Redo Table 4 & 7

**Instructions for Redoing Table 4 and Table 7**

Please redo **Table 4** and **Table 7** from the actual dataset in **jamovi** and provide screenshots of the work.

## General requirements

1. Use **jamovi only** for this order.
2. Use the **actual dataset**, not hand-entered values.
3. Send screenshots of:

* the dataset in jamovi with variable names visible
* the analysis setup screen
* the output/results screen
4. Send the final tables in **APA format**.
5. Send a brief interpretation of each table.
6. Please confirm the **reference group** used in Table 7.
7. Do not guess values or recreate the tables manually without software output.

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## Table 4 — Descriptive Statistics by Firm Type

### Goal

Redo Table 4 using descriptive statistics by **firm_type**.

### Variables to include

* `log_scope1_emissions`
* `log_scope2_emissions`
* `revenue_cagr_5yr`
* `roa`
* `governance_transparency_score`
* `stakeholder_integration_index`
* `firm_size_log_assets`
* `public_status`

### Grouping variable

* `firm_type`

### What Table 4 should show

For each firm type group and total sample, show:

* **Mean (M)**
* **Standard Deviation (SD)**
* **N**

### jamovi steps

1. Open the dataset in jamovi.
2. Go to **Analyses**.
3. Select **Exploration**.
4. Select **Descriptives**.
5. Move the variables listed above into the **Variables** box.
6. Move `firm_type` into **Split by**.
7. Under statistics, select:

* N
* Mean
* Standard deviation
* Minimum
* Maximum
8. For `public_status`, if needed, also run a frequency or descriptive check so the percentage public in each group can be reported correctly.

### Screenshots needed for Table 4

* dataset with variable names visible
* Descriptives setup screen
* output screen with the results visible

### Final deliverables for Table 4

* APA-formatted Table 4
* screenshots
* brief interpretation

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## Table 7 — Linear Mixed Model Results for Main Effects

### Goal

Redo Table 7 using a **linear mixed model** in jamovi.

### Dependent variables

Run **three separate models**:

* **Model 1:** `log_scope1_emissions`
* **Model 2:** `roa`
* **Model 3:** `stakeholder_integration_index`

### Predictors / controls

Use:

* `firm_type`
* `firm_size_log_assets`
* `public_status`
* `year`

### Random effect / repeated unit

Use:

* `firm_id` as the firm-level clustering / random intercept variable

### jamovi setup

If needed, install the mixed model module in jamovi first.

Then:

1. Open the dataset in jamovi.
2. Go to **Analyses**.
3. Open the **Mixed Models / Linear Mixed Model** option.
4. For each model, set:

* Dependent variable = one of the three outcomes above
* Factors = `firm_type`, `public_status`
* Covariates = `firm_size_log_assets`, `year`
* Cluster / random effect / subject = `firm_id`
5. Include a **random intercept** for firm.
6. Include **year fixed effects** if the model setup allows that directly through the year variable.
7. Clearly confirm which **reference group** is used for `firm_type`.

### What Table 7 should report

For each model, provide:

* **unstandardized coefficients (B)**
* **standard errors (SE)**
* significance markers if available
* model fit values if available
* N

### Screenshots needed for Table 7

* dataset with the model variables visible
* model setup screen
* output/results screen
* if there are multiple output pages, send the main coefficient table and the model information page

### Final deliverables for Table 7

* APA-formatted Table 7
* screenshots
* brief interpretation
* note confirming the reference group used for `firm_type`

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## Important consistency rules

Please use these exact labels consistently:

* **B Corp-Certified**
* **Sustainable Non-Certified**
* **Conventional**

Please do not finalize the dissertation wording from these tables until the table output is confirmed and matches the final reference group and model setup.

## Final package to send back

Please send:

1. Table 4 in APA format
2. Table 7 in APA format
3. all required jamovi screenshots
4. brief interpretation for each table
5. confirmation that the tables were produced from the actual dataset in jamovi