PADP 7120 Data Applications in PA

class: center, middle, inverse, title-slide

.title[
# PADP 7120 Data Applications in PA
]
.subtitle[
## Data
]
.author[
### Alex Combs
]
.institute[
### UGA | SPIA | PADP
]
.date[
### Last updated: January 15, 2025
]

---

# Outline

- Identify the unit of analysis

- Identify types of variables

- Identify types of data structures

---
# Rectangular data

- Rectangular data consists of

- Observations of a unit of analysis
  - Variables

- **Unit of analysis**:  The subject/entity generating the data. Each row contains an observation of a member of the unit of analysis.

- **Variable**:  A characteristic measured of each member of the unit of analysis. Each column contains a variable.

---
# Rectangular data

<table>
 <thead>
  <tr>
   <th style="text-align:left;"> Variable_1 </th>
   <th style="text-align:left;"> Variable_2 </th>
   <th style="text-align:left;"> Variable_3 </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Member 1 of Unit of Analysis </td>
   <td style="text-align:left;"> Datum </td>
   <td style="text-align:left;"> Datum </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Member 2 of Unit of Analysis </td>
   <td style="text-align:left;"> Datum </td>
   <td style="text-align:left;"> Datum </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Member 3 of Unit of Analysis </td>
   <td style="text-align:left;"> Datum </td>
   <td style="text-align:left;"> Datum </td>
  </tr>
</tbody>
</table>

- Each row is (or should be) an **observation**

- Each column is (or should be) a **variable**

---
# Identify the unit of analysis

- What is the likely unit of analysis based on the variable names?

<table>
 <thead>
  <tr>
   <th style="text-align:left;"> Var1 </th>
   <th style="text-align:left;"> GDP </th>
   <th style="text-align:left;"> Population </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Unit of Analysis </td>
   <td style="text-align:left;"> Datum </td>
   <td style="text-align:left;"> Datum </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Unit </td>
   <td style="text-align:left;"> Datum </td>
   <td style="text-align:left;"> Datum </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Unit </td>
   <td style="text-align:left;"> Datum </td>
   <td style="text-align:left;"> Datum </td>
  </tr>
</tbody>
</table>

---
# Identify the unit of analysis

- What is the likely unit of analysis based on the variable names?

<table>
 <thead>
  <tr>
   <th style="text-align:left;"> Var1 </th>
   <th style="text-align:left;"> GRE_Score </th>
   <th style="text-align:left;"> Income </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Unit of Analysis </td>
   <td style="text-align:left;"> Datum </td>
   <td style="text-align:left;"> Datum </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Unit </td>
   <td style="text-align:left;"> Datum </td>
   <td style="text-align:left;"> Datum </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Unit </td>
   <td style="text-align:left;"> Datum </td>
   <td style="text-align:left;"> Datum </td>
  </tr>
</tbody>
</table>

---
# Identify the unit of analysis

- What is the unit of analysis based on the variable names?

<table>
 <thead>
  <tr>
   <th style="text-align:right;"> Year </th>
   <th style="text-align:left;"> Unemployment </th>
   <th style="text-align:left;"> Poverty </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:right;"> 2017 </td>
   <td style="text-align:left;"> Datum </td>
   <td style="text-align:left;"> Datum </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 2018 </td>
   <td style="text-align:left;"> Datum </td>
   <td style="text-align:left;"> Datum </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 2019 </td>
   <td style="text-align:left;"> Datum </td>
   <td style="text-align:left;"> Datum </td>
  </tr>
</tbody>
</table>

---
# Why care about unit of analysis?

- Important to know what level is observed or measured. For example, individuals vs. an aggregation of individuals within a city, county, state, etc.

- Spreadsheets will sometimes combine units of analysis. For example, state totals calculated from multiple county totals.

- Be careful not to accidentally include a unit analysis that does not belong in the data

---
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# Suppose we want to study homelessness in Athens-Clarke County (potential causes or correlates, demographics, government performance metrics, etc.). We need to collect data.

---
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# What variables might we collect if the unit of analysis is **individuals**?

---
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# What variables might we collect if the unit of analysis is **years**?

---
# Types of variables

![](lectures_files/variables.png)

---
# Qualitative vs. quantitative

- A **qualitative** or **categorical** variable is a variable that is naturally expressed in words with no intrinsic numerical value

- A **quantitative** or **numerical** variable is a variable that has intrinsic numerical meaning

---
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# Which of our variables are qualitative and which are quantitative?

---
# Types of qualitative variables
.center[
![:scale 40%](lectures_files/categorical.png) ]

- **Nominal** variables take on values that differ by name only

- **Ordinal** variables take on values that can be ranked relative to each other but the difference between rankings has no numerical value

---
# Types of quantitative variables
.center[
![:scale 40%](lectures_files/quantitative.png) ]

- **Discrete** variables take on numeric values that are **countable**, integers (e.g. 0, 1, 2, ...)

- **Continuous** variables take on potentially any value. For example, a percentage ranges between 0 and 100 but can take on infinite values between that range (e.g. 50, 50.4, 50.44, 50.444, ...) even though we round to a finite set of values.

---
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# Which of our qualitative variables are nominal or ordinal. Which of our quantitative variables are discrete or continuous?

---
# Variables and information

- Variables measure a characteristic of the unit of analysis

- Each variable has some amount of information encoded into it

---
# Why variable types matter

- Types of variables inform what kind of visualization and analysis to use.

- For [example](https://coggle.it/diagram/Vxlydu1akQFeqo6-/t/inference).

---
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# Dataset structures

---
# Cross-sectional

- A snapshot in time

- One way to identify the unit of analysis is finding the variable or set of variables that uniquely identifies each row in a dataset.

- What is the unit of analysis?

---
# Pooled cross-sectional

<table>
 <thead>
  <tr>
   <th style="text-align:left;"> country </th>
   <th style="text-align:left;"> continent </th>
   <th style="text-align:right;"> year </th>
   <th style="text-align:right;"> lifeExp </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Algeria </td>
   <td style="text-align:left;"> Africa </td>
   <td style="text-align:right;"> 2002 </td>
   <td style="text-align:right;"> 70.994 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Angola </td>
   <td style="text-align:left;"> Africa </td>
   <td style="text-align:right;"> 2002 </td>
   <td style="text-align:right;"> 41.003 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Argentina </td>
   <td style="text-align:left;"> Americas </td>
   <td style="text-align:right;"> 2007 </td>
   <td style="text-align:right;"> 75.320 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Benin </td>
   <td style="text-align:left;"> Africa </td>
   <td style="text-align:right;"> 2002 </td>
   <td style="text-align:right;"> 54.406 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Bolivia </td>
   <td style="text-align:left;"> Americas </td>
   <td style="text-align:right;"> 2007 </td>
   <td style="text-align:right;"> 65.554 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Botswana </td>
   <td style="text-align:left;"> Africa </td>
   <td style="text-align:right;"> 2002 </td>
   <td style="text-align:right;"> 46.634 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Brazil </td>
   <td style="text-align:left;"> Americas </td>
   <td style="text-align:right;"> 2007 </td>
   <td style="text-align:right;"> 72.390 </td>
  </tr>
</tbody>
</table>

- Multiple cross-sections combined
- Different subjects observed in each cross-section
- What is the unit of analysis?

---
# Time Series

<table>
 <thead>
  <tr>
   <th style="text-align:left;"> country </th>
   <th style="text-align:left;"> continent </th>
   <th style="text-align:right;"> year </th>
   <th style="text-align:right;"> lifeExp </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Argentina </td>
   <td style="text-align:left;"> Americas </td>
   <td style="text-align:right;"> 1977 </td>
   <td style="text-align:right;"> 68.481 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Argentina </td>
   <td style="text-align:left;"> Americas </td>
   <td style="text-align:right;"> 1982 </td>
   <td style="text-align:right;"> 69.942 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Argentina </td>
   <td style="text-align:left;"> Americas </td>
   <td style="text-align:right;"> 1987 </td>
   <td style="text-align:right;"> 70.774 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Argentina </td>
   <td style="text-align:left;"> Americas </td>
   <td style="text-align:right;"> 1992 </td>
   <td style="text-align:right;"> 71.868 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Argentina </td>
   <td style="text-align:left;"> Americas </td>
   <td style="text-align:right;"> 1997 </td>
   <td style="text-align:right;"> 73.275 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Argentina </td>
   <td style="text-align:left;"> Americas </td>
   <td style="text-align:right;"> 2002 </td>
   <td style="text-align:right;"> 74.340 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Argentina </td>
   <td style="text-align:left;"> Americas </td>
   <td style="text-align:right;"> 2007 </td>
   <td style="text-align:right;"> 75.320 </td>
  </tr>
</tbody>
</table>

- One subject across time
- What is the unit of analysis?

---
# Panel or longitudinal data

<table>
 <thead>
  <tr>
   <th style="text-align:left;"> country </th>
   <th style="text-align:left;"> continent </th>
   <th style="text-align:right;"> year </th>
   <th style="text-align:right;"> lifeExp </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Argentina </td>
   <td style="text-align:left;"> Americas </td>
   <td style="text-align:right;"> 1997 </td>
   <td style="text-align:right;"> 73.275 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Argentina </td>
   <td style="text-align:left;"> Americas </td>
   <td style="text-align:right;"> 2002 </td>
   <td style="text-align:right;"> 74.340 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Argentina </td>
   <td style="text-align:left;"> Americas </td>
   <td style="text-align:right;"> 2007 </td>
   <td style="text-align:right;"> 75.320 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Bolivia </td>
   <td style="text-align:left;"> Americas </td>
   <td style="text-align:right;"> 1997 </td>
   <td style="text-align:right;"> 62.050 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Bolivia </td>
   <td style="text-align:left;"> Americas </td>
   <td style="text-align:right;"> 2002 </td>
   <td style="text-align:right;"> 63.883 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Bolivia </td>
   <td style="text-align:left;"> Americas </td>
   <td style="text-align:right;"> 2007 </td>
   <td style="text-align:right;"> 65.554 </td>
  </tr>
</tbody>
</table>

- Same subjects observed across time
- Combination of cross-sectional and time series data
- What is the unit of analysis?