http://sydneyscott.nfshost.com/pubs/gmoknowledge.pdf

jc28

# -*- coding: utf-8 -*-

# http://sydneyscott.nfshost.com/pubs/gmoknowledge.pdf
# https://static-content.springer.com/esm/art%3A10.1038%2Fs41562-018-0520-3/MediaObjects/41562_2018_520_MOESM1_ESM.pdf

STUDY1_DATA = [
    {
        "name": someone,
        "age": N,
        "gender": "male" or "female",
        "objective_scores": [N, ..., N],
        "self_assessed_knowledge": N(1~7),
        "extremity_of_opposition": N(1~7),
        "extremity_of_concern": N(1~7),
    },
    ...
]

EXTREMETY_RANGE = [1,2,3,4,5,6,7]

def do_study1(data):
    all_extremity_of_oppositions = [d.extremity_of_opposition for d in data]
    all_averaged_objective_scores = [average(d.objective_scores) for d in data]
    all_self_assessed_knowledge_scores = [d.self_assessed_knowledge for d in data]

    def generate_figure1():
        '''
        1. Correlation between average self-assessed knowledge of GM foods and extremity of opposition
        2. Correlation between average scientific literacy and extremity of opposition
        3. Correlation between the gap of 1&2 and extremity of opposition
        '''
        plot(x=all_extremity_of_oppositions, y=all_averaged_objective_scores)
        reg(x=all_extremity_of_oppositions, y=all_averaged_objective_scores) # > 0 posi

        plot(x=all_extremity_of_oppositions, y=all_self_assessed_knowledge_scores)
        reg(x=all_extremity_of_oppositions, y=all_self_assessed_knowledge_scores) # < 0 nega

        all_averaged_objective_zscores = zscoring(all_averaged_objective_scores)
        all_self_assessed_knowledge_zscores = zscoring(all_self_assessed_knowledge_scores)
        difference_scores = [all_averaged_objective_zscores[i] - all_self_assessed_knowledge_zscores[i] for i in range(0, len(all_averaged_objective_zscores))]
        reg(x=all_extremity_of_oppositions, y=difference_scores) # > 0 posi

    def generate_figure2():
        '''
        Tested: whether objective knowledge predicts self-assessed knowledge differentially at different levels of extremity of opposition
        Method: regressing self-assessed knowledge on objective knowledge, extremity of opposition and their interaction
        ※ Interaction effects = It occurs when the effect of one variable depends on another variable. 交互作用。2つの因子が組み合わさることで初めて現れる相乗効果のこと。
        '''
        canvas = get_canvas()
        # got [2, 4.5, 7] in this study
        extremity_kinds = get_tenth_fiftieth_ninetieth_percentiles(EXTREMETY_RANGE)
        for extremity_kind in extremity_kinds:
            averaged_objective_scores_by_extremity = filter(all_extremity_of_oppositions, lambda d: d.extremity ≒ extremity_kind)
            self_assessed_knowledge_scores_by_extremity = filter(all_self_assessed_knowledge_scores, lambda d: d.extremity ≒ extremity_kind)
            model = fit_with_regression(averaged_objective_scores_by_extremity, self_assessed_knowledge_scores_by_extremity)
            objecctive_knowledge_scale = scale(averaged_objective_scores_by_extremity)
            predicted_averaged_objective_scores_by_extremity = [model.predict(objective_knowledge_score) for i in objecctive_knowledge_scale]
            canvas.plot(x=objecctive_knowledge_scale, y=predicted_averaged_objective_scores_by_extremity, same_canvas=True)

    generate_figure1()
    generate_figure2()


do_study1(STUDY1_DATA)