Meta_Analysis_IRLG_data_analysis.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "So we're going to load our data and make our mixed model."
      ],
      "metadata": {
        "id": "oqzFKw9QV_pH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import statsmodels.api as sm\n",
        "import statsmodels.formula.api as smf\n",
        "\n",
        "# Load the CSV data into a DataFrame\n",
        "df = pd.read_csv('/content/Clean_data.csv')  # Replace with the path to your CSV file\n",
        "\n",
        "# Define the formula for the mixed-effects model\n",
        "formula = (\"Violence ~ 1 + Dehumanization + Dislike + ZNeg_Emotions + ZImmoral + ZStupid + \"\n",
        "           \"ZTrait_Aggression + ZSDO + ZRWA\")\n",
        "\n",
        "# Fit the mixed-effects model\n",
        "mixed_model = smf.mixedlm(formula, df, groups=df[\"Participant\"], re_formula=\"~1+Dehumanization\",\n",
        "                          vc_formula={\"Target\": \"0 + C(Target)\"})\n",
        "mixed_fit = mixed_model.fit()\n",
        "\n",
        "# Display the summary of the model\n",
        "print(mixed_fit.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UUxywI7cV98z",
        "outputId": "e2c4f98f-6322-4d8f-ecb2-35554d4b6d37"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                Mixed Linear Model Regression Results\n",
            "=====================================================================\n",
            "Model:                 MixedLM     Dependent Variable:     Violence  \n",
            "No. Observations:      1194        Method:                 REML      \n",
            "No. Groups:            398         Scale:                  0.5533    \n",
            "Min. group size:       3           Log-Likelihood:         -2072.7507\n",
            "Max. group size:       3           Converged:              Yes       \n",
            "Mean group size:       3.0                                           \n",
            "---------------------------------------------------------------------\n",
            "                           Coef.  Std.Err.   z    P>|z| [0.025 0.975]\n",
            "---------------------------------------------------------------------\n",
            "Intercept                   3.774    0.053 70.942 0.000  3.669  3.878\n",
            "Dehumanization              0.561    0.065  8.674 0.000  0.434  0.687\n",
            "Dislike                    -0.048    0.056 -0.848 0.396 -0.158  0.063\n",
            "ZNeg_Emotions               0.546    0.072  7.609 0.000  0.405  0.686\n",
            "ZImmoral                    0.463    0.069  6.732 0.000  0.328  0.597\n",
            "ZStupid                     0.274    0.054  5.038 0.000  0.167  0.380\n",
            "ZTrait_Aggression           0.121    0.056  2.151 0.031  0.011  0.232\n",
            "ZSDO                        0.221    0.062  3.561 0.000  0.099  0.342\n",
            "ZRWA                        0.218    0.063  3.476 0.001  0.095  0.341\n",
            "Group Var                   0.526                                    \n",
            "Group x Dehumanization Cov -0.055                                    \n",
            "Dehumanization Var          0.279                                    \n",
            "Target Var                  0.736                                    \n",
            "=====================================================================\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Considering that python's statsmodel library typically does the z-test, we'll need to calculate the t-values, degrees of freedom, and p-values manually (almost)."
      ],
      "metadata": {
        "id": "gwAmycgaV-_K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import t\n",
        "\n",
        "#Extract coefficients and standard errors\n",
        "coefficients = mixed_fit.params\n",
        "standard_errors = mixed_fit.bse\n",
        "\n",
        "# Calculate t-values\n",
        "t_values = coefficients / standard_errors\n",
        "\n",
        "# Calculate degrees of freedom\n",
        "n_obs = len(df)\n",
        "n_fixed_effects = len(coefficients)\n",
        "degrees_of_freedom = n_obs - n_fixed_effects - 1\n",
        "\n",
        "# Calculate two-sided p-values\n",
        "p_values = 2 * (1 - t.cdf(abs(t_values), df=degrees_of_freedom))\n",
        "\n",
        "# Display t-values, degrees of freedom, and p-values\n",
        "print(\"T-values:\")\n",
        "print(t_values)\n",
        "\n",
        "print(f\"\\nDegrees of freedom: {degrees_of_freedom}\")\n",
        "\n",
        "print(\"\\nP-values:\")\n",
        "print(p_values)\n",
        "\n",
        "# Indicate significance levels\n",
        "print(\"\\nSignificance levels:\")\n",
        "for var, p_value in zip(coefficients.index, p_values):\n",
        "    if p_value < 0.01:\n",
        "        print(f\"{var}: *** (p < 0.01)\")\n",
        "    elif p_value < 0.05:\n",
        "        print(f\"{var}: ** (p < 0.05)\")\n",
        "    elif p_value < 0.1:\n",
        "        print(f\"{var}: * (p < 0.1)\")\n",
        "    else:\n",
        "        print(f\"{var}: (p >= 0.1)\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vQbJJp2UZ2to",
        "outputId": "e92b4576-84d0-4e8f-f24d-4d2bf16ff646"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "T-values:\n",
            "Intercept                     70.941847\n",
            "Dehumanization                 8.674250\n",
            "Dislike                       -0.848409\n",
            "ZNeg_Emotions                  7.609275\n",
            "ZImmoral                       6.731953\n",
            "ZStupid                        5.038461\n",
            "ZTrait_Aggression              2.151125\n",
            "ZSDO                           3.560729\n",
            "ZRWA                           3.475568\n",
            "Group Var                           NaN\n",
            "Group x Dehumanization Cov          NaN\n",
            "Dehumanization Var                  NaN\n",
            "Target Var                          NaN\n",
            "dtype: float64\n",
            "\n",
            "Degrees of freedom: 1180\n",
            "\n",
            "P-values:\n",
            "[0.00000000e+00 0.00000000e+00 3.96382232e-01 5.61772850e-14\n",
            " 2.60698130e-11 5.42567488e-07 3.16688380e-02 3.84547551e-04\n",
            " 5.28286554e-04            nan            nan            nan\n",
            "            nan]\n",
            "\n",
            "Significance levels:\n",
            "Intercept: *** (p < 0.01)\n",
            "Dehumanization: *** (p < 0.01)\n",
            "Dislike: (p >= 0.1)\n",
            "ZNeg_Emotions: *** (p < 0.01)\n",
            "ZImmoral: *** (p < 0.01)\n",
            "ZStupid: *** (p < 0.01)\n",
            "ZTrait_Aggression: ** (p < 0.05)\n",
            "ZSDO: *** (p < 0.01)\n",
            "ZRWA: *** (p < 0.01)\n",
            "Group Var: (p >= 0.1)\n",
            "Group x Dehumanization Cov: (p >= 0.1)\n",
            "Dehumanization Var: (p >= 0.1)\n",
            "Target Var: (p >= 0.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "NOT NATIVE DATA: Copied data from the RStudio mixed model"
      ],
      "metadata": {
        "id": "s_I0wUX1Z5K4"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "id": "hV3IlK5qODcR",
        "outputId": "0b87e6d8-1eb9-4644-e4b6-415cc40cf860"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Define the data for confidence intervals\n",
        "factors = ['sig01', 'sig02', 'sig03', 'sig04', 'sig05', 'sig06', '.sigma', '(Intercept)',\n",
        "           'Dehumanization', 'Dislike', 'ZNeg_Emotions', 'ZImmoral', 'ZStupid',\n",
        "           'ZTrait_Aggression', 'ZSDO', 'ZRWA']\n",
        "\n",
        "lower_bounds = [0.62027176, -0.26495540, 0.42170664, 0.21033032, -0.12799612, 0.09947769, 0.98841524,\n",
        "                3.06433182, 0.26966932, 0.07123586, 0.27725100, 0.05713947, 0.11578866, 0.01678985, 0.08667567, 0.10825976]\n",
        "\n",
        "upper_bounds = [0.8460347, 0.1857686, 0.7211978, 1.2568725, 1.0000000, 0.6544259, 1.0990510,\n",
        "                4.4406912, 1.0056337, 0.3208241, 0.5779373, 0.3541235, 0.3201968, 0.2359913, 0.3275592, 0.3522245]\n",
        "\n",
        "# Create the plot\n",
        "plt.figure(figsize=(10, 8))\n",
        "y_pos = np.arange(len(factors))\n",
        "\n",
        "# Plot confidence intervals with color coding for significance\n",
        "for y, lb, ub in zip(y_pos, lower_bounds, upper_bounds):\n",
        "    color = 'red' if lb > 0 or ub < 0 else 'blue'\n",
        "    plt.errorbar(x=(lb + ub) / 2, y=y, xerr=(ub - lb) / 2, fmt='o', color=color)\n",
        "\n",
        "# Labeling and titling\n",
        "plt.yticks(y_pos, factors)\n",
        "plt.xlabel('Confidence Interval')\n",
        "plt.title('Confidence Intervals of Factors')\n",
        "\n",
        "# Legend\n",
        "plt.legend(['Significant', 'Not Significant'], title='Statistical Significance')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I don't know where the sig variables came from. I actually believe that they were somewhat derived from the model working with the random effects. I need to get some more work done on that."
      ],
      "metadata": {
        "id": "Uj4pIFWOuvqB"
      }
    }
  ]
}