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# Data Coding Principles for 4D Latent Trait Model
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## V4 Implementation (Current Version)
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**As of V4 (2025-11-18), manifesto items implement the bipolar bridge structure described in this document.**
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**Key Changes from V3.x**:
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- ✅ Manifesto items now load on TWO dimensions (bipolar bridges)
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- ✅ Data format: `type_high` and `type_low` columns replace single `type`
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- ✅ Stan model: Unified `Gamma_man` matrix replaces per-dimension arrays
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- ✅ Measurement consistency: Manifesto items match expert data structure
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**Why V4?**
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- Better identification (each observation informs two dimensions)
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- Estimated correlations (not imposed by construction)
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- No double-counting (each quasi-sentence counted once)
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See CHANGELOG.md for full V4 migration details.
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---
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## Model Structure Overview
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The model estimates **four unipolar latent dimensions**:
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- **pro_market**: Support for market liberalization
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- **pro_welfare**: Support for welfare state expansion
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- **cosmopolitan**: Support for internationalism, diversity, openness
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- **traditional**: Support for nationalism, security, traditional values
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These are **separate dimensions**, not two bipolar scales. Correlations between dimensions (e.g., cosmopolitan-traditional) are **estimated empirically**, not imposed by construction.
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---
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## Item Types and Loading Structure
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### 1. Bipolar Bridge Items
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**Definition**: Items where the sample includes mentions of BOTH sides of an issue, and "positive" counts mentions favoring one pole.
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**Structure**:
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- `sample` = mentions of issue (any direction)
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- `positive` = mentions favoring one pole
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- `positive/sample` ratio varies from 0 to 1
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**Loading**: Should load on **ONE dimension only**
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**Examples**:
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**Manifesto Data**:
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```
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var: "Multiculturalism"
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type: "cosmopolitan"
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sample: per607 (pro-multiculturalism) + per608 (anti-multiculturalism)
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positive: per607 (pro-multiculturalism)
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```
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- High ratio → high cosmopolitan (party favors multiculturalism)
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- Low ratio → low cosmopolitan (party opposes multiculturalism)
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- Anti-multiculturalism is **implicitly measured** as (sample - positive)
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**PolDem Data**:
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```
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var: "Immigration (Media)"
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type: "cosmopolitan"
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sample: all immigration mentions (direction != 0)
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positive: pro-immigration mentions (direction > 0)
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```
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- High ratio → high cosmopolitan (media coverage shows party supporting immigration)
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- Low ratio → low cosmopolitan (media coverage shows party opposing immigration)
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### 2. Why One Loading Suffices for Bipolar Items
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**Question**: Shouldn't anti-immigration also load on traditional?
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**Answer**: No, because:
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1. **Both poles are already captured**: The bipolar structure means low cosmopolitan (anti-immigration) is automatically measured
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2. **Avoids double-counting**: Each mention/quasi-sentence contributes to exactly ONE item
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3. **Empirical correlations emerge naturally**: If anti-immigration parties also score high on nationalism/law-and-order, the **posterior correlation** between cosmopolitan and traditional will reflect this
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4. **More flexible model**: Cosmopolitan-traditional relationship is **estimated**, not imposed
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**Imposed vs. Estimated Correlation**:
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- If we double-load immigration on both cosmopolitan (negative) and traditional (positive), we **force** them to be opposites
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- By loading only on cosmopolitan, we let the data reveal whether anti-immigration parties are also nationalist (empirical question)
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---
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## Coding Decision Rules
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### Rule 1: Each Manifesto Code Appears in ONE Item Only
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**Good** (current structure):
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```
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"Multiculturalism" (cosmopolitan):
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- per607 (Positive), per608 (Negative)
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"National Identity" (traditional):
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- per601 (Positive), per107 (Negative)
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```
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- per607/per608 only in cosmopolitan
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- per601/per107 only in traditional
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- Correlation between dimensions is empirical
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**Bad** (double-loading):
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```
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"Multiculturalism" (cosmopolitan):
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- per607 (Positive), per601 (Negative)
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"National Identity" (traditional):
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- per601 (Positive), per607 (Negative)
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```
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- per601 and per607 counted twice
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- Imposes perfect negative correlation between cosmopolitan/traditional
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### Rule 2: Stance Assignment Within Items
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Within each item (var), codes are assigned stance based on:
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- **Positive**: Codes indicating support for the item's construct
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- **Negative**: Codes indicating opposition to the item's construct
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**Example - "Internationalism" (cosmopolitan)**:
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- per107 (Internationalism positive): stance = "Positive"
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- per109 (Internationalism negative): stance = "Negative"
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- Result: High per107 / low per109 → high cosmopolitan score
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### Rule 3: PolDem Direction Mapping
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PolDem uses `direction` variable (-1, 0, +1):
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- `direction > 0`: Support for the issue as coded
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- `direction < 0`: Opposition to the issue
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- `direction == 0`: Ambivalent (exclude from analysis)
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**Aggregation**:
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```r
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poldem_processed %>%
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filter(direction != 0) %>% # exclude neutral
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group_by(party, year, country, issue_cat) %>%
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summarise(
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sample = n(), # all non-neutral mentions
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positive = sum(direction > 0) # supportive mentions only
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)
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```
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---
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## Special Cases
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### Immigration (Direction Ambiguity)
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**Codebook says**: "Opposition to restrictive immigration"
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**Interpretation needed**: Does `direction = +1` mean:
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- A) Support for "opposition to restrictions" → pro-immigration (cosmopolitan)
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- B) Support for "restrictions" → anti-immigration (traditional)
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**Resolution**: Must manually inspect sample sentences before finalizing coding.
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If interpretation A is correct:
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```r
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issue_cat == "immig" & direction > 0 → positive for cosmopolitan
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issue_cat == "immig" & direction < 0 → negative for cosmopolitan
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```
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If interpretation B is correct:
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```r
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issue_cat == "immig" & direction > 0 → negative for cosmopolitan
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issue_cat == "immig" & direction < 0 → positive for cosmopolitan
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# (REVERSED)
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```
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### Europe/Euro Items
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EU integration naturally maps to cosmopolitan-traditional dimension:
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**Manifesto Data**:
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- Add new items using per108 (EU integration positive) and per106 (EU integration negative)
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- Create separate vars: "EU Integration Support" (cosmopolitan), "Euroskepticism" (traditional)
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**PolDem Data**:
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```r
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"EU Integration Support (Media)" (cosmopolitan):
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issue_cat = "europe" or "euro"
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sample = all mentions
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positive = direction > 0 (pro-EU)
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"Euroskepticism (Media)" (traditional):
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issue_cat = "europe" or "euro"
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sample = all mentions
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positive = direction < 0 (anti-EU)
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```
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**Note**: Same sentences contribute to BOTH items, but counting opposite directions. This creates natural negative correlation between cosmopolitan/traditional.
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**Alternative approach** (cleaner, recommended): Load only on cosmopolitan:
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```r
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"EU Position (Media)" (cosmopolitan):
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issue_cat = "europe" or "euro"
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sample = all mentions
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positive = direction > 0
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```
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This is sufficient if we treat EU as a bipolar cosmopolitan item.
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---
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## Data Structure Requirements
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### Manifesto Data Format (party-year-var level)
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Each row represents one item for one party-year:
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| party | country | year | var | type | sample | positive | project |
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|-------|---------|------|-----|------|--------|----------|---------|
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| 211 | DE | 2013 | Multiculturalism | cosmopolitan | 45 | 23 | Manifesto |
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| 211 | DE | 2013 | National Identity | traditional | 67 | 58 | Manifesto |
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| 211 | DE | 2013 | Economic Intervention | pro_welfare | 102 | 78 | Manifesto |
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- **var**: Item name (e.g., "Multiculturalism", "Economic Intervention")
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- **type**: Dimension it loads on (pro_market, pro_welfare, cosmopolitan, traditional)
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- **sample**: Total quasi-sentences mentioning this issue
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- **positive**: Quasi-sentences with positive stance toward this item
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### PolDem Data Format (same structure)
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| party | country | year | var | type | sample | positive | project |
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|-------|---------|------|-----|------|--------|----------|---------|
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| 211 | DE | 2013 | Immigration (Media) | cosmopolitan | 23 | 8 | PolDem |
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| 211 | DE | 2013 | Nationalism (Media) | traditional | 15 | 12 | PolDem |
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Combined using `bind_rows()` to create unified dataset.
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---
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## Summary
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1. **Bipolar items load on one dimension only** - the ratio captures both poles
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2. **Each manifesto code appears in exactly one item** - no double-counting
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3. **Correlations between dimensions are estimated, not imposed** - more flexible model
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4. **Direction reversals are handled within items** - via stance assignment (Manifesto) or direction sign (PolDem)
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5. **All items must allow varying positive/sample ratios** - mix of positive and negative stances required
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This structure preserves the conceptual independence of the four dimensions while allowing the data to reveal their empirical relationships.
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