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},
"metadata": {}
},
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"output_type": "stream",
"name": "stdout",
"text": [
"\n\nJoM for categories of training:\n\n"
]
},
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"output_type": "display_data",
"data": {
"text/plain": " JoM\nStudyID Training \n1 Graphic designer 56.735294\n Letter designer 55.160000\n Non-designer 53.977273\n Other designer 49.166667\n Typographer 61.000000\n2 Graphic designer 53.875000\n Letter designer 59.562500\n Non-designer 52.237288\n Other designer 53.083333\n Typographer 59.000000",
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JoM
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StudyID
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Training
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56.735294
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Letter designer
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55.160000
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Non-designer
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53.977273
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Other designer
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49.166667
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Typographer
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61.000000
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Graphic designer
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Letter designer
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"text": [
"\n\nJoL for categories of training:\n\n"
]
},
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"output_type": "display_data",
"data": {
"text/plain": " JoL\nStudyID Training JoL \n1 Graphic designer very easy to read 13.0\n ok 10.0\n easy to read 5.0\n difficult to read 4.0\n very difficult to read 2.0\n Letter designer very easy to read 14.0\n difficult to read 13.0\n ok 11.0\n easy to read 9.0\n very difficult to read 3.0\n Non-designer very easy to read 38.0\n ok 22.0\n difficult to read 14.0\n easy to read 12.0\n very difficult to read 2.0\n Other designer very easy to read 5.0\n ok 4.0\n difficult to read 2.0\n very difficult to read 1.0\n Typographer very easy to read 4.0\n difficult to read 3.0\n ok 2.0\n easy to read 1.0\n2 Graphic designer very easy to read 23.0\n difficult to read 12.0\n ok 11.0\n easy to read 10.0\n Letter designer difficult to read 13.0\n easy to read 7.0\n very easy to read 7.0\n ok 4.0\n very difficult to read 1.0\n Non-designer very easy to read 51.0\n ok 26.0\n difficult to read 18.0\n easy to read 17.0\n very difficult to read 6.0\n Other designer very easy to read 8.0\n difficult to read 6.0\n easy to read 5.0\n ok 4.0\n very difficult to read 1.0\n Typographer ok 6.0\n very easy to read 5.0\n easy to read 3.0",
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4.0
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Letter designer
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14.0
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difficult to read
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13.0
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11.0
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9.0
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very easy to read
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38.0
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22.0
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14.0
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easy to read
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12.0
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very difficult to read
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2.0
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Other designer
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very easy to read
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5.0
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4.0
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Typographer
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3.0
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ok
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2.0
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easy to read
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1.0
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\n
\n
2
\n
Graphic designer
\n
very easy to read
\n
23.0
\n
\n
\n
difficult to read
\n
12.0
\n
\n
\n
ok
\n
11.0
\n
\n
\n
easy to read
\n
10.0
\n
\n
\n
Letter designer
\n
difficult to read
\n
13.0
\n
\n
\n
easy to read
\n
7.0
\n
\n
\n
very easy to read
\n
7.0
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ok
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4.0
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very difficult to read
\n
1.0
\n
\n
\n
Non-designer
\n
very easy to read
\n
51.0
\n
\n
\n
ok
\n
26.0
\n
\n
\n
difficult to read
\n
18.0
\n
\n
\n
easy to read
\n
17.0
\n
\n
\n
very difficult to read
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6.0
\n
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\n
Other designer
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very easy to read
\n
8.0
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\n
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difficult to read
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6.0
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easy to read
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5.0
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ok
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4.0
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very difficult to read
\n
1.0
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Typographer
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ok
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6.0
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very easy to read
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5.0
\n
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easy to read
\n
3.0
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},
"metadata": {}
}
],
"source": [
"TPP = 72 # there are 72 trials in a session/participant\n",
"TPT = 36 # there are 36 trials in a part/test\n",
"TPL = 20 # there are 20 trials in a lexical test\n",
"\n",
"\n",
"print(\"Number of participants:\")\n",
"display(pd.DataFrame(d[\"StudyID\"].value_counts() / TPP))\n",
"\n",
"print()\n",
"print(\"Fluent vs. non-fluent:\")\n",
"display(pd.DataFrame(d.groupby(\"StudyID\")[\"Fluent\"].value_counts() / TPP))\n",
"\n",
"print()\n",
"print()\n",
"print(\"Different kinds of designers:\")\n",
"\n",
"display(pd.DataFrame(d.groupby(\"StudyID\")[\"Training\"].value_counts() / TPP))\n",
"\n",
"print()\n",
"print()\n",
"print(\"Different kinds of designers and which font was first:\")\n",
"for sid in [1, 2]:\n",
" print(\"Study #%s\" % sid)\n",
" dtt = pd.DataFrame(columns=[\"Designer\", \"Non-designer\", \"total\"], index=[\"sansforgetica\", \"arial\", \"total\"])\n",
" dtt[\"Designer\"] = dt[(dt[\"StudyID\"] == sid) & (dt[\"TestID\"] == 1) & (dt[\"Type\"] == \"lexical\") & (dt[\"Training\"] != \"Non-designer\")][\"Font\"].value_counts()\n",
" dtt[\"Non-designer\"] = dt[(dt[\"StudyID\"] == sid) & (dt[\"TestID\"] == 1) & (dt[\"Type\"] == \"lexical\") & (dt[\"Training\"] == \"Non-designer\")][\"Font\"].value_counts()\n",
" dtt /= TPL\n",
" dtt[\"total\"] = dtt.T.sum()\n",
" dtt.loc[\"total\"] = dtt.sum()\n",
" display(dtt)\n",
"\n",
"print()\n",
"print()\n",
"print(\"JoM for categories of training:\")\n",
"print()\n",
"display(pd.DataFrame(d.groupby([\"StudyID\", \"Training\"])[\"JoM\"].mean()))\n",
"print()\n",
"print()\n",
"print(\"JoL for categories of training:\")\n",
"print()\n",
"display(pd.DataFrame(d.groupby([\"StudyID\", \"Training\"])[\"JoL\"].value_counts() / TPT))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Assess normality of RTs\n",
"\n",
"The distributions of RTs are not normal, but close enough."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Normality test for RTnorms in lexical task\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": " W pval normal\nRTnorm 0.95118 1.909781e-16 False",
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1.909781e-16
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False
\n
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"metadata": {}
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"output_type": "stream",
"name": "stdout",
"text": [
"Normality test for RTnorms in lexical task (outliers replaced)\n"
]
},
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"data": {
"text/plain": " W pval normal\nRTnorm 0.949008 7.829168e-17 False",
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"output_type": "stream",
"name": "stdout",
"text": [
"Normality test for RTnorms in recognition task\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": " W pval normal\nRTnorm 0.95118 1.909781e-16 False",
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"text": [
"Normality test for RTnorms in recognition task (outliers replaced)\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": " W pval normal\nRTnorm 0.949008 7.829168e-17 False",
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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"# assess normality of RTs\n",
"\n",
"fig, axes = plt.subplots(2, 4, figsize=(20, 10))\n",
"plt.subplots_adjust(wspace=0, hspace=0.2)\n",
"for i, ttype in enumerate([\"lexical\", \"recognition\"]):\n",
" rts = agg[agg[\"Type\"] == ttype][\"RT\"]\n",
" rts.plot.hist(bins=50, ax=axes[i][0], sharey=True, title=\"%s\" % ttype)\n",
" rts = agg[agg[\"Type\"] == ttype][\"RTnorm\"]\n",
" rts.plot.hist(bins=50, ax=axes[i][1], sharey=True, title=\"%s (normalized)\" % ttype)\n",
" \n",
" rts = aggwo[aggwo[\"Type\"] == ttype][\"RT\"]\n",
" rts.plot.hist(bins=50, ax=axes[i][2], sharey=True, title=\"%s (outliers replaced)\" % ttype)\n",
" rts = aggwo[aggwo[\"Type\"] == ttype][\"RTnorm\"]\n",
" rts.plot.hist(bins=50, ax=axes[i][3], sharey=True, title=\"%s (outliers replaced, normalized)\" % ttype)\n",
" \n",
"# test for normality\n",
"# null hypothesis: RTs come from a normal distribution\n",
"for ttype in [\"lexical\", \"recognition\"]:\n",
" for col in [\"RTnorm\"]:\n",
" print(\"Normality test for %ss in %s task\" % (col, ttype))\n",
" display(pg.normality(agg[col]))\n",
" print(\"Normality test for %ss in %s task (outliers replaced)\" % (col, ttype))\n",
" display(pg.normality(aggwo[col]))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Q-Q plot for lexical task\nQ-Q plot for recognition task\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": "",
"image/svg+xml": "\n\n\n