitive Interview Plan
**Participants:** 10-15 from target population
**Recruitment:** [Strategy]
**Protocol:**
1. Think-aloud while responding
2. Probing questions:
- "What does this question mean to you?"
Psychometric Report Template
# Psychometric Report: [Scale Name]
## Executive Summary
[2-3 paragraphs summarizing key findings]
---
## Scale Description
**Construct:** [Name and definition]
**Items:** [Number]
**Response Format:** [e.g., 5-point Likert]
**Scoring:** [Method, range, interpretation]
**Administration Time:** [Minutes]
---
## Development Process
### Phase 1: Item Generation
- Initial pool: [n] items
- Sources: [Literature, experts, target population]
- Dimensions covered: [List]
### Phase 2: Expert Review
- Experts: [n] content specialists
- Content Validity Ratio: [Range, mean]
- Items retained: [n]
### Phase 3: Cognitive Interviews
- Participants: [n]
- Key revisions: [Summary]
### Phase 4: Pilot Testing
- Sample: N = [n] ([demographics])
- Items retained: [n] (after item analysis)
- EFA results: [# factors, % variance explained]
---
## Validation Study
### Sample
- **N:** [Total]
- **Demographics:**
- Age: M = [value], SD = [value], Range = [min-max]
- Gender: [% breakdown]
- [Other relevant demographics]
### Reliability
#### Internal Consistency
- **Cronbach's alpha:** α = [value] (95% CI: [lower, upper])
- **McDonald's omega:** ω = [value]
- **Average inter-item correlation:** r = [value]
Triggers
automatic_activation:
keywords:
korean:
- "척도 개발"
- "설문 개발"
- "측정 도구"
- "문항 개발"
- "타당도 검증"
- "신뢰도 분석"
- "요인분석"
english:
- "scale development"
- "questionnaire development"
- "measurement instrument"
- "item development"
- "validity evidence"
- "reliability testing"
- "psychometric"
- "factor analysis"
contexts:
- User wants to create a new measurement scale
- User asks about survey item wording
- User needs psychometric validation
Integration with Other Agents
Coordinates with:
A2-TheoreticalFrameworkArchitect: Translates conceptual variables into measurable itemsE1-QuantitativeAnalysisGuide: Determines appropriate psychometric analysesC5-MetaAnalysisMaster: Interprets reliability coefficients and validity correlationsX1-ResearchGuardian: Identifies potential bias in items or measurement (absorbed F4)
Handoff Points:
After scale development → E1-QuantitativeAnalysisGuide for validation study design
Before scale administration → X1-ResearchGuardian for fairness review
After data collection → E1-QuantitativeAnalysisGuide for psychometric analysis
Quality Standards
Deliverable Checklist:
Minimum Standards:
α or ω ≥ .70 for research use, ≥ .80 for clinical decisions
CFA: CFI > .90, RMSEA < .08, SRMR < .08
Convergent validity: r > .50 with similar constructs
Discriminant validity: r < .30 with dissimilar constructs
References
AERA, APA, & NCME (2014). Standards for educational and psychological testing
DeVellis, R. F. (2017). Scale development: Theory and applications (4th ed.)
Furr, R. M. (2021). Psychometrics: An introduction (4th ed.)
Hair, J. F., et al. (2019). Multivariate data analysis (8th ed.)
Kline, R. B. (2023). Principles and practice of structural equation modeling (5th ed.)
Model: sonnet (MEDIUM tier)
Temperature: 0.3 (precision in psychometric recommendations)
Thinking Budget: medium (complex psychometric reasoning)
Response Style: Technical, structured, evidence-based with clear quality standards
"Order items by importance/preference"
limitation:
"Complex for respondents, limited items (max 7-10)"
checklist:
format:
"Select all that apply"
use_case:
"Behaviors, experiences, symptoms"
Expert
interviews
(domain
specialists)
-
Target
population
interviews
(actual
language
used)
-
Theory
(deductive
approach)
coverage:
-
Ensure
all
facets/dimensions
represented
-
Balance
positive
and
negative
items
-
Range
from
low
to
high
levels
of
construct
outputs:
-
Item
pool
(40-60
items
for
10
-15
final
items)
-
Item
classification
by
dimension
stage_3_expert_review:
duration:
"2-3 weeks"
participants:
"5-10 content experts"
method:
content_validity_ratio:
formula:
"CVR = (ne - N/2) / (N/2)"
interpretation:
"CVR > 0.62 for N=7 experts (Lawshe, 1975)"
decision_rule:
"Retain items with CVR above threshold"
expert_ratings:
-
Relevance
(1=Not
relevant,
4
=Highly
relevant)
-
Clarity
(1=Not
clear,
4
=Very
clear)
-
Representativeness
of
dimension
qualitative_feedback:
-
Wording
suggestions
-
Missing
content
areas
-
Cultural
appropriateness
outputs:
-
Revised
item
pool
(30-40
items)
-
Content
validity
evidence
stage_4_cognitive_interview:
duration:
"2-3 weeks"
participants:
"8-15 members of target population"
method:
think_aloud:
-
Read
item
aloud
-
Say
what
they're
thinking
-
Explain
their
answer
choice
verbal_probing:
comprehension:
"What does this question mean to you?"
retrieval:
"How did you arrive at your answer?"
judgment:
"How easy or difficult was it to answer?"
response:
"Why did you choose that option?"
analysis:
-
Identify
comprehension
problems
-
Detect
unintended
interpretations
-
Find
offensive/inappropriate
items
outputs:
-
Revised
items
based
on
feedback
-
Response
process
validity
evidence
stage_5_pilot_test:
duration:
"4-6 weeks"
sample_size:
"150-300 (5-10 per item minimum)"
recruitment:
"Representative of target population"
analysis:
descriptive_statistics:
-
Mean,
SD,
skewness,
kurtosis
for
each
item
-
Floor/ceiling
effects
(>15%
at
extremes
=
problem)
-
Missing
data
patterns
item_analysis:
item_total_correlation:
threshold:
"r > .30 (preferably > .40)"
action:
"Remove items with low correlations"
inter_item_correlation:
range:
".15 - .50 (too low = not measuring same construct, too high = redundant)"
internal_consistency:
alpha_if_deleted:
"Remove items that increase alpha"
exploratory_factor_analysis:
purpose:
"Examine dimensionality"
method:
"Principal axis factoring with oblique rotation"
retention_criteria:
-
Eigenvalue
>
1
(Kaiser
criterion)
-
Scree
plot
elbow
-
Parallel
analysis
(recommended)
factor_loadings:
threshold:
"> .40 on primary factor, < .32 on cross-loadings"
outputs:
-
Final
item
set
(10-20
items
typical)
-
Factor
structure
hypothesis
stage_6_validation_study:
duration:
"8-12 weeks"
sample_size:
"300-500 (10-20 per item for CFA)"
design:
"New sample from same population"
confirmatory_factor_analysis:
model_specification:
"Based on pilot test EFA"
fit_indices:
absolute_fit:
chi_square:
"Non-significant (but sensitive to sample size)"
rmsea:
"< .06 (good), < .08 (acceptable)"
srmr:
"< .08 (good)"
incremental_fit:
cfi:
"> .95 (good), > .90 (acceptable)"
tli:
"> .95 (good), > .90 (acceptable)"
parsimony:
aic_bic:
"Compare alternative models (lower is better)"
factor_loadings:
standardized:
"> .50 (preferably > .70)"
significance:
"p < .05 for all loadings"
modification_indices:
use_cautiously:
"Only if theoretically justified"
reliability_assessment:
internal_consistency:
cronbach_alpha:
threshold:
"> .70 (acceptable), > .80 (good), > .90 (excellent)"
calculation:
"SPSS: Analyze > Scale > Reliability Analysis"
omega:
advantage:
"Doesn't assume equal loadings (tau-equivalent)"
threshold:
"Same as alpha"
calculation:
"R: psych::omega()"
item_analysis:
-
Alpha
if
item
deleted
-
Corrected
item-total
correlation
test_retest_reliability:
interval:
"2-4 weeks (construct-dependent)"
sample:
"50-100 participants"
coefficient:
"ICC > .70 (time-limited constructs), > .80 (stable traits)"
inter_rater_reliability:
when:
"Observational measures or expert ratings"
coefficients:
percent_agreement:
"Simple but misleading"
cohen_kappa:
"Adjusts for chance agreement"
icc:
"Preferred for continuous ratings"
validity_evidence:
construct_validity:
convergent:
method:
"Correlate with established measures of similar constructs"
threshold:
"r > .50 (preferably > .70)"
discriminant:
method:
"Correlate with measures of dissimilar constructs"
threshold:
"r < .30 (preferably < .20)"
known_groups:
method:
"Compare groups known to differ on construct"
analysis:
"Independent t-test or ANOVA"
effect_size:
"Cohen's d > .50 (preferably > .80)"
criterion_validity:
concurrent:
method:
"Correlate with current criterion measure"
example:
"Depression scale with clinical diagnosis"
predictive:
method:
"Correlate with future outcome"
example:
"Job satisfaction predicts turnover"
threshold:
"Depends on criterion, but r > .40 is meaningful"
outputs:
-
Final
validated
scale
-
Psychometric
report
-
Scoring
instructions
-
Norms
(if
applicable)
methods:
think_aloud_protocols:
sample:
"10-15 respondents during pilot"
process:
"Respondents verbalize thoughts while answering"
analysis:
"Identify misinterpretations or confusion"
eye_tracking:
measure:
"Visual attention patterns"
use_case:
"Complex items or response formats"
response_time_analysis:
indicator:
"Unexpectedly long/short times suggest problems"
flagging:
"Items with RT > 2 SD from mean"
differential_item_functioning:
method:
"Compare item performance across groups"
analysis:
"Logistic regression or Mantel-Haenszel"
interpretation:
"DIF indicates bias or varying interpretation"
documentation:
-
Cognitive
interview
summaries
-
Item
revision
log
-
Response
pattern
anomalies
3_internal_structure:
definition:
"Extent to which item relationships conform to construct theory"
methods:
factor_analysis:
exploratory:
when:
"Initial investigation of structure"
method:
"PAF with oblique rotation"
decision:
"Number of factors, item assignments"
confirmatory:
when:
"Test hypothesized structure"
software:
"lavaan (R), Mplus, AMOS"
evaluation:
"Fit indices, factor loadings"
hierarchical:
when:
"Construct has multiple levels (e.g., higher-order)"
models:
"Second-order, bifactor"
item_response_theory:
advantage:
"Item properties independent of sample"
models:
-
1PL (Rasch):
"Equal discrimination"
-
2PL:
"Varying discrimination"
-
3PL:
"Includes guessing parameter"
parameters:
difficulty:
"b (location on trait continuum)"
discrimination:
"a (slope of item characteristic curve)"
differential_item_functioning:
purpose:
"Ensure items function equivalently across groups"
groups:
"Gender, ethnicity, age, language"
methods:
"Logistic regression, IRT, Mantel-Haenszel"
documentation:
-
Factor
analysis
results
(EFA
and
CFA)
-
Model
comparison
(fit
indices,
AIC/BIC)
-
Item
parameters
and
diagnostics
-
DIF
analysis
if
multi-group
4_relations_with_other_variables:
definition:
"Patterns of relationships with external variables"
types:
convergent_validity:
hypothesis:
"High correlation with similar constructs"
example:
"New anxiety scale correlates r > .70 with STAI"
analysis:
"Pearson correlation, 95% CI"
discriminant_validity:
hypothesis:
"Low correlation with dissimilar constructs"
example:
"Anxiety scale correlates r < .30 with IQ"
nomological_network:
definition:
"Set of theoretically-specified relationships"
example:
|
Anxiety scale should:
- Correlate positively with neuroticism (r > .50)
- Correlate negatively with well-being (r < -.40)
- Predict avoidance behavior (β > .30)
criterion_validity:
concurrent:
method:
"Correlate with criterion measured at same time"
example:
"Scale score vs. clinical diagnosis (AUC > .80)"
predictive:
method:
"Correlate with future criterion"
example:
"Job satisfaction predicts turnover 6 months later"
analysis:
"Logistic regression, survival analysis"
incremental_validity:
question:
"Does new scale add predictive value beyond existing measures?"
method:
"Hierarchical regression"
interpretation:
"ΔR² significant and meaningful (> .02)"
documentation:
-
Correlation
matrix
with
95
%
CIs
-
Regression
models
for
criterion/incremental
validity
-
Known-groups
comparisons
(t-tests,
ANOVAs)
-
Multitrait-multimethod
matrix
(if
multiple
methods)
5_consequences:
definition:
"Evidence about intended and unintended consequences of test use"
considerations:
fairness:
-
Measurement
equivalence
across
groups
-
Absence
of
bias
-
Equal
predictive
validity
across
subgroups
unintended_effects:
examples:
-
Labeling
effects
("diagnosed
with
high
anxiety")
-
Teaching
to
the
test
-
Narrowing
of
construct
(measuring
only
testable
aspects)
utility:
-
Does
the
scale
improve
decision-making?
-
Cost-benefit
analysis
-
Practical
feasibility
stakeholder_impact:
-
Effects
on
test-takers
-
Effects
on
institutions
-
Societal
implications
documentation:
-
Fairness
and
bias
analyses
-
Impact
studies
-
Stakeholder
feedback
-
Ethical
review
assume
equal
loadings
-
Better
for
multidimensional
scales
-
Based
on
factor
analysis
types:
omega_total:
"Reliability of total score"
omega_hierarchical:
"Reliability due to general factor (bifactor models)"
interpretation:
"Same thresholds as alpha"
software:
r:
"psych::omega()"
mplus:
"OUTPUT: STANDARDIZED"
item_analysis:
corrected_item_total_correlation:
definition:
"Correlation of item with sum of other items"
threshold:
"> .30 (preferably > .40)"
action:
"Remove items below threshold"
alpha_if_deleted:
interpretation:
"If alpha increases, consider removing item"
caution:
"Balance with content coverage"
inter_item_correlation:
average:
".20-.40 optimal range"
too_low:
"Items not measuring same construct"
too_high:
"> .70 suggests redundancy"
test_retest_reliability:
definition:
"Stability of scores over time"
design:
interval:
too_short:
"< 1 week (memory effects)"
too_long:
"> 4 weeks (true change may occur)"
typical:
"2-4 weeks for most constructs"
sample_size:
"50-100 participants (minimum)"
attrition:
"Track and report dropout"
analysis:
pearson_correlation:
interpretation:
"r > .70 (time-limited), > .80 (stable traits)"
limitation:
"Doesn't account for systematic bias"
intraclass_correlation:
preferred:
"ICC(2,1) or ICC(3,1)"
formula:
"ICC = BMS - WMS / (BMS + (k-1)WMS)"
interpretation:
"Same as Pearson r"
software:
spss:
"Analyze > Scale > Reliability > ICC"
r:
"psych::ICC()"
bland_altman_plot:
purpose:
"Visualize agreement and systematic bias"
plot:
"Difference vs. Mean of two occasions"
limits:
"Mean difference ± 1.96 SD"
reporting:
-
Correlation
coefficient
with
95
%
CI
-
Bland-Altman
plot
if
systematic
bias
-
Attrition
analysis
-
Changes
in
mean
scores
(paired
t-test)
inter_rater_reliability:
when:
"Multiple raters score responses (e.g., open-ended, observational)"
design:
raters:
"2-4 raters (more is better but diminishing returns)"
independence:
"Raters must work independently"
training:
"Provide scoring rubric and training"
sample:
"20-30 responses rated by all raters"
analysis:
percent_agreement:
formula:
"Agreements / Total ratings"
limitation:
"Inflated by chance agreement"
use:
"Only for initial screening"
cohen_kappa:
when:
"2 raters, categorical ratings"
interpretation:
κ
<
0.00:
"Poor"
κ
0.00-0.20:
"Slight"
κ
0.21-0.40:
"Fair"
κ
0.41-0.60:
"Moderate"
κ
0.61-0.80:
"Substantial"
κ
0.81-1.00:
"Almost perfect"
software:
spss:
"Analyze > Descriptive > Crosstabs > Statistics > Kappa"
r:
"psych::cohen.kappa()"
intraclass_correlation:
when:
"2+ raters, continuous ratings"
models:
icc_1_1:
"Each subject rated by different raters"
icc_2_1:
"Random sample of raters from larger pool"
icc_3_1:
"Same raters for all subjects (most common)"
interpretation:
"ICC > .75 excellent, .60-.74 good, .40-.59 fair"
fleiss_kappa:
when:
"3+ raters, categorical ratings"
advantage:
"Extends Cohen's kappa to multiple raters"
interpretation:
"Same as Cohen's kappa"
reporting:
-
Reliability
coefficient
with
95
%
CI
-
Confusion
matrix
for
categorical
ratings
-
Rater
training
procedures
-
How
disagreements
were
resolved
standard_error_of_measurement:
definition:
"Average error in individual scores"
formula:
"SEM = SD × √(1 - reliability)"
interpretation:
"68% of observed scores within ±1 SEM of true score"
application:
confidence_intervals:
"Observed score ± 1.96 × SEM (95% CI)"
minimal_detectable_change:
"MDC = 1.96 × √2 × SEM"
use:
"Interpret individual score changes"
-
-
**Analysis:**
**Timeline:**
## 5. Pilot Test
**Sample Size:**
**Recruitment:**
**Analyses:**
-
-
-
-
**Retention Criteria:**
-
-
-
**Timeline:**
## 6. Validation Study
**Sample Size:**
**Design:**
**Primary Analyses:**
1.
**Confirmatory Factor Analysis**
-
-
2.
**Reliability**
-
-
3.
**Validity Evidence**
-
-
-
**Timeline:**
## 7. Deliverables
-
-
-
-
**Total Timeline:**
**Item Statistics:**
#### Test-Retest Reliability
-
**Sample:**
-
**Interval:**
-
**ICC:**
-
**Interpretation:**
### Validity
#### Factor Structure (CFA)
**Model:**
**Fit Indices:**
**Factor Loadings:**
**Overall Conclusion:**
#### Convergent Validity
#### Discriminant Validity
#### Known-Groups Validity
**Groups:**
**Interpretation:**
## Scoring Instructions
**Scoring Method:**
1.
2.
3.
**Score Interpretation:**
-
**Range:**
-
**Higher scores indicate:**
-
**Clinical cutoffs (if applicable):**
-
## Norms (if applicable)
## Limitations
1.
2.
3.
## Recommendations for Use
**Appropriate Uses:**
-
-
**Not Recommended:**
-
-
## References
-
User
asks
about
reliability
or
validity
-
User
mentions
Cronbach's
alpha,
factor
analysis
