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‎FactorAnalysisPersonality.ipynb‎

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"colab": {
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"name": "FactorAnalysisPersonality.ipynb",
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"provenance": [],
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"toc_visible": true,
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"include_colab_link": true
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},
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"kernelspec": {
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]
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},
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{
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"cell_type": "code",
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"cell_type": "markdown",
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"metadata": {
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"id": "_zn3Cg6ECVcW",
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"colab_type": "code",
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"colab": {}
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"id": "qTHBjB_hPxZ1",
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"colab_type": "text"
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},
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"source": [
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""
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],
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"execution_count": 0,
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"outputs": []
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"# Background on the data"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "814cvzSKQBjL",
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"colab_type": "text"
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},
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"source": [
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"A personality test is given to a large group of people. There are 5 questions which deal with agreeableness (A1-5), 5 questions which deal with conscientiousness (C1-5), 5 questions which deal with Extraversion(E1-5), 5 questions which deal with Neuroticism (N1-5) and 5 questions which deal with Openness (O1-5). \n",
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"\n",
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"Those answers are directly related with one another.\n",
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"\n",
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"We want to see if based on people's answers, we can regroup those categories.\n",
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"If we can, we will end up with 5 factors and each factor will be influenced by the questions which correspond to it. \n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "tP4cR7ZrSuLc",
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"colab_type": "code",
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"outputId": "d52f563c-c0d0-43ca-a322-8f1885cc7dc8",
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"outputId": "611e1c92-4bc6-4193-d21b-5b717b8bf307",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 207
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"gauth.credentials = GoogleCredentials.get_application_default()\n",
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"drive = GoogleDrive(gauth)"
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],
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"execution_count": 0,
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"execution_count": 2,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Collecting factor_analyzer==0.2.3\n",
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" Downloading https://files.pythonhosted.org/packages/79/1b/84808bbeee0f3a8753c3d8034baf0aa0013cf08957eff750f366ce83f04a/factor_analyzer-0.2.3-py2.py3-none-any.whl\n",
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"Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from factor_analyzer==0.2.3) (1.16.5)\n",
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"Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from factor_analyzer==0.2.3) (0.24.2)\n",
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"Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from factor_analyzer==0.2.3) (1.3.1)\n",
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"Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from factor_analyzer==0.2.3) (1.16.5)\n",
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"Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas->factor_analyzer==0.2.3) (2018.9)\n",
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"Requirement already satisfied: python-dateutil>=2.5.0 in /usr/local/lib/python3.6/dist-packages (from pandas->factor_analyzer==0.2.3) (2.5.3)\n",
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"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.5.0->pandas->factor_analyzer==0.2.3) (1.12.0)\n",
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"colab_type": "text"
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},
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"source": [
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"Now we will load the data. This line reads in the comma separated value sheet that I made in excel.\n",
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"Now we will load the data. This line reads in the comma separated value sheet.\n",
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"\n",
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"If you are given sensor data in excel and want to export it to csv in the future, see this link:\n",
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"https://www.ablebits.com/office-addins-blog/2014/04/24/convert-excel-csv/"
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"metadata": {
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"id": "cbE21AiDU_5L",
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"colab_type": "code",
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"outputId": "0f51b05e-0763-4115-8322-0d1ff60199f6",
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"outputId": "2a0c7e8c-e5f4-4a9e-f4ba-12ef316d9f9e",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 544
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"#calcualte the number of variables\n",
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"numVars = df.shape[1]-len(unnecessaryColumns)\n"
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],
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"execution_count": 0,
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"execution_count": 5,
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"outputs": [
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{
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"output_type": "stream",
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"metadata": {
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"id": "aXtTewA1WHwR",
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"colab_type": "code",
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"outputId": "e579ef8c-4963-45a7-e4a2-2041627acdf1",
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"outputId": "3b7b6a91-717d-4ad4-e23b-2ee1316d3bb4",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 34
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"chi_square_value,p_value=calculate_bartlett_sphericity(df)\n",
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"chi_square_value, p_value"
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],
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"execution_count": 0,
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"execution_count": 6,
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"outputs": [
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{
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"output_type": "execute_result",
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"metadata": {
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"tags": []
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},
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"execution_count": 4
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"execution_count": 6
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}
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]
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},
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"metadata": {
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"id": "gJbu2WwCXBa0",
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"colab_type": "code",
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"outputId": "d27d5cbe-3eb2-4dec-f19d-10d268bd818c",
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"outputId": "17522ec2-a2c3-4a8c-cb99-97e23c603420",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 34
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"\n",
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"kmo_model"
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],
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"execution_count": 0,
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"execution_count": 7,
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"outputs": [
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{
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"output_type": "execute_result",
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"metadata": {
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"tags": []
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},
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"execution_count": 5
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"execution_count": 7
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}
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]
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},
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"metadata": {
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"id": "Z6zPLCYTXNgL",
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"colab_type": "code",
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"outputId": "49e91e74-94aa-4158-8d27-7437f971fac9",
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"outputId": "37c538c0-50ca-4b5d-b43d-177486fd110f",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 855
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"ev, v = fa.get_eigenvalues()\n",
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"ev"
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],
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"execution_count": 0,
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"execution_count": 8,
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"outputs": [
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{
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"output_type": "execute_result",
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"metadata": {
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"tags": []
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},
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"execution_count": 6
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"execution_count": 8
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}
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]
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},
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"metadata": {
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"id": "8EcInPmvXS4E",
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"colab_type": "code",
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"outputId": "6050dd3b-4043-4c62-9c88-bcb005c4d5e7",
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"outputId": "6589d02c-5353-457b-a17b-c79dd90408d2",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 295
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"plt.grid()\n",
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"plt.show()"
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],
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"execution_count": 0,
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"execution_count": 9,
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"outputs": [
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{
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"output_type": "display_data",
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"metadata": {
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"id": "es4vNDC1XYdW",
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"colab_type": "code",
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"outputId": "399e5d49-3f56-4be5-e704-5c7c6f660c6e",
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"outputId": "c5735c87-1ad2-4027-ead4-1dfeed33b5a4",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 855
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"fa.analyze(df, numFactors, rotation=\"varimax\")\n",
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"fa.loadings"
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],
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"execution_count": 0,
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"execution_count": 10,
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"outputs": [
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{
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"output_type": "execute_result",
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"metadata": {
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"tags": []
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},
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"execution_count": 8
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"execution_count": 10
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}
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]
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},
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"metadata": {
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"id": "jXtE5F5VaLvW",
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"colab_type": "code",
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"outputId": "ee31f049-282c-443d-9edc-4389d8ac7cf7",
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"outputId": "5ebab41d-9386-42c6-de90-0ed765640d62",
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"cellView": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 102
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"height": 122
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}
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},
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"source": [
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" contributions = [(np.round(factor[x],2),headings[x]) for x in descending if np.abs(factor[x])>factor_threshold]\n",
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" print('Factor %d:'%(i+1),contributions)"
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],
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"execution_count": 0,
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"execution_count": 13,
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"outputs": [
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{
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"output_type": "stream",
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"colab_type": "text"
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},
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"source": [
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"Factor 1 is mostly influenced by the questions about extrovertedness (E); some parts of agreeableness, openness, and nueroticism, influence this, but it is mostly E.\n",
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"Factor 1 is mostly influenced by the questions about extrovertedness (E); some parts of agreeableness, openness, and nueroticism, influence this, but it is mostly E. These were the original E questions.\n",
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"\n",
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"Factor 2 is mostly influenced by neuroticism (N), but smoe parts of extrovertedness and conscientiousness play a role.\n",
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"Factor 2 is mostly influenced by neuroticism (N), but smoe parts of extrovertedness and conscientiousness play a role. These were the original N questions.\n",
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"\n",
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"Factor 3 is mostly influenced by conscientiousness, but extrovertedness can play a role.\n",
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"These were the original C questions.\n",
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"\n",
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"Factor 4 is mostly influenced by openness but extrovertedness plays a role.\n",
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"These were the original O questions.\n",
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"\n",
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"Factor 5 is mostly influenced by agreeableness but extrovertedness plays a role.\n",
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"These were the original A questions.\n",
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"\n",
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"In summary, extrovertedness played a role in all of the factors so it is dominanting the system. However looking at individual factors, we can see that specific traits influence specific factors. "
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"In summary, we were able to extract the original groupings however it was interesting to note the role that E plays in all of the questions. This could suggest that there is some bias or systematic error at play. "
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]
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},
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{
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"# Computing the variance"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "bxzTgYfzRvsp",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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""
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {

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