generated from mwc/project_argument
861 lines
109 KiB
Plaintext
861 lines
109 KiB
Plaintext
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "worldwide-blood",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Introduction"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "understanding-numbers",
|
||
"metadata": {},
|
||
"source": [
|
||
"*✏️ Write 2-3 sentences describing your research.*"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "greater-circular",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Overarching Question: What effect does technology usage have on teens?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "appreciated-testimony",
|
||
"metadata": {},
|
||
"source": [
|
||
"I am interested in this topic after recently reading “The Anxious Generation.” by Johnathan Haidt In this book, Haidt, explores how the wide prevalence of the smartphone and social media has rewired childhood for kids during middle school and early high school. His main premise is that increased social media and smartphone usage has led to the massive increases in depression and anxiety we have seen since 2012, which is about the same time the smartphone became readily available. While I think this project will be similar to his work, I will not set out to look at his main premise, but rather explore some other ideas related to teen tech habits. This question also has relevance to my work as a teacher where social media is a daily part of my students’ lives. "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "permanent-pollution",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Data"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"id": "technical-evans",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#Include any import statements you will need\n",
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"id": "overhead-sigma",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"### 💻 FILL IN YOUR DATASET FILE NAME BELOW 💻 ###\n",
|
||
"\n",
|
||
"file_name = \"March 7-April 10, 2018 - Teens and Tech Survey - CSV.csv\"\n",
|
||
"dataset_path = \"data/\" + file_name\n",
|
||
"\n",
|
||
"df = pd.read_csv(dataset_path)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "heated-blade",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>CASEID</th>\n",
|
||
" <th>SURV_LANG</th>\n",
|
||
" <th>FITIN</th>\n",
|
||
" <th>FRIEND1</th>\n",
|
||
" <th>FRIEND2</th>\n",
|
||
" <th>FRIEND3</th>\n",
|
||
" <th>FRIEND4_1</th>\n",
|
||
" <th>FRIEND4_2</th>\n",
|
||
" <th>FRIEND4_3</th>\n",
|
||
" <th>FRIEND4_4</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>HH25</th>\n",
|
||
" <th>HH612</th>\n",
|
||
" <th>HH1317</th>\n",
|
||
" <th>HH18OV</th>\n",
|
||
" <th>CO_DATE</th>\n",
|
||
" <th>DURATION</th>\n",
|
||
" <th>SURV_MODE</th>\n",
|
||
" <th>MODE_END</th>\n",
|
||
" <th>DEVICE</th>\n",
|
||
" <th>WEIGHT</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>2</td>\n",
|
||
" <td>EN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>20180322</td>\n",
|
||
" <td>22.866667</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>Desktop</td>\n",
|
||
" <td>0.794092</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>7</td>\n",
|
||
" <td>EN</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>20180324</td>\n",
|
||
" <td>17.850000</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>Smartphone</td>\n",
|
||
" <td>0.313173</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>9</td>\n",
|
||
" <td>EN</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>20180323</td>\n",
|
||
" <td>12.316667</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>Smartphone</td>\n",
|
||
" <td>0.357175</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>10</td>\n",
|
||
" <td>EN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>20180402</td>\n",
|
||
" <td>9.733333</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>Smartphone</td>\n",
|
||
" <td>0.762108</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>13</td>\n",
|
||
" <td>EN</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>20180308</td>\n",
|
||
" <td>14.633333</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>Smartphone</td>\n",
|
||
" <td>0.842675</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>5 rows × 176 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" CASEID SURV_LANG FITIN FRIEND1 FRIEND2 FRIEND3 FRIEND4_1 FRIEND4_2 \\\n",
|
||
"0 2 EN NaN 3 1 2 0 1 \n",
|
||
"1 7 EN 2.0 2 1 3 1 1 \n",
|
||
"2 9 EN 2.0 2 1 3 0 0 \n",
|
||
"3 10 EN 1.0 4 3 2 0 1 \n",
|
||
"4 13 EN 2.0 4 4 2 0 1 \n",
|
||
"\n",
|
||
" FRIEND4_3 FRIEND4_4 ... HH25 HH612 HH1317 HH18OV CO_DATE DURATION \\\n",
|
||
"0 0 1 ... 1 3 2 4 20180322 22.866667 \n",
|
||
"1 1 0 ... 0 2 2 4 20180324 17.850000 \n",
|
||
"2 0 1 ... 1 2 1 2 20180323 12.316667 \n",
|
||
"3 1 0 ... 0 0 1 2 20180402 9.733333 \n",
|
||
"4 0 0 ... 0 1 2 3 20180308 14.633333 \n",
|
||
"\n",
|
||
" SURV_MODE MODE_END DEVICE WEIGHT \n",
|
||
"0 1 1 Desktop 0.794092 \n",
|
||
"1 2 2 Smartphone 0.313173 \n",
|
||
"2 2 2 Smartphone 0.357175 \n",
|
||
"3 2 2 Smartphone 0.762108 \n",
|
||
"4 2 2 Smartphone 0.842675 \n",
|
||
"\n",
|
||
"[5 rows x 176 columns]"
|
||
]
|
||
},
|
||
"execution_count": 3,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "continental-franklin",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Data Overview**\n",
|
||
"\n",
|
||
"This data set shows the results of a survey by the Pew Research Center given to teens in 2018. Teens were asked various questions about technology and most were answered on a scale of 0-4. The dataset also includes some general demographic information. A key supplements the data which includes the full question and responses for each question. I will refer to those questions and responses in the methods and results below."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "infinite-instrument",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Methods and Results"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "26501d6d-81d8-499c-b226-618e43fee349",
|
||
"metadata": {},
|
||
"source": [
|
||
"First, the data relavant to this study will be extracted into a new data set.\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "basic-canadian",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>AGE</th>\n",
|
||
" <th>INTREQ</th>\n",
|
||
" <th>FITIN</th>\n",
|
||
" <th>SOC1</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>14</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>3.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>15</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>13</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>3.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>17</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>15</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" AGE INTREQ FITIN SOC1\n",
|
||
"0 14 5 NaN 3.0\n",
|
||
"1 15 1 2.0 2.0\n",
|
||
"2 13 2 2.0 3.0\n",
|
||
"3 17 1 1.0 2.0\n",
|
||
"4 15 2 2.0 2.0"
|
||
]
|
||
},
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"new_df=df[['AGE','INTREQ','FITIN','SOC1']]\n",
|
||
"new_df.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "raw",
|
||
"id": "b7ff5919-a432-41dd-9213-10861771dcfe",
|
||
"metadata": {},
|
||
"source": [
|
||
"AGE indicates participant age\n",
|
||
"INTREQ indicates frequency of which a teen uses technology (further defined below)\n",
|
||
"FITIN indicates how well a teen thinks he or she fits in\n",
|
||
"SOC1 indicates what effect teens think social media has on teens their age."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "recognized-positive",
|
||
"metadata": {},
|
||
"source": [
|
||
"## First Research Question: How does the amount of time teens spend on technology vary by age?\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "graduate-palmer",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Methods"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "endless-variation",
|
||
"metadata": {},
|
||
"source": [
|
||
"*Explain how you will approach this research question below. Consider the following:* \n",
|
||
" - *Which aspects of the dataset will you use?* \n",
|
||
" - *How will you reorganize/store the data?* \n",
|
||
" - *What data science tools/functions will you use and why?* \n",
|
||
" \n",
|
||
"✏️ *Write your answer below:*\n",
|
||
"\n",
|
||
"Teens in this survery were grouped into 5 age categories ranging incluisviely from age 13 to age 17. The number of participants in each category was roughly equal as indicated in the table below."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "def017b8-7537-4963-9c50-2f9087260cfb",
|
||
"metadata": {},
|
||
"source": [
|
||
"Figure 1 shows a countplot of number of teens vs. age, and broken down based on their answer to the question \"How frequently do you use technology?\" A countplot was selected here in order to indicate how many participants fit in a certain category.\n",
|
||
"\n",
|
||
"The numeric scores on the legend correspond as follows:\n",
|
||
"1- Almost constantly\n",
|
||
"2- Several Times a Day\n",
|
||
"3- About Once a Day\n",
|
||
"4- Several times a week\n",
|
||
"5- Less often\n",
|
||
"\n",
|
||
"Figure 2 focuses on the most frequent response (almost constantly and several times a day) and then sorts those responses by age to provide another way to look at the most frequent responses in the data."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "portuguese-japan",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Results "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "405135b6-700d-40b8-bc0e-3ed338190f74",
|
||
"metadata": {},
|
||
"source": [
|
||
"Table 1: Percent of Respondents in Each Age Category"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "bdf7dd26-d0c1-469e-9458-bf014cd02200",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"13 0.220727\n",
|
||
"15 0.211306\n",
|
||
"16 0.192463\n",
|
||
"17 0.191117\n",
|
||
"14 0.184388\n",
|
||
"Name: AGE, dtype: float64"
|
||
]
|
||
},
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"new_df.AGE.value_counts(normalize=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c7297da9-bfaf-415d-aa60-b9353564d256",
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"Figure 1: Number of Respondents in Each Age Category Bassed on How Frequently They Utilize Technology"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "negative-highlight",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import seaborn as sns\n",
|
||
"sns.set_theme()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "victorian-burning",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Axes: xlabel='AGE', ylabel='count'>"
|
||
]
|
||
},
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"sns.countplot(data=new_df, x=\"AGE\", hue=\"INTREQ\")\n",
|
||
"\n",
|
||
"\n",
|
||
"# Is there a way to put these labels on the key/legend?\n",
|
||
"#new_labels = [\"Almost Constantly\", \"Several Times a Day\", \"About Once a Day\", \"Several times a week\", \"Less often\"]\n",
|
||
"#handles, labels = get_legend_handles_labels()\n",
|
||
"#legend(handles, new_labels)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "096c0ad1-c7f0-4984-ad86-1b07e4ef6478",
|
||
"metadata": {},
|
||
"source": [
|
||
"Figure 2: Number of Teens That Use Technology Almost Constantly or Several Times Per Day Sorted by Age"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "2eff3a2b-30ca-4cf3-b365-d41acc121ece",
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/var/folders/c3/lngy07lx6hx3_kdztwrlr7j80000gn/T/ipykernel_12977/2489872726.py:3: SettingWithCopyWarning: \n",
|
||
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
||
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
||
"\n",
|
||
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
||
" new_df[\"Tech Frequency\"] = pd.cut(new_df.INTREQ, bins=bins, labels=labels)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"bins = [0, 1, 2]\n",
|
||
"labels = [\"Almost Constantly\", \"Several Times Per Day\"]\n",
|
||
"new_df[\"Tech Frequency\"] = pd.cut(new_df.INTREQ, bins=bins, labels=labels)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "fa39e859-5e46-4592-a0ac-d4ffd00a5038",
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Axes: xlabel='Tech Frequency', ylabel='count'>"
|
||
]
|
||
},
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"sns.countplot(data=new_df, x=\"Tech Frequency\", hue=\"AGE\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "collectible-puppy",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Second Research Question: How does frequency of using technology correspond with how well a teen perceives he or she fits in? and How does frequency of using social media correspond with how much a teen thinks social media has an effect on people their age?\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "demographic-future",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Methods"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "incorporate-roller",
|
||
"metadata": {},
|
||
"source": [
|
||
"*Explain how you will approach this research question below. Consider the following:* \n",
|
||
" - *Which aspects of the dataset will you use?* \n",
|
||
" - *How will you reorganize/store the data?* \n",
|
||
" - *What data science tools/functions will you use and why?* \n",
|
||
"\n",
|
||
"✏️ *Write your answer below:*\n",
|
||
"This question will continue to look at the responses sorted by age, but this time will focus on the responses to 2 questions.\n",
|
||
"\n",
|
||
"The first, \"In general, which of the following statements comes closest to describing how you see yourself compared with other people your age where you live?\" where the choices were 1-I tend to fit in pretty easily or 2-I tend to stand out.\n",
|
||
"\n",
|
||
"And the second, \"Overall, what effect would you say social media has had on people your age?\", where the choices were 1-mostly positive, 2- mostly negative, or 3- neither positive or negative.\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "juvenile-creation",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Results "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "pursuant-surrey",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Axes: xlabel='INTREQ', ylabel='count'>"
|
||
]
|
||
},
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"sns.countplot(data=new_df, x=\"INTREQ\", hue=\"FITIN\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "located-night",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Axes: xlabel='INTREQ', ylabel='count'>"
|
||
]
|
||
},
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"sns.countplot(data=new_df, x=\"INTREQ\", hue=\"SOC1\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "infectious-symbol",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Discussion"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "furnished-camping",
|
||
"metadata": {
|
||
"code_folding": []
|
||
},
|
||
"source": [
|
||
"## Considerations"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "bearing-stadium",
|
||
"metadata": {},
|
||
"source": [
|
||
"*It's important to recognize the limitations of our research.\n",
|
||
"Consider the following:*\n",
|
||
"\n",
|
||
"- *Do the results give an accurate depiction of your research question? Why or why not?*\n",
|
||
"- *What were limitations of your datset?*\n",
|
||
"- *Are there any known biases in the data?*\n",
|
||
"\n",
|
||
"✏️ *Write your answer below:*"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "beneficial-invasion",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Summary"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "about-raise",
|
||
"metadata": {},
|
||
"source": [
|
||
"*Summarize what you discovered through the research. Consider the following:*\n",
|
||
"\n",
|
||
"- *What did you learn about your media consumption/digital habits?*\n",
|
||
"- *Did the results make sense?*\n",
|
||
"- *What was most surprising?*\n",
|
||
"- *How will this project impact you going forward?*\n",
|
||
"\n",
|
||
"✏️ *Write your answer below:*"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"jupytext": {
|
||
"cell_metadata_json": true,
|
||
"text_representation": {
|
||
"extension": ".Rmd",
|
||
"format_name": "rmarkdown",
|
||
"format_version": "1.2",
|
||
"jupytext_version": "1.9.1"
|
||
}
|
||
},
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.12.5"
|
||
},
|
||
"toc": {
|
||
"base_numbering": 1,
|
||
"nav_menu": {},
|
||
"number_sections": false,
|
||
"sideBar": true,
|
||
"skip_h1_title": false,
|
||
"title_cell": "Table of Contents",
|
||
"title_sidebar": "Contents",
|
||
"toc_cell": false,
|
||
"toc_position": {},
|
||
"toc_section_display": true,
|
||
"toc_window_display": false
|
||
},
|
||
"varInspector": {
|
||
"cols": {
|
||
"lenName": 16,
|
||
"lenType": 16,
|
||
"lenVar": 40
|
||
},
|
||
"kernels_config": {
|
||
"python": {
|
||
"delete_cmd_postfix": "",
|
||
"delete_cmd_prefix": "del ",
|
||
"library": "var_list.py",
|
||
"varRefreshCmd": "print(var_dic_list())"
|
||
},
|
||
"r": {
|
||
"delete_cmd_postfix": ") ",
|
||
"delete_cmd_prefix": "rm(",
|
||
"library": "var_list.r",
|
||
"varRefreshCmd": "cat(var_dic_list()) "
|
||
}
|
||
},
|
||
"types_to_exclude": [
|
||
"module",
|
||
"function",
|
||
"builtin_function_or_method",
|
||
"instance",
|
||
"_Feature"
|
||
],
|
||
"window_display": false
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|