diff --git a/CHANGELOG.md b/CHANGELOG.md
index 02d125638..4fa1bfd27 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -20,6 +20,7 @@
 
 ## [3.6.0](https://github.com/BLKSerene/Wordless/releases/tag/3.6.0) - ??/??/2024
 ### 🎉 New Features
+- Measures: Add effect size - squared association ratio
 - Utils: Add Stanza's Sindhi dependency parser
 
 ### 📌 Bugfixes
diff --git a/doc/doc.md b/doc/doc.md
index 3c8666b83..d98138534 100644
--- a/doc/doc.md
+++ b/doc/doc.md
@@ -946,11 +946,13 @@ The following variables would be used in formulas:<br>
 **NumCharsAlpha**: Number of alphabetic characters (letters, CJK characters, etc.)
 
 <!--
-Al-Heeti's Readability Prediction Formula:
-    \begin{align*}
-        \text{RD}_\text{Policy One} &= 4.41434307 \times \frac{\text{NumCharsAlpha}}{\text{NumWords}} - 13.46873475 \\
-        \text{RD}_\text{Policy Two} &= 0.97569509 \times \frac{\text{NumCharsAlpha}}{\text{NumWords}} + 0.37237998 \times \frac{\text{NumWords}}{\text{NumSentences}} - 0.90451827 \times \frac{\text{NumWords}}{\text{NumWordTypes}} - 1.06000414
-    \end{align*}
+* Only people's names are capitalized and case of readability formulas is preserved as in original papers
+
+Al-Heeti's readability formula:
+\begin{align*}
+    \text{RD}_\text{Policy One} &= 4.41434307 \times \frac{\text{NumCharsAlpha}}{\text{NumWords}} - 13.46873475 \\
+    \text{RD}_\text{Policy Two} &= 0.97569509 \times \frac{\text{NumCharsAlpha}}{\text{NumWords}} + 0.37237998 \times \frac{\text{NumWords}}{\text{NumSentences}} - 0.90451827 \times \frac{\text{NumWords}}{\text{NumWordTypes}} - 1.06000414
+\end{align*}
 
 Automated Arabic Readability Index:
     {\text{AARI} = 3.28 \times \text{NumCharsAlnum} + 1.43 \times \frac{\text{NumCharsAlnum}}{\text{NumWords}} + 1.24 \times \frac{\text{NumWords}}{\text{NumSentences}}}
@@ -961,7 +963,7 @@ Automated Readability Index:
         \text{ARI}_{\text{Navy}} &= 0.37 \times \frac{\text{NumWords}}{\text{NumSentences}} + 5.84 \times \frac{\text{NumCharsAll}}{\text{NumWords}} - 26.01
     \end{align*} 
 
-Bormuth's Cloze Mean & Grade Placement:
+Bormuth's cloze mean & grade placement:
     \begin{align*}
         \text{M} = \; &0.886593 - 0.083640 \times \frac{\text{NumCharsAlpha}}{\text{NumWords}} + 0.161911 \times \left(\frac{\text{NumWordsDale}_{3000}}{\text{NumWords}}\right)^3 \\
         &- 0.021401 \times \frac{\text{NumWords}}{\text{NumSentences}} + 0.000577 \times \left(\frac{\text{NumWords}}{\text{NumSentences}}\right)^2 - 0.000005 \times \left(\frac{\text{NumWords}}{\text{NumSentences}}\right)^3 \\
@@ -969,13 +971,13 @@ Bormuth's Cloze Mean & Grade Placement:
         &- 44.285 \times \left(\text{M} \times \text{C}\right) + 97.620 \times \left(\text{M} \times \text{C}\right)^2 - 59.538 \times \left(\text{M} \times \text{C}\right)^3
     \end{align*}
 
-Coleman-Liau Index:
+Coleman-Liau index:
     \begin{align*}
-        \text{Estimated Cloze \ %} &= 141.8401 - 0.21459 \times \left(\frac{\text{NumCharsAlpha}}{\text{NumWords}} \times 100\right) + 1.079812 \times \left(\frac{\text{NumSentences}}{\text{NumWords}} \times 100\right) \\
-        \text{Grade Level} &= -27.4004 \times \frac{\text{Estimated Cloze \; %}}{100} + 23.06395
+        \text{Estimated cloze \ %} &= 141.8401 - 0.21459 \times \left(\frac{\text{NumCharsAlpha}}{\text{NumWords}} \times 100\right) + 1.079812 \times \left(\frac{\text{NumSentences}}{\text{NumWords}} \times 100\right) \\
+        \text{Grade level} &= -27.4004 \times \frac{\text{Estimated cloze \; %}}{100} + 23.06395
     \end{align*}
 
-Coleman's Readability Formula:
+Coleman's readability formula:
     \begin{align*}
         \text{Cloze \; %}_1 &= 1.29 \times \left(\frac{\text{NumWordsSyl}_1}{\text{NumWords}} \times 100\right) - 38.45 \\
         \text{Cloze \; %}_2 &= 1.16 \times \left(\frac{\text{NumWordsSyl}_1}{\text{NumWords}} \times 100\right) + 1.48 \times \left(\frac{\text{NumSentences}}{\text{NumWords}} \times 100\right) - 37.95 \\
@@ -983,27 +985,30 @@ Coleman's Readability Formula:
         \text{Cloze \; %}_4 &= 1.04 \times \left(\frac{\text{NumWordsSyl}_1}{\text{NumWords}} \times 100\right) + 1.06 \times \left(\frac{\text{NumSentences}}{\text{NumWords}} \times 100\right) + 0.56 \times \left(\frac{\text{NumProns}}{\text{NumWords}} \times 100\right) - 0.36 \times \left(\frac{\text{NumPreps}}{\text{NumWords}} \times 100\right) - 26.01
     \end{align*}
 
-Dale-Chall Readability Formula:
+Crawford's readability formula:
+    {\text{Grade level} = \frac{\text{NumSentences}}{\text{NumWords}} \times 100 \times (-0.205) + \frac{\text{NumSyls}}{\text{NumWords}} \times 100 \times 0.049 - 3.407}
+
+Dale-Chall readability formula:
     \begin{align*}
         \text{X}_{\text{c50}} &= 0.1579 \times \left(\frac{\text{NumWordsDale}_{3000}}{\text{NumWords}} \times 100\right) + 0.0496 \times \frac{\text{NumWords}}{\text{NumSentences}} + 3.6365 \\
         \text{X}_{\text{c50-Powers-Sumner-Kearl}} &= 3.2672 + 0.1155 \times \left(\frac{\text{NumWordsDale}_{3000}}{\text{NumWords}} \times 100\right) + 0.0596 \times \frac{\text{NumWords}}{\text{NumSentences}} \\
-        \text{X}_{\text{c50-New}} &= 64 - 0.95 \times \left(\frac{\text{NumWordsDale}_{3000}}{\text{NumWords}} \times 100\right) - 0.69 \times \frac{\text{NumWords}}{\text{NumSentences}}
+        \text{X}_{\text{c50-new}} &= 64 - 0.95 \times \left(\frac{\text{NumWordsDale}_{3000}}{\text{NumWords}} \times 100\right) - 0.69 \times \frac{\text{NumWords}}{\text{NumSentences}}
     \end{align*}
 
-Danielson-Bryan's Readability Formula:
+Danielson-Bryan's readability formula:
     \begin{align*}
         \text{Danielson-Bryan}_1 &= 1.0364 \times \frac{\text{NumCharsAll}}{\text{NumWords} - 1} + 0.0194 \times \frac{\text{NumCharsAll}}{\text{NumSentences}} - 0.6059 \\
         \text{Danielson-Bryan}_2 &= 131.059 - 10.364 \times \frac{\text{NumCharsAll}}{\text{NumWords} - 1} - 0.194 \times \frac{\text{NumCharsAll}}{\text{NumSentences}}
     \end{align*}
 
-Dawood's Readability Formula:
+Dawood's readability formula:
     {\text{Dawood} = -0.0533 \times \frac{\text{NumCharsAlpha}}{\text{NumWords}} - 0.2066 \times \frac{\text{NumWords}}{\text{NumSentences}} + 5.5543 \times \frac{\text{NumWords}}{\text{NumWordTypes}} - 1.0801}
 
 Degrees of Reading Power:
     \text{DRP} = 100 - (\lfloor \text{M} \times 100 + 0.5 \rfloor)
 
 Devereux Readability Index:
-    \text{Grade Placement} = 1.56 \times \frac{\text{NumCharsAll}}{\text{NumWords}} + 0.19 \times \frac{\text{NumWords}}{\text{NumSentences}} - 6.49
+    \text{Grade placement} = 1.56 \times \frac{\text{NumCharsAll}}{\text{NumWords}} + 0.19 \times \frac{\text{NumWords}}{\text{NumSentences}} - 6.49
 
 Dickes-Steiwer Handformel:
     {\text{Dickes-Steiwer} = 235.95993 - \ln \left(\frac{\text{NumCharsAlpha}}{\text{NumWords}} + 1\right) \times 73.021 - \ln \left(\frac{\text{NumWords}}{\text{NumSentences}} + 1\right) \times 12.56438 - \frac{\text{NumWordTypes}}{\text{NumWords}} \times 50.03293}
@@ -1011,10 +1016,10 @@ Dickes-Steiwer Handformel:
 Easy Listening Formula:
     \text{ELF} = \frac{\text{NumSyls} - \text{NumWords}}{\text{NumSentences}}
 
-Flesch-Kincaid Grade Level:
+Flesch-Kincaid grade level:
     \text{GL} = 0.39 \times \frac{\text{NumWords}}{\text{NumSentences}} + 11.8 \times \frac{\text{NumSyls}}{\text{NumWords}} - 15.59
 
-Flesch Reading Ease:
+Flesch reading ease:
     \begin{align*}
         \text{wl} &= \frac{\text{NumSyls}}{\text{NumWords}} \qquad \text{sl} = \frac{\text{NumWords}}{\text{NumSentences}} \\
         \text{RE} &= 206.835 - 84.6 \times \text{wl} - 1.015 \times \text{sl} \\
@@ -1030,26 +1035,20 @@ Flesch Reading Ease:
         \text{RE}_\text{Ukrainian} &= 206.84 - 28.3 \times \text{wl} - 5.93 \times \text{sl}
     \end{align*}
 
-Flesch Reading Ease (Farr-Jenkins-Paterson):
+Flesch reading ease (Farr-Jenkins-Paterson):
     \begin{align*}
         \text{RE} &= 1.599 \times \left(\frac{\text{NumWordsSyl}_1}{\text{NumWords}} \times 100\right) - 1.015 \times \frac{\text{NumWords}}{\text{NumSentences}} - 31.517 \\
         \text{RE}_\text{Farr-Jenkins-Paterson} &= 8.4335 - 0.0648 \times \left(\frac{\text{NumWordsSyl}_1}{\text{NumWords}} \times 100\right) + 0.0923 \times \frac{\text{NumWords}}{\text{NumSentences}}
     \end{align*}
 
-FORCAST Grade Level:
+FORCAST:
     \text{RGL} = 20.43 - 0.11 \times \text{NumWordsSyl}_1
 
-Fórmula de comprensibilidad de Gutiérrez de Polini:
-    \text{CP} = 95.2 - 9.7 \times \frac{\text{NumCharsAlpha}}{\text{NumWords}} - 0.35 \times \frac{\text{NumWords}}{\text{NumSentences}}
-
-Fórmula de Crawford:
-    {\text{Grade Level} = \frac{\text{NumSentences}}{\text{NumWords}} \times 100 \times (-0.205) + \frac{\text{NumSyls}}{\text{NumWords}} \times 100 \times 0.049 - 3.407}
-
 Fucks's Stilcharakteristik:
     \text{Stilcharakteristik} = \frac{\text{NumWords}}{\text{NumSentneces}} \times \frac{\text{NumSyls}}{\text{NumWords}}
 
-Gulpease Index:
-    \text{Gulpease Index} = 89 + \frac{300 \times \text{NumSentences} - 10 \times \text{NumCharsAlpha}}{\text{NumWords}}
+GULPEASE:
+    \text{GULPEASE} = 89 + \frac{300 \times \text{NumSentences} - 10 \times \text{NumCharsAlpha}}{\text{NumWords}}
 
 Gunning Fog Index:
     \begin{align*}
@@ -1059,10 +1058,13 @@ Gunning Fog Index:
         \text{Fog Index}_\text{Polish} &= \frac{\sqrt{\left(\frac{\text{NumWords}}{\text{NumSentences}}\right)^2 + \left(\frac{\text{NumHardWords}}{\text{NumWords}} \times 100\right)^2}}{2}
     \end{align*}
 
+Gutiérrez de Polini's readability formula:
+    \text{CP} = 95.2 - 9.7 \times \frac{\text{NumCharsAlpha}}{\text{NumWords}} - 0.35 \times \frac{\text{NumWords}}{\text{NumSentences}}
+
 Legibilidad µ:
     \mu = \frac{\text{NumWords}}{\text{NumWords} - 1} \times \frac{\text{LenWordsAvg}}{\text{LenWordsVar}} \times 100
 
-Lensear Write:
+Lensear Write Formula:
     \text{Score} = \text{NumWordsSyl}_1 + 3 \times \text{NumSentences}
 
 Lix:
@@ -1071,10 +1073,10 @@ Lix:
 Lorge Readability Index:
     \begin{align*}
         \text{Lorge} &= \frac{\text{NumWords}}{\text{NumSentences}} \times 0.07 + \frac{\text{NumPreps}}{\text{NumWords}} \times 13.01 + \frac{\text{NumWordTypesDale}_\text{769}}{\text{NumWords}} \times 10.73 + 1.6126 \\
-        \text{Lorge}_\text{Corrected} &= \frac{\text{NumWords}}{\text{NumSentences}} \times 0.06 + \frac{\text{NumPreps}}{\text{NumWords}} \times 0.1 + \frac{\text{NumWordTypesDale}_\text{769}}{\text{NumWords}} \times 0.1 + 1.99
+        \text{Lorge}_\text{corrected} &= \frac{\text{NumWords}}{\text{NumSentences}} \times 0.06 + \frac{\text{NumPreps}}{\text{NumWords}} \times 0.1 + \frac{\text{NumWordTypesDale}_\text{769}}{\text{NumWords}} \times 0.1 + 1.99
     \end{align*}
 
-Luong-Nguyen-Dinh's Readability Formula:
+Luong-Nguyen-Dinh's readability formula:
     {\text{Readability} = 0.004 \times \frac{\text{NumCharsAlnum}}{\text{NumSentences}} + 0.1905 \times \frac{\text{NumCharsAlnum}}{\text{NumWords}} + 2.7147 \times \frac{\text{NumSylsLuongNguyenDinh}_\text{1000}}{\text{NumSyls}} - 0.7295}
 
 McAlpine EFLAW Readability Score:
@@ -1105,74 +1107,74 @@ OSMAN:
 Rix:
     \text{Rix} = \frac{\text{NumWordsLtrs}_{7+}}{\text{NumSentences}}
 
-SMOG Grade:
+SMOG Grading:
     \begin{align*}
         \text{g} &= 3.1291 + 1.043 \times \sqrt{\text{NumWordsSyls}_{3+}} \\
         \text{g}_\text{German} &= \sqrt{\frac{\text{NumWordsSyl}_{3+}}{\text{NumSentences}} \times 30} - 2
     \end{align*}
 
-Spache Grade Level:
+Spache readability formula:
     \begin{align*}
-        \text{Grade Level} &= 0.141 \times \frac{100}{\text{NumSentences}} + 0.086 \times \left(\frac{\text{NumWordsDale}_{769}}{100} \times 100\right) + 0.839 \\
-        \text{Grade Level}_{Revised} &= 0.121 \times \frac{100}{\text{NumSentences}} + 0.082 \times \left(\frac{\text{NumWordsSpache}}{100} \times 100\right) + 0.659
+        \text{Grade level} &= 0.141 \times \frac{100}{\text{NumSentences}} + 0.086 \times \left(\frac{\text{NumWordsDale}_{769}}{100} \times 100\right) + 0.839 \\
+        \text{Grade level}_{revised} &= 0.121 \times \frac{100}{\text{NumSentences}} + 0.082 \times \left(\frac{\text{NumWordsSpache}}{100} \times 100\right) + 0.659
     \end{align*}
 
 Strain Index:
     \text{Strain Index} = \frac{\text{NumSyls}}{10}
 
-Tränkle & Bailer's Readability Formula:
+Tränkle-Bailer's readability formula:
     \begin{align*}
         \text{Tr}\ddot{\text{a}}\text{nkle-Bailer}_1 &= 224.6814 - \ln{\frac{\text{NumCharsAlnum}}{\text{NumWords}}} \times 79.8304 - \ln{\frac{\text{NumWords}}{\text{NumSentences}}} \times 12.24032 - \text{NumPreps} \times 1.292857 \\
         \text{Tr}\ddot{\text{a}}\text{nkle-Bailer}_2 &= 234.1063 - \ln{\frac{\text{NumCharsAlnum}}{\text{NumWords}}} \times 96.11069 - \text{NumPreps} \times 2.05444 - \text{NumConjs} \times 1.02805
     \end{align*}
 
-Tuldava's Text Difficulty:
+Tuldava's readability formula:
     \text{TD} = \frac{\text{NumSyls}}{\text{NumWords}} \times \ln \frac{\text{NumWords}}{\text{NumSentences}}
 
-Wheeler & Smith's Readability Formula:
+Wheeler-Smith's readability formula:
     \text{Wheeler-Smith} = \frac{\text{NumWords}}{\text{NumUnits}} \times \frac{\text{NumWordsSyls}_{2+}}{\text{NumWords}} \times 10
 -->
 
 Readability Formula|Formula|Supported Languages
 -------------------|-------|:-----------------:
-<span id="ref-rd"></span>Al-Heeti's Readability Prediction Formula¹<br>([Al-Heeti, 1984, pp. 102, 104, 106](#ref-al-heeti-1984))|![Formula](/doc/measures/readability/rd.svg)|**Arabic**
+<span id="ref-rd"></span>Al-Heeti's readability formula¹<br>([Al-Heeti, 1984, pp. 102, 104, 106](#ref-al-heeti-1984))|![Formula](/doc/measures/readability/rd.svg)|**Arabic**
 <span id="ref-aari"></span>Automated Arabic Readability Index<br>([Al-Tamimi et al., 2013](#ref-al-tamimi-et-al-2013))|![Formula](/doc/measures/readability/aari.svg)|**Arabic**
 <span id="ref-ari"></span>Automated Readability Index¹<br>([Smith & Senter, 1967, p. 8](#ref-smith-senter-1967)<br>Navy: [Kincaid et al., 1975, p. 14](#ref-kincaid-et-al-1975))|![Formula](/doc/measures/readability/ari.svg)|All languages
-<span id="ref-bormuths-cloze-mean-gp"></span>Bormuth's Cloze Mean & Grade Placement<br>([Bormuth, 1969, pp. 152, 160](#ref-bormuth-1969))|![Formula](/doc/measures/readability/bormuths_cloze_mean_gp.svg)<br>where **C** is the cloze criterion score, whose value could be changed via **Menu Bar → Preferences → Settings → Measures → Readability → Bormuth's Grade Placement → Cloze criterion score**|**English**
-<span id="ref-coleman-liau-index"></span>Coleman-Liau Index<br>([Coleman & Liau, 1975](#ref-coleman-liau-1975))|![Formula](/doc/measures/readability/coleman_liau_index.svg)|All languages
-<span id="ref-colemans-readability-formula"></span>Coleman's Readability Formula¹<br>([Liau et al., 1976](#ref-liau-et-al-1976))|![Formula](/doc/measures/readability/colemans_readability_formula.svg)|All languages²³
-<span id="ref-x-c50"></span>Dale-Chall Readability Formula¹<br>([Dale & Chall, 1948a](#ref-dale-chall-1948a); [Dale & Chall, 1948b](#ref-dale-chall-1948b)<br>Powers-Sumner-Kearl: [Powers et al., 1958](#ref-powers-et-al-1958)<br>New: [Chall & Dale, 1995](#ref-chall-dale-1995))|![Formula](/doc/measures/readability/x_c50.svg)|**English**
-<span id="ref-danielson-bryans-readability-formula"></span>Danielson-Bryan's Readability Formula¹<br>([Danielson & Bryan, 1963](#ref-danielson-bryan-1963))|![Formula](/doc/measures/readability/danielson_bryans_readability_formula.svg)|All languages
-<span id="ref-dawoods-readability-formula"></span>Dawood's Readability Formula<br>([Dawood, 1977](#ref-dawood-1977))|![Formula](/doc/measures/readability/dawoods_readability_formula.svg)|**Arabic**
+<span id="ref-bormuths-cloze-mean-gp"></span>Bormuth's cloze mean & grade placement<br>([Bormuth, 1969, pp. 152, 160](#ref-bormuth-1969))|![Formula](/doc/measures/readability/bormuths_cloze_mean_gp.svg)<br>where **C** is the cloze criterion score, whose value could be changed via **Menu Bar → Preferences → Settings → Measures → Readability → Bormuth's Grade Placement → Cloze criterion score**|**English**
+<span id="ref-coleman-liau-index"></span>Coleman-Liau index<br>([Coleman & Liau, 1975](#ref-coleman-liau-1975))|![Formula](/doc/measures/readability/coleman_liau_index.svg)|All languages
+<span id="ref-colemans-readability-formula"></span>Coleman's readability formula¹<br>([Liau et al., 1976](#ref-liau-et-al-1976))|![Formula](/doc/measures/readability/colemans_readability_formula.svg)|All languages²³
+<span id="ref-crawfords-readability-formula"></span>Crawford's readability formula<br>([Crawford, 1985](#ref-crawford-1985))|![Formula](/doc/measures/readability/crawfords_readability_formula.svg)|**Spanish**²
+<span id="ref-x-c50"></span>Dale-Chall readability formula¹<br>([Dale & Chall, 1948a](#ref-dale-chall-1948a); [Dale & Chall, 1948b](#ref-dale-chall-1948b)<br>Powers-Sumner-Kearl: [Powers et al., 1958](#ref-powers-et-al-1958)<br>New: [Chall & Dale, 1995](#ref-chall-dale-1995))|![Formula](/doc/measures/readability/x_c50.svg)|**English**
+<span id="ref-danielson-bryans-readability-formula"></span>Danielson-Bryan's readability formula¹<br>([Danielson & Bryan, 1963](#ref-danielson-bryan-1963))|![Formula](/doc/measures/readability/danielson_bryans_readability_formula.svg)|All languages
+<span id="ref-dawoods-readability-formula"></span>Dawood's readability formula<br>([Dawood, 1977](#ref-dawood-1977))|![Formula](/doc/measures/readability/dawoods_readability_formula.svg)|**Arabic**
 <span id="ref-drp"></span>Degrees of Reading Power<br>([College Entrance Examination Board, 1981](#ref-college-entrance-examination-board-1981))|![Formula](/doc/measures/readability/drp.svg)<br>where **M** is *Bormuth's cloze mean*.|**English**
 <span id="ref-devereux-readability-index"></span>Devereux Readability Index<br>([Smith, 1961](#ref-smith-1961))|![Formula](/doc/measures/readability/devereux_readability_index.svg)|All languages
 <span id="ref-dickes-steiwer-handformel"></span>Dickes-Steiwer Handformel<br>([Dickes & Steiwer, 1977](#ref-dickes-steiwer-1977))|![Formula](/doc/measures/readability/dickes_steiwer_handformel.svg)|All languages
 <span id="ref-elf"></span>Easy Listening Formula<br>([Fang, 1966](#ref-fang-1966))|![Formula](/doc/measures/readability/elf.svg)|All languages²
-<span id="ref-gl"></span>Flesch-Kincaid Grade Level<br>([Kincaid et al., 1975, p. 14](#ref-kincaid-et-al-1975))|![Formula](/doc/measures/readability/gl.svg)|All languages²
-<span id="ref-re"></span>Flesch Reading Ease¹<br>([Flesch, 1948](#ref-flesch-1948)<br>Powers-Sumner-Kearl: [Powers et al., 1958](#ref-powers-et-al-1958)<br>Dutch: [Douma, 1960, p. 453](#ref-douma-1960); [Brouwer, 1963](#ref-brouwer-1963)<br>French: [Kandel & Moles, 1958](#ref-kandel-moles-1958)<br>German: [Amstad, 1978](#ref-amstad-1978)<br>Italian: [Franchina & Vacca, 1986](#ref-franchina-vacca-1986)<br>Russian: [Oborneva, 2006, p. 13](#ref-oborneva-2006)<br>Spanish: [Fernández Huerta, 1959](#ref-fernandez-huerta-1959); [Szigriszt Pazos, 1993, p. 247](#ref-szigrisze-pazos-1993)<br>Ukrainian: [Partiko, 2001](#ref-partiko-2001))|![Formula](/doc/measures/readability/re.svg)|All languages²
-<span id="ref-re-farr-jenkins-paterson"></span>Flesch Reading Ease (Farr-Jenkins-Paterson)¹<br>([Farr et al., 1951](#ref-farr-et-al-1951)<br>Powers-Sumner-Kearl: [Powers et al., 1958](#ref-powers-et-al-1958))|![Formula](/doc/measures/readability/re_farr_jenkins_paterson.svg)|All languages²
-<span id="ref-rgl"></span>FORCAST Grade Level<br>([Caylor & Sticht, 1973, p. 3](#ref-caylor-sticht-1973))|![Formula](/doc/measures/readability/rgl.svg)<br><br>* **One sample of 150 words** would be taken randomly from the text, so the text should be **at least 150 words long**.|All languages²
-<span id="ref-cp"></span>Fórmula de comprensibilidad de Gutiérrez de Polini<br>([Gutiérrez de Polini, 1972](#ref-gutierrez-de-polini-1972))|![Formula](/doc/measures/readability/cp.svg)|**Spanish**
-<span id="ref-formula-de-crawford"></span>Fórmula de Crawford<br>([Crawford, 1985](#ref-crawford-1985))|![Formula](/doc/measures/readability/formula_de_crawford.svg)|**Spanish**²
+<span id="ref-gl"></span>Flesch-Kincaid grade level<br>([Kincaid et al., 1975, p. 14](#ref-kincaid-et-al-1975))|![Formula](/doc/measures/readability/gl.svg)|All languages²
+<span id="ref-re"></span>Flesch reading ease¹<br>([Flesch, 1948](#ref-flesch-1948)<br>Powers-Sumner-Kearl: [Powers et al., 1958](#ref-powers-et-al-1958)<br>Dutch: [Douma, 1960, p. 453](#ref-douma-1960); [Brouwer, 1963](#ref-brouwer-1963)<br>French: [Kandel & Moles, 1958](#ref-kandel-moles-1958)<br>German: [Amstad, 1978](#ref-amstad-1978)<br>Italian: [Franchina & Vacca, 1986](#ref-franchina-vacca-1986)<br>Russian: [Oborneva, 2006, p. 13](#ref-oborneva-2006)<br>Spanish: [Fernández Huerta, 1959](#ref-fernandez-huerta-1959); [Szigriszt Pazos, 1993, p. 247](#ref-szigrisze-pazos-1993)<br>Ukrainian: [Partiko, 2001](#ref-partiko-2001))|![Formula](/doc/measures/readability/re.svg)|All languages²
+<span id="ref-re-farr-jenkins-paterson"></span>Flesch reading ease (Farr-Jenkins-Paterson)¹<br>([Farr et al., 1951](#ref-farr-et-al-1951)<br>Powers-Sumner-Kearl: [Powers et al., 1958](#ref-powers-et-al-1958))|![Formula](/doc/measures/readability/re_farr_jenkins_paterson.svg)|All languages²
+<span id="ref-rgl"></span>FORCAST<br>([Caylor & Sticht, 1973, p. 3](#ref-caylor-sticht-1973))|![Formula](/doc/measures/readability/rgl.svg)<br><br>* **One sample of 150 words** would be taken randomly from the text, so the text should be **at least 150 words long**.|All languages²
 <span id="ref-fuckss-stilcharakteristik"></span>Fucks's Stilcharakteristik<br>([Fucks, 1955](#ref-fucks-1955))|![Formula](/doc/measures/readability/fuckss_stilcharakteristik.svg)|All languages²
-<span id="ref-gulpease-index"></span>Gulpease Index<br>([Lucisano & Emanuela Piemontese, 1988](#ref-lucisano-emanuela-piemontese-1988))|![Formula](/doc/measures/readability/gulpease_index.svg)|**Italian**
+<span id="ref-gulpease"></span>GULPEASE<br>([Lucisano & Emanuela Piemontese, 1988](#ref-lucisano-emanuela-piemontese-1988))|![Formula](/doc/measures/readability/gulpease.svg)|**Italian**
 <span id="ref-fog-index"></span>Gunning Fog Index¹<br>(English: [Gunning, 1968, p. 38](#ref-gunning-1968)<br>Powers-Sumner-Kearl: [Powers et al., 1958](#ref-powers-et-al-1958)<br>Navy: [Kincaid et al., 1975, p. 14](#ref-kincaid-et-al-1975)<br>Polish: [Pisarek, 1969](#ref-pisarek-1969))|![Formula](/doc/measures/readability/fog_index.svg)<br>where **NumHardWords** is the number of words with 3 or more syllables, except proper nouns and words with 3 syllables ending with *-ed* or *-es*, for **English texts**, and the number of words with 4 or more syllables in their base forms, except proper nouns, for **Polish texts**.|**English & Polish**²
+<span id="ref-cp"></span>Gutiérrez de Polini's readability formula<br>([Gutiérrez de Polini, 1972](#ref-gutierrez-de-polini-1972))|![Formula](/doc/measures/readability/cp.svg)|**Spanish**
 <span id="ref-mu"></span>Legibilidad µ<br>([Muñoz Baquedano, 2006](#ref-munoz-baquedano-2006))|![Formula](/doc/measures/readability/mu.svg)<br>where **LenWordsAvg** is the average word length in letters, and **LenWordsVar** is the variance of word lengths in letters.|**Spanish**
-<span id="ref-lensear-write"></span>Lensear Write<br>([O’Hayre, 1966, p. 8](#ref-o-hayre-1966))|![Formula](/doc/measures/readability/lensear_write.svg)<br>where **NumWords1Syl** is the number of monosyllabic words excluding *the*, *is*, *are*, *was*, *were*.<br><br>* **One sample of 100 words** would be taken randomly from the text, and if the text is **shorter than 100 words**, **NumWords1Syl** and **NumSentences** would be multiplied by 100 and then divided by **NumWords**.|**English**²
+<span id="ref-lensear-write-formula"></span>Lensear Write Formula<br>([O’Hayre, 1966, p. 8](#ref-o-hayre-1966))|![Formula](/doc/measures/readability/lensear_write_formula.svg)<br>where **NumWords1Syl** is the number of monosyllabic words excluding *the*, *is*, *are*, *was*, *were*.<br><br>* **One sample of 100 words** would be taken randomly from the text, and if the text is **shorter than 100 words**, **NumWords1Syl** and **NumSentences** would be multiplied by 100 and then divided by **NumWords**.|**English**²
 <span id="ref-lix"></span>Lix<br>([Björnsson, 1968](#ref-bjornsson-1968))|![Formula](/doc/measures/readability/lix.svg)|All languages
 <span id="ref-lorge-readability-index"></span>Lorge Readability Index¹<br>([Lorge, 1944](#ref-lorge-1944)<br>Corrected: [Lorge, 1948](#ref-lorge-1948))|![Formula](/doc/measures/readability/lorge_readability_index.svg)|**English**³
-<span id="ref-luong-nguyen-dinhs-readability-formula"></span>Luong-Nguyen-Dinh's Readability Formula<br>([Luong et al., 2018](#ref-luong-et-al-2018))|![Formula](/doc/measures/readability/luong_nguyen_dinh_readability_formula.svg)<br><br>* The number of syllables is estimated by tokenizing the text by whitespace and counting the number of tokens excluding punctuation marks|**Vietnamese**
-<span id="ref-eflaw"></span>McAlpine EFLAW Readability Score<br>([Nirmaldasan, 2009](#ref-nirmaldasan-2009))|![Formula](/doc/measures/readability/eflaw.svg)|**English**
+<span id="ref-luong-nguyen-dinhs-readability-formula"></span>Luong-Nguyen-Dinh's readability formula<br>([Luong et al., 2018](#ref-luong-et-al-2018))|![Formula](/doc/measures/readability/luong_nguyen_dinhs_readability_formula.svg)<br><br>* The number of syllables is estimated by tokenizing the text by whitespace and counting the number of tokens excluding punctuation marks|**Vietnamese**
+<span id="ref-eflaw"></span>McAlpine EFLAW Readability Score<br>([McAlpine, 2006](#ref-mcalpine-2006))|![Formula](/doc/measures/readability/eflaw.svg)|**English**
 <span id="ref-nwl"></span>neue Wiener Literaturformeln¹<br>([Bamberger & Vanecek, 1984, p. 82](#ref-bamberger-vanecek-1984))|![Formula](/doc/measures/readability/nwl.svg)|**German**²
 <span id="ref-nws"></span>neue Wiener Sachtextformel¹<br>([Bamberger & Vanecek, 1984, pp. 83–84](#ref-bamberger-vanecek-1984))|![Formula](/doc/measures/readability/nws.svg)|**German**²
 <span id="ref-osman"></span>OSMAN<br>([El-Haj & Rayson, 2016](#ref-elhaj-rayson-2016))|![Formula](/doc/measures/readability/osman.svg)<br>where **NumFaseehWords** is the number of words which have 5 or more syllables and contain ء/ئ/ؤ/ذ/ظ or end with وا/ون.<br><br>* The number of syllables in each word is estimated by adding up the number of short syllables and twice the number of long and stress syllables in each word.|**Arabic**
 <span id="ref-rix"></span>Rix<br>([Anderson, 1983](#ref-anderson-1983))|![Formula](/doc/measures/readability/rix.svg)|All languages
-<span id="ref-smog-grade"></span>SMOG Grade<br>([McLaughlin, 1969](#ref-mclaughlin-1969)<br>German: [Bamberger & Vanecek, 1984, p.78](#ref-bamberger-vanecek-1984))|![Formula](/doc/measures/readability/smog_grade.svg)<br><br>* A sample would be constructed using **the first 10 sentences, the last 10 sentences, and the 10 sentences at the middle of the text**, so the text should be **at least 30 sentences long**.|All languages²
-<span id="ref-spache-grade-level"></span>Spache Grade Level¹<br>([Spache, 1953](#ref-spache-1953)<br>Revised: [Spache, 1974](#ref-spache-1974))|![Formula](/doc/measures/readability/spache_grade_level.svg)<br><br>* **Three samples each of 100 words** would be taken randomly from the text and the results would be averaged out, so the text should be **at least 100 words long**.|All languages
-<span id="ref-strain-index"></span>Strain Index<br>([Solomon, 2006](#ref-solomon-2006))|![Formula](/doc/measures/readability/strain_index.svg)<br><br>* A sample would be constructed using **the first 3 sentences in the text**, so the text should be **at least 3 sentences long**.|All languages²
-<span id="ref-trankle-bailers-readability-formula"></span>Tränkle & Bailer's Readability Formula¹<br>([Tränkle & Bailer, 1984](#ref-trankle-bailer-1984))|![Formula](/doc/measures/readability/trankle_bailers_readability_formula.svg)<br><br>* **One sample of 100 words** would be taken randomly from the text, so the text should be **at least 100 words long**.|All languages³
-<span id="ref-td"></span>Tuldava's Text Difficulty<br>([Tuldava, 1975](#ref-tuldava-1975))|![Formula](/doc/measures/readability/td.svg)|All languages²
-<span id="ref-wheeler-smiths-readability-formula"></span>Wheeler & Smith's Readability Formula<br>([Wheeler & Smith, 1954](#ref-wheeler-smith-1954))|![Formula](/doc/measures/readability/wheeler_smiths_readability_formula.svg)<br>where **NumUnits** is the number of sentence segments ending in periods, question marks, exclamation marks, colons, semicolons, and dashes.|All languages²
+<span id="ref-smog-grading"></span>SMOG Grading<br>([McLaughlin, 1969](#ref-mclaughlin-1969)<br>German: [Bamberger & Vanecek, 1984, p.78](#ref-bamberger-vanecek-1984))|![Formula](/doc/measures/readability/smog_grading.svg)<br><br>* A sample would be constructed using **the first 10 sentences, the last 10 sentences, and the 10 sentences at the middle of the text**, so the text should be **at least 30 sentences long**.|All languages²
+<span id="ref-spache-readability-formula"></span>Spache readability formula¹<br>([Spache, 1953](#ref-spache-1953)<br>Revised: [Spache, 1974](#ref-spache-1974))|![Formula](/doc/measures/readability/spache_readability_formula.svg)<br><br>* **Three samples each of 100 words** would be taken randomly from the text and the results would be averaged out, so the text should be **at least 100 words long**.|English
+<span id="ref-strain-index"></span>Strain Index<br>([Nathaniel, 2017](#ref-nathaniel-2017))|![Formula](/doc/measures/readability/strain_index.svg)<br><br>* A sample would be constructed using **the first 3 sentences in the text**, so the text should be **at least 3 sentences long**.|All languages²
+<span id="ref-trankle-bailers-readability-formula"></span>Tränkle-Bailer's readability formula¹<br>([Tränkle & Bailer, 1984](#ref-trankle-bailer-1984))|![Formula](/doc/measures/readability/trankle_bailers_readability_formula.svg)<br><br>* **One sample of 100 words** would be taken randomly from the text, so the text should be **at least 100 words long**.|All languages³
+<span id="ref-td"></span>Tuldava's readability formula<br>([Tuldava, 1975](#ref-tuldava-1975))|![Formula](/doc/measures/readability/td.svg)|All languages²
+<span id="ref-wheeler-smiths-readability-formula"></span>Wheeler-Smith's readability formula<br>([Wheeler & Smith, 1954](#ref-wheeler-smith-1954))|![Formula](/doc/measures/readability/wheeler_smiths_readability_formula.svg)<br>where **NumUnits** is the number of sentence segments ending in periods, question marks, exclamation marks, colons, semicolons, and dashes.|All languages²
 
 > [!NOTE]
 > 1. Variants available and can be selected via **Menu Bar → Preferences → Settings → Measures → Readability**
@@ -1191,7 +1193,9 @@ The following variables would be used in formulas:<br>
 **NumTokens**: Number of tokens<br>
 
 <!--
-Brunét's Index:
+* Only people's names are capitalized and case of indicators of density/diversity is preserved as in original papers
+
+Brunét's index:
     \text{W} = \text{NumTokens}^{\text{NumTypes}^{-0.165}}
 
 Corrected TTR:
@@ -1200,14 +1204,14 @@ Corrected TTR:
 Fisher's Index of Diversity:
     \alpha = -\frac{\text{NumTokens} \times \text{NumTypes}}{\text{NumTokens} \times W_{-1}\left(-\frac{\exp\left(-\frac{\text{NumTypes}}{\text{NumTokens}}\right) \times \text{NumTypes}}{\text{NumTokens}}\right) + \text{NumTypes}}
 
-Herdan's Vₘ:
-    \text{V}_\text{m} = \frac{\sum_{f = 1}^{\text{f}_\text{max}}(\text{NumTypes}_f \times f^2)}{\text{NumTokens}^2} - \frac{1}{\text{NumTypes}}
+Herdan's vₘ:
+    \text{v}_\text{m} = \frac{\sum_{f = 1}^{\text{f}_\text{max}}(\text{NumTypes}_f \times f^2)}{\text{NumTokens}^2} - \frac{1}{\text{NumTypes}}
 
-Honoré's Statistic:
+Honoré's statistic:
     \text{R} = 100 \times \ln\frac{\text{NumTokens}}{1 - \frac{\text{NumTypes}_1}{\text{NumTypes}}
 
-Lexical Density:
-    \text{Lexical Density} = \frac{\text{NumContentWords}}{\text{NumTokens}}
+Lexical density:
+    \text{Lexical density} = \frac{\text{NumContentWords}}{\text{NumTokens}}
 
 LogTTR:
     \begin{align*}
@@ -1218,7 +1222,7 @@ LogTTR:
         \text{LogTTR}_\text{Dugast} &= \frac{\ln^2{\text{NumTokens}}}{\ln{\text{NumTokens}} - \ln{\text{NumTypes}}}
     \end{align*}
 
-Mean Segmental TTR:
+Mean segmental TTR:
     \text{MSTTR} = \frac{\sum_{i = 1}^{n}\frac{\text{NumTypesSeg}_i}{\text{NumTokensSeg}_i}}{n}
 
 Moving-average TTR:
@@ -1236,28 +1240,28 @@ Popescu-Mačutek-Altmann's B₁/B₂/B₃/B₄/B₅:
         \text{B}_5 &= \frac{\text{NumTypes}_1}{\text{L}}
     \end{align*}
 
-Repeat Rate:
+Repeat rate:
     \begin{align*}
-        \text{RR}_\text{Rank-frequency distribution} &= \frac{\sum_{i = 1}^\text{NumTypes}\text{f}_i^2}{\text{NumTokens}^2} \\
-        \text{RR}_\text{Frequency spectrum} &= \frac{\sum_{f = 1}^{\text{f}_\text{max}}\text{NumTypes}_f^2}{\text{NumTypes}^2}
+        \text{RR}_\text{rank-frequency distribution} &= \frac{\sum_{i = 1}^\text{NumTypes}\text{f}_i^2}{\text{NumTokens}^2} \\
+        \text{RR}_\text{frequency spectrum} &= \frac{\sum_{f = 1}^{\text{f}_\text{max}}\text{NumTypes}_f^2}{\text{NumTypes}^2}
     \end{align*}
 
 Root TTR:
     \text{RTTR} = \frac{\text{NumTypes}}{\sqrt{\text{NumTokens}}}
 
-Shannon Entropy:
+Shannon entropy:
     \begin{align*}
-        \text{H}_\text{Rank-frequency distribution} &= -\sum_{i = 1}^\text{NumTypes}\left(\frac{\text{f}_i}{\text{NumTokens}} \times \log_{2}\frac{\text{f}_i}{\text{NumTokens}}\right) \\
-        \text{H}_\text{Frequency spectrum} &= -\sum_{f = 1}^{\text{f}_\text{max}}\left(\frac{\text{NumTypes}_f}{\text{NumTypes}} \times \log_{2}\frac{\text{NumTypes}_f}{\text{NumTypes}}\right)
+        \text{H}_\text{rank-frequency distribution} &= -\sum_{i = 1}^\text{NumTypes}\left(\frac{\text{f}_i}{\text{NumTokens}} \times \log_{2}\frac{\text{f}_i}{\text{NumTokens}}\right) \\
+        \text{H}_\text{frequency spectrum} &= -\sum_{f = 1}^{\text{f}_\text{max}}\left(\frac{\text{NumTypes}_f}{\text{NumTypes}} \times \log_{2}\frac{\text{NumTypes}_f}{\text{NumTypes}}\right)
     \end{align*}
 
 Simpleson's l:
     \text{l} = \frac{\sum_{f = 1}^{\text{f}_\text{max}}(\text{NumTypes}_f \times f^2) - \text{NumTokens}}{\text{NumTokens} \times (\text{NumTokens} - 1)}
 
-Type-token Ratio:
+Type-token ratio:
     \text{TTR} = \frac{\text{NumTypes}}{\text{NumTokens}}
 
-Yule's Characteristic K:
+Yule's characteristic K:
     \text{K} = 10000 \times \frac{\sum_{f = 1}^{\text{f}_\text{max}}(\text{NumTypes}_f \times f^2) - \text{NumTokens}}{\text{NumTokens}^2}
 
 Yule's Index of Diversity:
@@ -1266,29 +1270,29 @@ Yule's Index of Diversity:
 
 Indicator of Lexical Density/Diversity|Formula
 --------------------------------------|-------
-<span id="ref-brunets-index"></span>Brunét's Index<br>([Brunét, 1978](#ref-brunet-1978))|![Formula](/doc/measures/lexical_density_diversity/brunets_index.svg)
+<span id="ref-brunets-index"></span>Brunét's index<br>([Brunét, 1978](#ref-brunet-1978))|![Formula](/doc/measures/lexical_density_diversity/brunets_index.svg)
 <span id="ref-cttr"></span>Corrected TTR<br>([Carroll, 1964](#ref-carroll-1964))|![Formula](/doc/measures/lexical_density_diversity/cttr.svg)
-<span id="ref-fishers-index-of-diversity"></span>Fisher's Index of Diversity<br>([Fisher et al., 1943](#ref-fisher-et-al-1943))|![Formula](/doc/measures/lexical_density_diversity/fishers_index_of_diversity.svg)<br>where W₋₁ is the -1 branch of the [Lambert W function](https://en.wikipedia.org/wiki/Lambert_W_function)
-<span id="ref-herdans-vm"></span>Herdan's Vₘ<br>([Herdan, 1955](#ref-herdan-1955))|![Formula](/doc/measures/lexical_density_diversity/herdans_vm.svg)
+<span id="ref-fishers-index-of-diversity"></span>Fisher's Index of Diversity<br>([Fisher et al., 1943](#ref-fisher-et-al-1943))|![Formula](/doc/measures/lexical_density_diversity/fishers_index_of_diversity.svg)<br>where *W*₋₁ is the -1 branch of the [Lambert W function](https://en.wikipedia.org/wiki/Lambert_W_function)
+<span id="ref-herdans-vm"></span>Herdan's vₘ<br>([Herdan, 1955](#ref-herdan-1955))|![Formula](/doc/measures/lexical_density_diversity/herdans_vm.svg)
 <span id="ref-hdd"></span>HD-D<br>([McCarthy & Jarvis, 2010](#ref-mccarthy-jarvis-2010))|For detailed calculation procedures, see reference.<br>The sample size could be modified via **Menu Bar → Preferences → Settings → Measures → Lexical Density/Diversity → HD-D → Sample size**.
-<span id="ref-honores-stat"></span>Honoré's Statistic<br>([Honoré, 1979](#ref-honore-1979))|![Formula](/doc/measures/lexical_density_diversity/honores_stat.svg)
-<span id="ref-lexical-density"></span>Lexical Density<br>([Ure, 1971](#ref-ure-1971))|![Formula](/doc/measures/lexical_density_diversity/lexical_density.svg)<br>where **NumContentWords** is the number of content words. By default, all tokens whose universal part-of-speech tags assigned by built-in part-of-speech taggers are ADJ (adjectives), ADV (adverbs), INTJ (interjections), NOUN (nouns), PROPN (proper nouns), NUM (numerals), VERB (verbs), SYM (symbols), or X (others) are categorized as content words. For some built-in part-of-speech taggers, this behavior could be changed via **Menu Bar → Preferences → Settings → Part-of-speech Tagging → Tagsets → Mapping Settings → Content/Function Words**.
+<span id="ref-honores-stat"></span>Honoré's statistic<br>([Honoré, 1979](#ref-honore-1979))|![Formula](/doc/measures/lexical_density_diversity/honores_stat.svg)
+<span id="ref-lexical-density"></span>Lexical density<br>([Halliday, 1989, p. 64](#ref-halliday-1989))|![Formula](/doc/measures/lexical_density_diversity/lexical_density.svg)<br>where **NumContentWords** is the number of content words. By default, all tokens whose universal part-of-speech tags assigned by built-in part-of-speech taggers are ADJ (adjectives), ADV (adverbs), INTJ (interjections), NOUN (nouns), PROPN (proper nouns), NUM (numerals), VERB (verbs), SYM (symbols), or X (others) are categorized as content words. For some built-in part-of-speech taggers, this behavior could be changed via **Menu Bar → Preferences → Settings → Part-of-speech Tagging → Tagsets → Mapping Settings → Content/Function Words**.
 <span id="ref-logttr"></span>LogTTR¹<br>(Herdan: [Herdan, 1960, p. 28](#ref-herdan-1960)<br>Somers: [Somers, 1966](#ref-somers-1966)<br>Rubet: [Dugast, 1979](#ref-dugast-1979)<br>Maas: [Maas, 1972](#ref-maas-1972)<br>Dugast: [Dugast, 1978](#ref-dugast-1978); [Dugast, 1979](#ref-dugast-1979))|![Formula](/doc/measures/lexical_density_diversity/logttr.svg)
-<span id="ref-msttr"></span>Mean Segmental TTR<br>([Johnson, 1944](#ref-johnson-1944))|![Formula](/doc/measures/lexical_density_diversity/msttr.svg)<br>where **n** is the number of equal-sized segment, the length of which could be modified via **Menu Bar → Preferences → Settings → Measures → Lexical Density/Diversity → Mean Segmental TTR → Number of tokens in each segment**, **NumTypesSegᵢ** is the number of token types in the **i**-th segment, and **NumTokensSegᵢ** is the number of tokens in the **i**-th segment.
-<span id="ref-mtld"></span>Measure of Textual Lexical Diversity<br>([McCarthy, 2005, pp. 95–96, 99–100](#ref-mccarthy-2005); [McCarthy & Jarvis, 2010](#ref-mccarthy-jarvis-2010))|For detailed calculation procedures, see references.<br>The factor size could be modified via **Menu Bar → Preferences → Settings → Measures → Lexical Density/Diversity → Measure of Textual Lexical Diversity → Factor size**.
+<span id="ref-msttr"></span>Mean segmental TTR<br>([Johnson, 1944](#ref-johnson-1944))|![Formula](/doc/measures/lexical_density_diversity/msttr.svg)<br>where **n** is the number of equal-sized segment, the length of which could be modified via **Menu Bar → Preferences → Settings → Measures → Lexical Density/Diversity → Mean Segmental TTR → Number of tokens in each segment**, **NumTypesSegᵢ** is the number of token types in the **i**-th segment, and **NumTokensSegᵢ** is the number of tokens in the **i**-th segment.
+<span id="ref-mtld"></span>Measure of textual lexical diversity<br>([McCarthy, 2005, pp. 95–96, 99–100](#ref-mccarthy-2005); [McCarthy & Jarvis, 2010](#ref-mccarthy-jarvis-2010))|For detailed calculation procedures, see references.<br>The factor size could be modified via **Menu Bar → Preferences → Settings → Measures → Lexical Density/Diversity → Measure of Textual Lexical Diversity → Factor size**.
 <span id="ref-mattr"></span>Moving-average TTR<br>([Covington & McFall, 2010](#ref-covington-mcfall-2010))|![Formula](/doc/measures/lexical_density_diversity/mattr.svg)<br>where **w** is the window size which could be modified via **Menu Bar → Preferences → Settings → Measures → Lexical Density/Diversity → Moving-average TTR → Window size**, **NumTypesWindowₚ** is the number of token types within the moving window starting at position **p**, and **NumTokensWindowₚ** is the number of tokens within the moving window starting at position **p**.
 <span id="ref-popescu-macutek-altmanns-b1-b2-b3-b4-b5"></span>Popescu-Mačutek-Altmann's B₁/B₂/B₃/B₄/B₅<br>([Popescu et al., 2008](#ref-popescu-et-al-2008))|![Formula](/doc/measures/lexical_density_diversity/popescu_macutek_altmanns_b1_b2_b3_b4_b5.svg)
 <span id="ref-popescus-r1"></span>Popescu's R₁<br>([Popescu, 2009, pp. 18, 30, 33](#ref-popescu-2009))|For detailed calculation procedures, see reference.
 <span id="ref-popescus-r2"></span>Popescu's R₂<br>([Popescu, 2009, pp. 35–36, 38](#ref-popescu-2009))|For detailed calculation procedures, see reference.
 <span id="ref-popescus-r3"></span>Popescu's R₃<br>([Popescu, 2009, pp. 48–49, 53](#ref-popescu-2009))|For detailed calculation procedures, see reference.
 <span id="ref-popescus-r4"></span>Popescu's R₄<br>([Popescu, 2009, p. 57](#ref-popescu-2009))|For detailed calculation procedures, see reference.
-<span id="ref-repeat-rate"></span>Repeat Rate¹<br>([Popescu, 2009, p. 166](#ref-popescu-2009))|![Formula](/doc/measures/lexical_density_diversity/repeat_rate.svg)
+<span id="ref-repeat-rate"></span>Repeat rate¹<br>([Popescu, 2009, p. 166](#ref-popescu-2009))|![Formula](/doc/measures/lexical_density_diversity/repeat_rate.svg)
 <span id="ref-rttr"></span>Root TTR<br>([Guiraud, 1954](#ref-guiraud-1954))|![Formula](/doc/measures/lexical_density_diversity/rttr.svg)
-<span id="ref-shannon-entropy"></span>Shannon Entropy¹<br>([Popescu, 2009, p. 173](#ref-popescu-2009))|![Formula](/doc/measures/lexical_density_diversity/shannon_entropy.svg)
+<span id="ref-shannon-entropy"></span>Shannon entropy¹<br>([Popescu, 2009, p. 173](#ref-popescu-2009))|![Formula](/doc/measures/lexical_density_diversity/shannon_entropy.svg)
 <span id="ref-simpsons-l"></span>Simpson's l<br>([Simpson, 1949](#ref-simpson-1949))|![Formula](/doc/measures/lexical_density_diversity/simpsons_l.svg)
-<span id="ref-ttr"></span>Type-token Ratio<br>([Johnson, 1944](#ref-johnson-1944))|![Formula](/doc/measures/lexical_density_diversity/ttr.svg)
+<span id="ref-ttr"></span>Type-token ratio<br>([Johnson, 1944](#ref-johnson-1944))|![Formula](/doc/measures/lexical_density_diversity/ttr.svg)
 <span id="ref-vocdd"></span>vocd-D<br>([Malvern et al., 2004, pp. 51, 56–57](#ref-malvern-et-al-2004))|For detailed calculation procedures, see reference.
-<span id="ref-yules-characteristic-k"></span>Yule's Characteristic K<br>([Yule, 1944, pp. 52–53](#ref-yule-1944))|![Formula](/doc/measures/lexical_density_diversity/yules_characteristic_k.svg)
+<span id="ref-yules-characteristic-k"></span>Yule's characteristic K<br>([Yule, 1944, pp. 52–53](#ref-yule-1944))|![Formula](/doc/measures/lexical_density_diversity/yules_characteristic_k.svg)
 <span id="ref-yules-index-of-diversity"></span>Yule's Index of Diversity<br>([Williams, 1970, p. 100](#ref-williams-1970))|![Formula](/doc/measures/lexical_density_diversity/yules_index_of_diversity.svg)
 
 > [!NOTE]
@@ -1304,16 +1308,16 @@ For distance-based measures, the distance between each pair of subsequent occurr
 Then, the dispersion and adjusted frequency of the word are calculated as follows:
 
 <!--
-Average Logarithmic Distance:
+Average logarithmic distance:
     \begin{align*}
         \text{ALD} &= \frac{1}{N} \times \sum_{i = 1}^{F}(d_i \times \log_{10}d_i) \\
         \text{f}_{\text{ALD}} &= \exp\left(-\sum_{i = 1}^{F}{\frac{d_i}{N} \times \ln\frac{d_i}{N}}\right)
     \end{align*}
 
-Average Reduced Frequency:
+Average reduced frequency:
     \text{ARF} = \text{f}_{\text{ARF}} = \frac{F}{N} \times \sum_{i = 1}^{F}\min\left\{d_i, \frac{N}{F}\right\}
 
-Average Waiting Time:
+Average waiting time:
     \begin{align*}
         \text{AWT} &= \frac{1}{2} \times \left(1 + \frac{1}{N} \times \sum_{i = 1}^{F}{d_i^2}\right) \\
         \text{f}_{\text{AWT}} &= \frac{N^2}{\sum_{i = 1}^F{d_i^2}}
@@ -1375,14 +1379,14 @@ Measure of Dispersion (Parts-based)|Measure of Adjusted Frequency (Parts-based)|
 
 Measure of Dispersion (Distance-based)|Measure of Adjusted Frequency (Distance-based)|Formula
 --------------------------------------|----------------------------------------------|-------
-<span id="ref-ald"></span>Average Logarithmic Distance<br>([Savický & Hlaváčová, 2002](#ref-savicky-hlavacova-2002))|<span id="ref-fald"></span>Average Logarithmic Distance<br>([Savický & Hlaváčová, 2002](#ref-savicky-hlavacova-2002))|![Formula](/doc/measures/dispersion_adjusted_frequency/ald.svg)
-<span id="ref-arf"></span>Average Reduced Frequency<br>([Savický & Hlaváčová, 2002](#ref-savicky-hlavacova-2002))|<span id="ref-farf"></span>Average Reduced Frequency<br>([Savický & Hlaváčová, 2002](#ref-savicky-hlavacova-2002))|![Formula](/doc/measures/dispersion_adjusted_frequency/arf.svg)
-<span id="ref-awt"></span>Average Waiting Time<br>([Savický & Hlaváčová, 2002](#ref-savicky-hlavacova-2002))|<span id="ref-fawt"></span>Average Waiting Time<br>([Savický & Hlaváčová, 2002](#ref-savicky-hlavacova-2002))|![Formula](/doc/measures/dispersion_adjusted_frequency/awt.svg)
+<span id="ref-ald"></span>Average logarithmic distance<br>([Savický & Hlaváčová, 2002](#ref-savicky-hlavacova-2002))|<span id="ref-fald"></span>Average logarithmic distance<br>([Savický & Hlaváčová, 2002](#ref-savicky-hlavacova-2002))|![Formula](/doc/measures/dispersion_adjusted_frequency/ald.svg)
+<span id="ref-arf"></span>Average reduced frequency<br>([Savický & Hlaváčová, 2002](#ref-savicky-hlavacova-2002))|<span id="ref-farf"></span>Average reduced frequency<br>([Savický & Hlaváčová, 2002](#ref-savicky-hlavacova-2002))|![Formula](/doc/measures/dispersion_adjusted_frequency/arf.svg)
+<span id="ref-awt"></span>Average waiting time<br>([Savický & Hlaváčová, 2002](#ref-savicky-hlavacova-2002))|<span id="ref-fawt"></span>Average waiting time<br>([Savický & Hlaváčová, 2002](#ref-savicky-hlavacova-2002))|![Formula](/doc/measures/dispersion_adjusted_frequency/awt.svg)
 
 <span id="doc-12-4-4"></span>
 #### [12.4.4 Tests of Statistical Significance, Measures of Bayes Factor, and Measures of Effect Size](#doc)
 
-In order to calculate the statistical significance, Bayes factor, and effect size (except **Mann-Whitney U Test**, **Student's t-test (2-sample)**, and **Welch's t-test**) for two words in the same file (collocates) or for one specific word in two different files (keywords), two contingency tables must be constructed first, one for observed values, the other for expected values.
+In order to calculate the statistical significance, Bayes factor, and effect size (except **Mann-Whitney U test**, **Student's t-test (2-sample)**, and **Welch's t-test**) for two words in the same file (collocates) or for one specific word in two different files (keywords), two contingency tables must be constructed first, one for observed values, the other for expected values.
 
 As for collocates (in *Collocation Extractor* and *Colligation Extractor*):
 
@@ -1420,20 +1424,20 @@ O₁₂: Number of occurrences of *Word w* in the reference file.<br>
 O₂₁: Number of occurrences of all words except *Word w* in the observed file.<br>
 O₂₂: Number of occurrences of all words except *Word w* in the reference file.
 
-To conduct **Mann-Whitney U Test**, **Student's t-test (2-sample)**, and **Welch's t-test** on a specific word, each column total is first divided into **n** (5 by default) sub-sections respectively. To be more specific, in *Collocation Extractor* and *Colligation Extractor*, all collocates where Word 1 appears as node and the other collocates where Word 1 does not appear as node are divided into **n** parts respectively. And in *Keyword Extractor*, all tokens in the observed file and all tokens in the reference files are equally divided into **n** parts respectively.
+To conduct **Mann-Whitney U test**, **Student's t-test (2-sample)**, and **Welch's t-test** on a specific word, each column total is first divided into **n** (5 by default) sub-sections respectively. To be more specific, in *Collocation Extractor* and *Colligation Extractor*, all collocates where Word 1 appears as node and the other collocates where Word 1 does not appear as node are divided into **n** parts respectively. And in *Keyword Extractor*, all tokens in the observed file and all tokens in the reference files are equally divided into **n** parts respectively.
 
 The frequencies of *Word 2* (in *Collocation Extractor* and *Colligation Extractor*) or *Word w* (in *Keyword Extractor*) in each sub-section of the 2 column totals are counted and denoted by **F₁₁**, **F₂₁**, **F₃₁**, ..., **Fₙ₁**, and **F₁₂**, **F₂₂**, **F₃₂**, ..., **Fₙ₂** respectively. The total frequency of *Word 2* (in *Collocation Extractor* and *Colligation Extractor*) or *Word w* (in *Keyword Extractor*) in the 2 column totals are denoted by **Fₓ₁** and **Fₓ₂** respectively. The mean value of the frequencies over all sub-sections in the 2 column totals are denoted by ![f_x1_bar](/doc/measures/f_x1_bar.svg) and ![f_x2_bar](/doc/measures/f_x2_bar.svg) respectively.
 
 Then the test statistic, Bayes factor, and effect size are calculated as follows:
 
 <!--
-Log-likelihood Ratio:
+Log-likelihood ratio test:
     \begin{align*}
         \text{G} &= 2 \times \sum_{i = 1}^2 \sum_{j = 1}^2 \left(O_{ij} \times \ln \frac{O_{ij}}{E_{ij}}\right) \\
         \text{BF} &= \text{G} - \ln O_{xx}
     \end{align*}
 
-Pearson's Chi-squared Test:
+Pearson's chi-squared test:
     \chi^2 = \sum_{i = 1}^2 \sum_{j = 1}^2 \frac{\left(O_{ij} - E{ij}\right)^2}{E_{ij}}
 
 Student's t-test (1-sample):
@@ -1447,10 +1451,10 @@ Student's t-test (2-sample):
         \text{BF} &= \text{t}^2 - \ln(2 \times n)
     \end{align*}
 
-z-score:
+Z-test:
     \text{z} = \frac{O_{11} - E_{11}}{\sqrt{E_{11} \times \left(1 - \frac{E_{11}}{O_{xx}}\right)}}
 
-z-score (Berry-Rogghe):
+Z-test (Berry-Rogghe):
     \begin{align*}
         \text{p} &= \frac{O_{x1}}{O_{xx} - O_{1x}} \\
         \text{E} &= \text{p} \times O_{1x} \times \text{S} \\
@@ -1460,47 +1464,47 @@ z-score (Berry-Rogghe):
 
 Test of Statistical Significance|Measure of Bayes Factor|Formula
 --------------------------------|-----------------------|-------
-<span id="ref-fishers-exact-test"></span>Fisher's Exact Test<br>([Pedersen, 1996](#ref-pedersen-1996))||See: [Fisher's exact test - Wikipedia](https://en.wikipedia.org/wiki/Fisher%27s_exact_test#Example)
-<span id="ref-log-likehood-ratio-test"></span>Log-likelihood Ratio Test<br>([Dunning, 1993](#ref-dunning-1993))|Log-likelihood Ratio Test<br>([Wilson, 2013](#ref-wilson-2013))|![Formula](/doc/measures/statistical_significance/log_likehood_ratio_test.svg)
-<span id="ref-mann-whiteney-u-test"></span>Mann-Whitney U Test<br>([Kilgarriff, 2001](#ref-kilgarriff-2001))||See: [Mann–Whitney U test - Wikipedia](https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test#Calculations)
-<span id="ref-pearsons-chi-squared-test"></span>Pearson's Chi-squared Test<br>([Hofland & Johanson, 1982](#ref-hofland-johanson-1982); [Oakes, 1998](#ref-oakes-1998))||![Formula](/doc/measures/statistical_significance/pearsons_chi_squared_test.svg)
+<span id="ref-fishers-exact-test"></span>Fisher's exact test<br>([Pedersen, 1996](#ref-pedersen-1996))||See: [Fisher's exact test - Wikipedia](https://en.wikipedia.org/wiki/Fisher%27s_exact_test#Example)
+<span id="ref-log-likehood-ratio-test"></span>Log-likelihood ratio test<br>([Dunning, 1993](#ref-dunning-1993))|Log-likelihood ratio test<br>([Wilson, 2013](#ref-wilson-2013))|![Formula](/doc/measures/statistical_significance/log_likehood_ratio_test.svg)
+<span id="ref-mann-whiteney-u-test"></span>Mann-Whitney U test<br>([Kilgarriff, 2001](#ref-kilgarriff-2001))||See: [Mann–Whitney U test - Wikipedia](https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test#Calculations)
+<span id="ref-pearsons-chi-squared-test"></span>Pearson's chi-squared test<br>([Hofland & Johansson, 1982](#ref-hofland-johansson-1982); [Oakes, 1998](#ref-oakes-1998))||![Formula](/doc/measures/statistical_significance/pearsons_chi_squared_test.svg)
 <span id="ref-students-t-test-1-sample"></span>Student's t-test (1-sample)<br>([Church et al., 1991](#ref-church-et-al-1991))||![Formula](/doc/measures/statistical_significance/students_t_test_1_sample.svg)
 <span id="ref-students-t-test-2-sample"></span>Student's t-test (2-sample)<br>([Paquot & Bestgen, 2009](#ref-paquot-bestgen-2009))|Student's t-test (2-sample)<br>([Wilson, 2013](#ref-wilson-2013))|![Formula](/doc/measures/statistical_significance/students_t_test_2_sample.svg)
-<span id="ref-z-score"></span>z-score<br>([Dennis, 1964](#ref-dennis-1964))||![Formula](/doc/measures/statistical_significance/z_score.svg)
-<span id="ref-z-score-berry-rogghes"></span>z-score (Berry-Rogghe)<br>([Berry-Rogghe, 1973](#ref-berry-rogghe-1973))||![Formula](/doc/measures/statistical_significance/z_score_berry_rogghe.svg)<br>where **S** is the average span size on both sides of the node word.
+<span id="ref-z-test"></span>Z-test<br>([Dennis, 1964](#ref-dennis-1964))||![Formula](/doc/measures/statistical_significance/z_test.svg)
+<span id="ref-z-test-berry-rogghes"></span>Z-test (Berry-Rogghe)<br>([Berry-Rogghe, 1973](#ref-berry-rogghe-1973))||![Formula](/doc/measures/statistical_significance/z_test_berry_rogghe.svg)<br>where **S** is the average span size on both sides of the node word.
 
 <!--
 %DIFF:
     \text{%DIFF} = \frac{\left(\frac{O_{11}}{O_{x1}} - \frac{O_{12}}{O_{x2}}\right) \times 100}{\frac{O_{12}}{O_{x2}}}
 
-Cubic Association Ratio:
+Cubic association ratio:
     \text{IM}^3 = \log_{2} \frac{{O_{11}}^3}{E_{11}}
 
-Dice's Coefficient:
+Dice-Sørensen coefficient:
     \text{DSC} = \frac{2 \times O_{11}}{O_{1x} + O_{x1}}
 
-Difference Coefficient:
-    \text{Difference Coefficient} = \frac{\frac{O_{11}}{O_{x1}} - \frac{O_{12}}{O_{x2}}}{\frac{O_{11}}{O_{x1}} + \frac{O_{12}}{O_{x2}}}
+Difference coefficient:
+    \text{Difference coefficient} = \frac{\frac{O_{11}}{O_{x1}} - \frac{O_{12}}{O_{x2}}}{\frac{O_{11}}{O_{x1}} + \frac{O_{12}}{O_{x2}}}
 
-Jaccard Index:
+Jaccard index:
     \text{J} = \frac{O_{11}}{O_{11} + O_{12} + O_{21}}
 
-Kilgarriff's Ratio:
-    \text{Kilgarriff's Ratio} = \frac{\frac{O_{11}}{O_{11} + O_{21}} \times 1000000 + \alpha}{\frac{O_{12}}{O_{12} + O_{22}} \times 1000000 + \alpha}
+Kilgarriff's ratio:
+    \text{Kilgarriff's ratio} = \frac{\frac{O_{11}}{O_{11} + O_{21}} \times 1000000 + \alpha}{\frac{O_{12}}{O_{12} + O_{22}} \times 1000000 + \alpha}
 
-Log Ratio:
-    \text{Log Ratio} = \log_{2} \frac{\frac{O_{11}}{O_{x1}}}{\frac{O_{12}}{O_{x2}}}
+logDice:
+    \text{logDice} = 14 + \log_{2} \frac{2 \times O_{11}}{O_{1x} + O_{x1}}
 
-Log-Frequency Biased MD:
+Log-frequency biased MD:
     \text{LFMD} = \log_{2} \frac{O_{11}}{E_{11}} + \log_{2} O_{11}
 
-logDice:
-    \text{logDice} = 14 + \log_{2} \frac{2 \times O_{11}}{O_{1x} + O_{x1}}
+Log Ratio:
+    \text{Log Ratio} = \log_{2} \frac{\frac{O_{11}}{O_{x1}}}{\frac{O_{12}}{O_{x2}}}
 
 MI.log-f:
     \text{MI.log-f} = \log_{2} \frac{{O_{11}}^2}{E_{11}} \times \ln (O_{11} + 1)
 
-Minimum Sensitivity:
+Minimum sensitivity:
     \text{S} = \min\left\{\frac{O_{11}}{O_{1x}},\;\frac{O_{11}}{O_{x1}}\right\}
 
 Mutual Dependency:
@@ -1509,63 +1513,67 @@ Mutual Dependency:
 Mutual Expectation:
     \text{ME} = O_{11} \times \frac{2 \times O_{11}}{O_{1x} + O_{x1}}
 
-Mutual Information:
+Mutual information:
     \text{MI} = \sum_{i = 1}^n \sum_{j = 1}^n \left(\frac{O_{ij}}{O_{xx}} \times \log_{2} \frac{O_{ij}}{E_{ij}}\right)
 
-Odds Ratio:
-    \text{Odd's Ratio} = \frac{O_{11} \times O_{22}}{O_{12} \times O_{21}}
+Odds ratio:
+    \text{Odds ratio} = \frac{O_{11} \times O_{22}}{O_{12} \times O_{21}}
 
-Pointwise Mutual Information:
+Pointwise mutual information:
     \text{PMI} = \log_{2} \frac{O_{11}}{E_{11}}
 
-Poisson Collocation Measure:
+Poisson collocation measure:
     \text{sig} = \frac{O_{11} \times (\ln O_{11} - \ln E_{11} - 1)}{\ln O_{xx}}
 
-Squared Phi Coefficient:
+Squared association ratio:
+    \text{IM}^2 = \log_{2} \frac{{O_{11}}^2}{E_{11}}
+
+Squared phi coefficient:
     \phi^2 = \frac{(O_{11} \times O_{22} - O_{12} \times O_{21})^2}{O_{1x} \times O_{2x} \times O_{x1} \times O_{x2}}
 -->
 
 Measure of Effect Size|Formula
 ----------------------|-------
-<span id="ref-pct-diff"></span>%DIFF<br>([Gabrielatos & Marchi, 2012](#ref-gabrielatos-marchi-2012))|![Formula](/doc/measures/effect_size/pct_diff.svg)
-<span id="ref-im3"></span>Cubic Association Ratio<br>([Daille, 1994](#ref-daille-1994), [1995](#ref-daille-1995))|![Formula](/doc/measures/effect_size/im3.svg)
-<span id="ref-dices-coeff"></span>Dice's Coefficient<br>([Smadja et al., 1996](#ref-smadja-et-al-1996))|![Formula](/doc/measures/effect_size/dices_coeff.svg)
-<span id="ref-diff-coeff"></span>Difference Coefficient<br>([Hofland & Johanson, 1982](#ref-hofland-johanson-1982); [Gabrielatos, 2018](#ref-gabrielatos-2018))|![Formula](/doc/measures/effect_size/diff_coeff.svg)
-<span id="ref-jaccard-index"></span>Jaccard Index<br>([Dunning, 1998](#ref-dunning-1998))|![Formula](/doc/measures/effect_size/jaccard_index.svg)
-<span id="ref-kilgarriffs-ratio"></span>Kilgarriff's Ratio<br>([Kilgarriff, 2009](#ref-kilgarriff-2009))|![Formula](/doc/measures/effect_size/kilgarriffs_ratio.svg)<br>where **α** is the smoothing parameter, whose value could be changed via **Menu Bar → Preferences → Settings → Measures → Effect Size → Kilgarriff's Ratio → Smoothing Parameter**.
-<span id="ref-log-ratio"></span>Log Ratio<br>([Hardie, 2014](#ref-hardie-2014))|![Formula](/doc/measures/effect_size/log_ratio.svg)
-<span id="ref-lfmd"></span>Log-Frequency Biased MD<br>([Thanopoulos et al., 2002](#ref-thanopoulos-et-al-2002))|![Formula](/doc/measures/effect_size/lfmd.svg)
+<span id="ref-pct-diff"></span>%DIFF<br>([Gabrielatos & Marchi, 2011](#ref-gabrielatos-marchi-2011))|![Formula](/doc/measures/effect_size/pct_diff.svg)
+<span id="ref-im3"></span>Cubic association ratio<br>([Daille, 1994](#ref-daille-1994))|![Formula](/doc/measures/effect_size/im3.svg)
+<span id="ref-dice-sorensen-coeff"></span>Dice-Sørensen coefficient<br>([Smadja et al., 1996](#ref-smadja-et-al-1996))|![Formula](/doc/measures/effect_size/dice_sorensen_coeff.svg)
+<span id="ref-diff-coeff"></span>Difference coefficient<br>([Hofland & Johansson, 1982](#ref-hofland-johansson-1982); [Gabrielatos, 2018](#ref-gabrielatos-2018))|![Formula](/doc/measures/effect_size/diff_coeff.svg)
+<span id="ref-jaccard-index"></span>Jaccard index<br>([Dunning, 1998](#ref-dunning-1998))|![Formula](/doc/measures/effect_size/jaccard_index.svg)
+<span id="ref-kilgarriffs-ratio"></span>Kilgarriff's ratio<br>([Kilgarriff, 2009](#ref-kilgarriff-2009))|![Formula](/doc/measures/effect_size/kilgarriffs_ratio.svg)<br>where **α** is the smoothing parameter, whose value could be changed via **Menu Bar → Preferences → Settings → Measures → Effect Size → Kilgarriff's Ratio → Smoothing Parameter**.
 <span id="ref-log-dice"></span>logDice<br>([Rychlý, 2008](#ref-rychly-2008))|![Formula](/doc/measures/effect_size/log_dice.svg)
-<span id="ref-mi-log-f"></span>MI.log-f<br>([Lexical Computing Ltd., 2015](#ref-lexical-computing-ltd-2015); [Kilgarriff & Tugwell, 2002](#ref-kilgarriff-tugwell-2002))|![Formula](/doc/measures/effect_size/mi_log_f.svg)
-<span id="ref-min-sensitivity"></span>Minimum Sensitivity<br>([Pedersen, 1998](#ref-pedersen-1998))|![Formula](/doc/measures/effect_size/min_sensitivity.svg)
+<span id="ref-lfmd"></span>Log-frequency biased MD<br>([Thanopoulos et al., 2002](#ref-thanopoulos-et-al-2002))|![Formula](/doc/measures/effect_size/lfmd.svg)
+<span id="ref-log-ratio"></span>Log Ratio<br>([Hardie, 2014](#ref-hardie-2014))|![Formula](/doc/measures/effect_size/log_ratio.svg)
+<span id="ref-mi-log-f"></span>MI.log-f<br>([Kilgarriff & Tugwell, 2002](#ref-kilgarriff-tugwell-2002); [Lexical Computing Ltd., 2015](#ref-lexical-computing-ltd-2015))|![Formula](/doc/measures/effect_size/mi_log_f.svg)
+<span id="ref-min-sensitivity"></span>Minimum sensitivity<br>([Pedersen, 1998](#ref-pedersen-1998))|![Formula](/doc/measures/effect_size/min_sensitivity.svg)
 <span id="ref-md"></span>Mutual Dependency<br>([Thanopoulos et al., 2002](#ref-thanopoulos-et-al-2002))|![Formula](/doc/measures/effect_size/md.svg)
 <span id="ref-me"></span>Mutual Expectation<br>([Dias et al., 1999](#ref-dias-et-al-1999))|![Formula](/doc/measures/effect_size/me.svg)
-<span id="ref-mi"></span>Mutual Information<br>([Dunning, 1998](#ref-dunning-1998))|![Formula](/doc/measures/effect_size/mi.svg)
-<span id="ref-odds-ratio"></span>Odds Ratio<br>([Pojanapunya & Todd, 2016](#ref-pojanapunya-todd-2016))|![Formula](/doc/measures/effect_size/odds_ratio.svg)
-<span id="ref-pmi"></span>Pointwise Mutual Information<br>([Church & Hanks, 1990](#ref-church-hanks-1990))|![Formula](/doc/measures/effect_size/pmi.svg)
-<span id="ref-poisson-collocation-measure"></span>Poisson Collocation Measure<br>([Quasthoff & Wolff, 2002](#ref-quasthoff-wolff-2002))|![Formula](/doc/measures/effect_size/poisson_collocation_measure.svg)
-<span id="ref-squared-phi-coeff"></span>Squared Phi Coefficient<br>([Church & Gale, 1991](#ref-church-gale-1991))|![Formula](/doc/measures/effect_size/squared_phi_coeff.svg)
+<span id="ref-mi"></span>Mutual information<br>([Dunning, 1998](#ref-dunning-1998))|![Formula](/doc/measures/effect_size/mi.svg)
+<span id="ref-odds-ratio"></span>Odds ratio<br>([Pojanapunya & Todd, 2016](#ref-pojanapunya-todd-2016))|![Formula](/doc/measures/effect_size/odds_ratio.svg)
+<span id="ref-pmi"></span>Pointwise mutual information<br>([Church & Hanks, 1990](#ref-church-hanks-1990))|![Formula](/doc/measures/effect_size/pmi.svg)
+<span id="ref-poisson-collocation-measure"></span>Poisson collocation measure<br>([Quasthoff & Wolff, 2002](#ref-quasthoff-wolff-2002))|![Formula](/doc/measures/effect_size/poisson_collocation_measure.svg)
+<span id="ref-im2"></span>Squared association ratio<br>([Daille, 1995](#ref-daille-1995))|![Formula](/doc/measures/effect_size/im2.svg)
+<span id="ref-squared-phi-coeff"></span>Squared phi coefficient<br>([Church & Gale, 1991](#ref-church-gale-1991))|![Formula](/doc/measures/effect_size/squared_phi_coeff.svg)
 
 <span id="doc-13"></span>
 ## [13 References](#doc)
 <span id="ref-al-heeti-1984"></span>
 1. [**^**](#ref-rd) Al-Heeti, K. N. (1984). *Judgment analysis technique applied to readability prediction of Arabic reading material* [Doctoral dissertation, University of Northern Colorado]. ProQuest Dissertations and Theses Global.
 <span id="ref-al-tamimi-et-al-2013"></span>
-1. [**^**](#ref-aari) Al-Tamimi, A., Jaradat M., Aljarrah, N., & Ghanim, S. (2013). AARI: Automatic Arabic readability index. *The International Arab Journal of Information Technology*, *11*(4), 370–378.
+1. [**^**](#ref-aari) Al-Tamimi, A., Jaradat M., Aljarrah, N., & Ghanim, S. (2013). AARI: Automatic Arabic Readability Index. *The International Arab Journal of Information Technology*, *11*(4), 370–378.
 <span id="ref-amstad-1978"></span>
 1. [**^**](#ref-re) Amstad, T. (1978). *Wie verständlich sind unsere Zeitungen?* [Unpublished doctoral dissertation]. University of Zurich.
 <span id="ref-anderson-1983"></span>
 1. [**^**](#ref-rix) Anderson, J. (1983). Lix and Rix: Variations on a little-known readability index. *Journal of Reading*, *26*(6), 490–496.
 <span id="ref-bamberger-vanecek-1984"></span>
-1. [**^**](#ref-num-word-types-bamberger-vanecek) [**^**](#ref-nwl) [**^**](#ref-nws) [**^**](#ref-smog-grade) Bamberger, R., & Vanecek, E. (1984). *Lesen-verstehen-lernen-schreiben: Die schwierigkeitsstufen von texten in deutscher sprache*. Jugend und Volk.
+1. [**^**](#ref-num-word-types-bamberger-vanecek) [**^**](#ref-nwl) [**^**](#ref-nws) [**^**](#ref-smog-grading) Bamberger, R., & Vanecek, E. (1984). *Lesen-verstehen-lernen-schreiben: Die schwierigkeitsstufen von texten in deutscher sprache*. Jugend und Volk.
 <span id="ref-berry-rogghe-1973"></span>
-1. [**^**](#ref-z-score-berry-rogghes) Berry-Rogghe, G. L. M. (1973). The computation of collocations and their relevance in lexical studies. In A. J. Aiken, R. W. Bailey, & N. Hamilton-Smith (Eds.), *The computer and literary studies* (pp. 103–112). Edinburgh University Press.
+1. [**^**](#ref-z-test-berry-rogghes) Berry-Rogghe, G. L. M. (1973). The computation of collocations and their relevance in lexical studies. In A. J. Aiken, R. W. Bailey, & N. Hamilton-Smith (Eds.), *The computer and literary studies* (pp. 103–112). Edinburgh University Press.
 <span id="ref-bormuth-1969"></span>
 1. [**^**](#ref-bormuths-cloze-mean-gp) Bormuth, J. R. (1969). *Development of readability analyses*. U.S. Department of Health, Education, and Welfare. http://files.eric.ed.gov/fulltext/ED029166.pdf
 <span id="ref-bjornsson-1968"></span>
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diff --git a/doc/measures/effect_size/dices_coeff.svg b/doc/measures/effect_size/dice_sorensen_coeff.svg
similarity index 100%
rename from doc/measures/effect_size/dices_coeff.svg
rename to doc/measures/effect_size/dice_sorensen_coeff.svg
diff --git a/doc/measures/effect_size/diff_coeff.svg b/doc/measures/effect_size/diff_coeff.svg
index daaceb5f1..a7657003f 100644
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similarity index 80%
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index 277341cb3..e38a34146 100644
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diff --git a/doc/measures/readability/lensear_write.svg b/doc/measures/readability/lensear_write_formula.svg
similarity index 100%
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diff --git a/doc/measures/readability/lorge_readability_index.svg b/doc/measures/readability/lorge_readability_index.svg
index 0dc170ba3..6fee9315a 100644
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diff --git a/doc/measures/readability/luong_nguyen_dinh_readability_formula.svg b/doc/measures/readability/luong_nguyen_dinhs_readability_formula.svg
similarity index 100%
rename from doc/measures/readability/luong_nguyen_dinh_readability_formula.svg
rename to doc/measures/readability/luong_nguyen_dinhs_readability_formula.svg
diff --git a/doc/measures/readability/smog_grade.svg b/doc/measures/readability/smog_grading.svg
similarity index 100%
rename from doc/measures/readability/smog_grade.svg
rename to doc/measures/readability/smog_grading.svg
diff --git a/doc/measures/readability/spache_grade_level.svg b/doc/measures/readability/spache_readability_formula.svg
similarity index 77%
rename from doc/measures/readability/spache_grade_level.svg
rename to doc/measures/readability/spache_readability_formula.svg
index b7d3cd45e..fa2a5c179 100644
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diff --git a/doc/measures/statistical_significance/z_score.svg b/doc/measures/statistical_significance/z_test.svg
similarity index 100%
rename from doc/measures/statistical_significance/z_score.svg
rename to doc/measures/statistical_significance/z_test.svg
diff --git a/doc/measures/statistical_significance/z_score_berry_rogghe.svg b/doc/measures/statistical_significance/z_test_berry_rogghe.svg
similarity index 100%
rename from doc/measures/statistical_significance/z_score_berry_rogghe.svg
rename to doc/measures/statistical_significance/z_test_berry_rogghe.svg
diff --git a/tests/tests_measures/test_measures_effect_size.py b/tests/tests_measures/test_measures_effect_size.py
index 0fff027b8..f29af43c1 100644
--- a/tests/tests_measures/test_measures_effect_size.py
+++ b/tests/tests_measures/test_measures_effect_size.py
@@ -63,9 +63,9 @@ def test_im3():
     assert_zeros(wl_measures_effect_size.im3)
 
 # Reference: Smadja, F., McKeown, K. R., & Hatzivassiloglou, V. (1996). Translating collocations for bilingual lexicons: A statistical approach. Computational Linguistics, 22(1), pp. 1–38. (p. 13)
-def test_dices_coeff():
+def test_dice_sorensen_coeff():
     numpy.testing.assert_array_equal(
-        numpy.round(wl_measures_effect_size.dices_coeff(
+        numpy.round(wl_measures_effect_size.dice_sorensen_coeff(
             main,
             numpy.array([130] * 2),
             numpy.array([3121 - 130] * 2),
@@ -75,7 +75,7 @@ def test_dices_coeff():
         numpy.array([0.08] * 2)
     )
 
-    assert_zeros(wl_measures_effect_size.dices_coeff)
+    assert_zeros(wl_measures_effect_size.dice_sorensen_coeff)
 
 # Reference: Hofland, K., & Johanson, S. (1982). Word frequencies in British and American English. Norwegian Computing Centre for the Humanities. (p. 471)
 def test_diff_coeff():
@@ -110,7 +110,13 @@ def test_kilgarriffs_ratio():
 
     assert_zeros(wl_measures_effect_size.kilgarriffs_ratio, result = 1)
 
-# Reference: Hardie, A. (2014, April 28). Log ratio: An informal introduction. ESRC Centre for Corpus Approaches to Social Science (CASS). http://cass.lancs.ac.uk/log-ratio-an-informal-introduction/.
+def test_log_dice():
+    assert_zeros(wl_measures_effect_size.log_dice, result = 14)
+
+def test_lfmd():
+    assert_zeros(wl_measures_effect_size.lfmd)
+
+# Reference: Hardie, A. (2014, April 28). Log Ratio: An informal introduction. ESRC Centre for Corpus Approaches to Social Science (CASS). http://cass.lancs.ac.uk/log-ratio-an-informal-introduction/.
 def test_log_ratio():
     numpy.testing.assert_array_equal(
         wl_measures_effect_size.log_ratio(
@@ -134,12 +140,6 @@ def test_log_ratio():
         numpy.array([float('-inf'), float('inf'), 0])
     )
 
-def test_lfmd():
-    assert_zeros(wl_measures_effect_size.lfmd)
-
-def test_log_dice():
-    assert_zeros(wl_measures_effect_size.log_dice, result = 14)
-
 def test_mi_log_f():
     assert_zeros(wl_measures_effect_size.mi_log_f)
 
@@ -164,7 +164,7 @@ def test_md():
 def test_me():
     assert_zeros(wl_measures_effect_size.me)
 
-# Reference: Dunning, T. E. (1998). Finding structure in text, genome and other symbolic sequences [Doctoral dissertation, University of Sheffield]. arXiv. arxiv.org/pdf/1207.1847.pdf (p. 51)
+# Reference: Dunning, T. E. (1998). Finding structure in text, genome and other symbolic sequences [Doctoral dissertation, University of Sheffield]. arXiv. https://arxiv.org/pdf/1207.1847 (p. 51)
 def test_mi():
     numpy.testing.assert_array_equal(
         numpy.round(wl_measures_effect_size.mi(
@@ -221,6 +221,9 @@ def test_pmi():
 def test_poisson_collocation_measure():
     assert_zeros(wl_measures_effect_size.poisson_collocation_measure)
 
+def test_im2():
+    assert_zeros(wl_measures_effect_size.im2)
+
 # Reference: Church, K. W., & Gale, W. A. (1991, September 29–October 1). Concordances for parallel text [Paper presentation]. Using Corpora: Seventh Annual Conference of the UW Centre for the New OED and Text Research, St. Catherine's College, Oxford, United Kingdom.
 def test_squared_phi_coeff():
     numpy.testing.assert_array_equal(
@@ -239,13 +242,13 @@ def test_squared_phi_coeff():
 if __name__ == '__main__':
     test_pct_diff()
     test_im3()
-    test_dices_coeff()
+    test_dice_sorensen_coeff()
     test_diff_coeff()
     test_jaccard_index()
     test_kilgarriffs_ratio()
-    test_log_ratio()
-    test_lfmd()
     test_log_dice()
+    test_lfmd()
+    test_log_ratio()
     test_mi_log_f()
     test_min_sensitivity()
     test_md()
@@ -254,4 +257,5 @@ def test_squared_phi_coeff():
     test_odds_ratio()
     test_pmi()
     test_poisson_collocation_measure()
+    test_im2()
     test_squared_phi_coeff()
diff --git a/tests/tests_measures/test_measures_readability.py b/tests/tests_measures/test_measures_readability.py
index 32dcf60b8..671bd5d1e 100644
--- a/tests/tests_measures/test_measures_readability.py
+++ b/tests/tests_measures/test_measures_readability.py
@@ -89,12 +89,6 @@ def test_rd():
     rd_ara_12_policy_2 = wl_measures_readability.rd(main, test_text_ara_12)
     rd_eng_12 = wl_measures_readability.rd(main, test_text_eng_12)
 
-    print("Al-Heeti's Readability Prediction Formula:")
-    print(f'\tara/0: {rd_ara_0}')
-    print(f'\tara/12-policy-1: {rd_ara_12_policy_1}')
-    print(f'\tara/12-policy-2: {rd_ara_12_policy_2}')
-    print(f'\teng/12: {rd_eng_12}')
-
     assert rd_ara_0 == 'text_too_short'
     assert rd_ara_12_policy_1 == 4.41434307 * (45 / 12) - 13.46873475
     assert rd_ara_12_policy_2 == 0.97569509 * (45 / 12) + 0.37237998 * (12 / 3) - 0.90451827 * (12 / 5) - 1.06000414
@@ -105,11 +99,6 @@ def test_aari():
     aari_ara_12 = wl_measures_readability.aari(main, test_text_ara_12)
     aari_eng_12 = wl_measures_readability.aari(main, test_text_eng_12)
 
-    print('Automated Arabic Readability Index:')
-    print(f'\tara/0: {aari_ara_0}')
-    print(f'\tara/12: {aari_ara_12}')
-    print(f'\teng/12: {aari_eng_12}')
-
     assert aari_ara_0 == 'text_too_short'
     assert aari_ara_12 == 3.28 * 46 + 1.43 * (46 / 12) + 1.24 * (12 / 3)
     assert aari_eng_12 == 'no_support'
@@ -122,12 +111,6 @@ def test_ari():
     ari_eng_12_navy = wl_measures_readability.ari(main, test_text_eng_12)
     ari_spa_12 = wl_measures_readability.ari(main, test_text_spa_12)
 
-    print('Automated Readability Index:')
-    print(f'\teng/0: {ari_eng_0}')
-    print(f'\teng/12: {ari_eng_12}')
-    print(f'\teng/12-navy: {ari_eng_12_navy}')
-    print(f'\tspa/12: {ari_spa_12}')
-
     assert ari_eng_0 == 'text_too_short'
     assert ari_eng_12 == 0.5 * (12 / 3) + 4.71 * (47 / 12) - 21.43
     assert ari_eng_12_navy == ari_spa_12 == 0.37 * (12 / 3) + 5.84 * (47 / 12) - 26.01
@@ -137,11 +120,6 @@ def test_bormuths_cloze_mean():
     m_eng_12 = wl_measures_readability.bormuths_cloze_mean(main, test_text_eng_12)
     m_other_12 = wl_measures_readability.bormuths_cloze_mean(main, test_text_other_12)
 
-    print("Bormuth's Cloze Mean:")
-    print(f'\teng/0: {m_eng_0}')
-    print(f'\teng/12: {m_eng_12}')
-    print(f'\tother/12: {m_other_12}')
-
     assert m_eng_0 == 'text_too_short'
     assert m_eng_12 == (
         0.886593 -
@@ -158,11 +136,6 @@ def test_bormuths_gp():
     gp_eng_12 = wl_measures_readability.bormuths_gp(main, test_text_eng_12)
     gp_other_12 = wl_measures_readability.bormuths_gp(main, test_text_other_12)
 
-    print("Bormuth's Grade Placement:")
-    print(f'\teng/0: {gp_eng_0}')
-    print(f'\teng/12: {gp_eng_12}')
-    print(f'\tother/12: {gp_other_12}')
-
     m = wl_measures_readability.bormuths_cloze_mean(main, test_text_eng_12)
     c = 0.35
 
@@ -179,11 +152,6 @@ def test_coleman_liau_index():
     grade_level_eng_12 = wl_measures_readability.coleman_liau_index(main, test_text_eng_12)
     grade_level_spa_12 = wl_measures_readability.coleman_liau_index(main, test_text_spa_12)
 
-    print('Coleman-Liau Index:')
-    print(f'\teng/0: {grade_level_eng_0}')
-    print(f'\teng/12: {grade_level_eng_12}')
-    print(f'\tspa/12: {grade_level_spa_12}')
-
     est_cloze_pct = 141.8401 - 0.21459 * (45 / 12 * 100) + 1.079812 * (3 / 12 * 100)
 
     assert grade_level_eng_0 == 'text_too_short'
@@ -202,15 +170,6 @@ def test_colemans_readability_formula():
     cloze_pct_tha_12 = wl_measures_readability.colemans_readability_formula(main, test_text_tha_12)
     cloze_pct_other_12 = wl_measures_readability.colemans_readability_formula(main, test_text_other_12)
 
-    print("Coleman's Readability Formula:")
-    print(f'\teng/0: {cloze_pct_eng_0}')
-    print(f'\teng/12-1: {cloze_pct_eng_12_1}')
-    print(f'\teng/12-2: {cloze_pct_eng_12_2}')
-    print(f'\teng/12-3: {cloze_pct_eng_12_3}')
-    print(f'\teng/12-4: {cloze_pct_eng_12_4}')
-    print(f'\ttha/12: {cloze_pct_tha_12}')
-    print(f'\tother/12: {cloze_pct_other_12}')
-
     assert cloze_pct_eng_0 == 'text_too_short'
     assert cloze_pct_eng_12_1 == 1.29 * (9 / 12 * 100) - 38.45
     assert cloze_pct_eng_12_2 == 1.16 * (9 / 12 * 100) + 1.48 * (3 / 12 * 100) - 37.95
@@ -219,6 +178,15 @@ def test_colemans_readability_formula():
     assert cloze_pct_tha_12 != 'no_support'
     assert cloze_pct_other_12 == 'no_support'
 
+def test_crawfords_readability_formula():
+    grade_level_spa_0 = wl_measures_readability.crawfords_readability_formula(main, test_text_spa_0)
+    grade_level_spa_12 = wl_measures_readability.crawfords_readability_formula(main, test_text_spa_12)
+    grade_level_eng_12 = wl_measures_readability.crawfords_readability_formula(main, test_text_eng_12)
+
+    assert grade_level_spa_0 == 'text_too_short'
+    assert grade_level_spa_12 == 3 / 12 * 100 * (-0.205) + 18 / 12 * 100 * 0.049 - 3.407
+    assert grade_level_eng_12 == 'no_support'
+
 def test_x_c50():
     x_c50_eng_0 = wl_measures_readability.x_c50(main, test_text_eng_0)
     settings['x_c50']['variant'] = 'Original'
@@ -229,13 +197,6 @@ def test_x_c50():
     x_c50_eng_12_new = wl_measures_readability.x_c50(main, test_text_eng_12)
     x_c50_spa_12 = wl_measures_readability.x_c50(main, test_text_spa_12)
 
-    print('Dale-Chall Readability Formula:')
-    print(f'\teng/0: {x_c50_eng_0}')
-    print(f'\teng/12-orig: {x_c50_eng_12_orig}')
-    print(f'\teng/12-psk: {x_c50_eng_12_psk}')
-    print(f'\teng/12-new: {x_c50_eng_12_new}')
-    print(f'\tspa/12: {x_c50_spa_12}')
-
     assert x_c50_eng_0 == 'text_too_short'
     assert x_c50_eng_12_orig == 0.1579 * (1 / 12 * 100) + 0.0496 * (12 / 3) + 3.6365
     assert x_c50_eng_12_psk == 3.2672 + 0.1155 * (1 / 12 * 100) + 0.0596 * (12 / 3)
@@ -250,12 +211,6 @@ def test_danielson_bryans_readability_formula():
     danielson_bryan_eng_12_2 = wl_measures_readability.danielson_bryans_readability_formula(main, test_text_eng_12)
     danielson_bryan_other_12 = wl_measures_readability.danielson_bryans_readability_formula(main, test_text_other_12)
 
-    print("Danielson-Bryan's Readability Formula:")
-    print(f'\teng/0: {danielson_bryan_eng_0}')
-    print(f'\teng/12-1: {danielson_bryan_eng_12_1}')
-    print(f'\teng/12-2: {danielson_bryan_eng_12_2}')
-    print(f'\tother/12: {danielson_bryan_other_12}')
-
     assert danielson_bryan_eng_0 == 'text_too_short'
     assert danielson_bryan_eng_12_1 == 1.0364 * (47 / (12 - 1)) + 0.0194 * (47 / 3) - 0.6059
     assert danielson_bryan_eng_12_2 == danielson_bryan_other_12 == 131.059 - 10.364 * (47 / (12 - 1)) - 0.194 * (47 / 3)
@@ -265,11 +220,6 @@ def test_dawoods_readability_formula():
     dawood_ara_12 = wl_measures_readability.dawoods_readability_formula(main, test_text_ara_12)
     dawood_eng_12 = wl_measures_readability.dawoods_readability_formula(main, test_text_eng_12)
 
-    print("Dawood's Readability Formula:")
-    print(f'\tara/0: {dawood_ara_0}')
-    print(f'\tara/12: {dawood_ara_12}')
-    print(f'\teng/12: {dawood_eng_12}')
-
     assert dawood_ara_0 == 'text_too_short'
     assert dawood_ara_12 == (-0.0533) * (45 / 12) - 0.2066 * (12 / 3) + 5.5543 * (12 / 5) - 1.0801
     assert dawood_eng_12 == 'no_support'
@@ -279,11 +229,6 @@ def test_drp():
     drp_eng_12 = wl_measures_readability.drp(main, test_text_eng_12)
     drp_other_12 = wl_measures_readability.drp(main, test_text_other_12)
 
-    print('Degrees of Reading Power:')
-    print(f'\teng/0: {drp_eng_0}')
-    print(f'\teng/12: {drp_eng_12}')
-    print(f'\tother/12: {drp_other_12}')
-
     assert drp_eng_0 == 'text_too_short'
     m = wl_measures_readability.bormuths_cloze_mean(main, test_text_eng_12)
     assert drp_eng_12 == 100 - math.floor(m * 100 + 0.5)
@@ -294,11 +239,6 @@ def test_devereux_readability_index():
     grade_placement_eng_12 = wl_measures_readability.devereux_readability_index(main, test_text_eng_12)
     grade_placement_spa_12 = wl_measures_readability.devereux_readability_index(main, test_text_spa_12)
 
-    print('Devereux Readability Index:')
-    print(f'\teng/0: {grade_placement_eng_0}')
-    print(f'\teng/12: {grade_placement_eng_12}')
-    print(f'\tspa/12: {grade_placement_spa_12}')
-
     assert grade_placement_eng_0 == 'text_too_short'
     assert grade_placement_eng_12 == 1.56 * (47 / 12) + 0.19 * (12 / 3) - 6.49
     assert grade_placement_spa_12 != 'text_too_short'
@@ -308,11 +248,6 @@ def test_dickes_steiwer_handformel():
     dickes_steiwer_eng_12 = wl_measures_readability.dickes_steiwer_handformel(main, test_text_eng_12)
     dickes_steiwer_spa_12 = wl_measures_readability.dickes_steiwer_handformel(main, test_text_spa_12)
 
-    print('Dickes-Steiwer Handformel:')
-    print(f'\teng/0: {dickes_steiwer_eng_0}')
-    print(f'\teng/12: {dickes_steiwer_eng_12}')
-    print(f'\tspa/12: {dickes_steiwer_spa_12}')
-
     assert dickes_steiwer_eng_0 == 'text_too_short'
     assert dickes_steiwer_eng_12 == 235.95993 - numpy.log(45 / 12 + 1) * 73.021 - numpy.log(12 / 3 + 1) * 12.56438 - 5 / 12 * 50.03293
     assert dickes_steiwer_spa_12 != 'text_too_short'
@@ -323,12 +258,6 @@ def test_elf():
     elf_spa_12 = wl_measures_readability.elf(main, test_text_spa_12)
     elf_other_12 = wl_measures_readability.elf(main, test_text_other_12)
 
-    print('Easy Listening Formula:')
-    print(f'\teng/0: {elf_eng_0}')
-    print(f'\teng/12: {elf_eng_12}')
-    print(f'\tspa/12: {elf_spa_12}')
-    print(f'\tother/12: {elf_other_12}')
-
     assert elf_eng_0 == 'text_too_short'
     assert elf_eng_12 == (15 - 12) / 3
     assert elf_spa_12 != 'no_support'
@@ -340,12 +269,6 @@ def test_gl():
     gl_spa_12 = wl_measures_readability.gl(main, test_text_spa_12)
     gl_other_12 = wl_measures_readability.gl(main, test_text_other_12)
 
-    print('Flesch-Kincaid Grade Level:')
-    print(f'\teng/0: {gl_eng_0}')
-    print(f'\teng/12: {gl_eng_12}')
-    print(f'\tspa/12: {gl_spa_12}')
-    print(f'\tother/12: {gl_other_12}')
-
     assert gl_eng_0 == 'text_too_short'
     assert gl_eng_12 == 0.39 * (12 / 3) + 11.8 * (15 / 12) - 15.59
     assert gl_spa_12 != 'no_support'
@@ -375,22 +298,6 @@ def test_re_flesch():
     flesch_re_afr_12 = wl_measures_readability.re_flesch(main, test_text_afr_12)
     flesch_re_other_12 = wl_measures_readability.re_flesch(main, test_text_other_12)
 
-    print('Flesch Reading Ease:')
-    print(f'\teng/0: {flesch_re_eng_0}')
-    print(f'\teng/12-psk: {flesch_re_eng_12_psk}')
-    print(f'\teng/12: {flesch_re_eng_12}')
-    print(f'\tnld/12-douma: {flesch_re_nld_12_douma}')
-    print(f'\tnld/12-brouwer: {flesch_re_nld_12_brouwer}')
-    print(f'\tfra/12: {flesch_re_fra_12}')
-    print(f'\tdeu/12: {flesch_re_deu_12}')
-    print(f'\tita/12: {flesch_re_ita_12}')
-    print(f'\trus/12: {flesch_re_rus_12}')
-    print(f'\tspa/12-fh: {flesch_re_spa_12_fh}')
-    print(f'\tspa/12-sp: {flesch_re_spa_12_sp}')
-    print(f'\tukr/12: {flesch_re_ukr_12}')
-    print(f'\tafr/12: {flesch_re_afr_12}')
-    print(f'\tother/12: {flesch_re_other_12}')
-
     assert flesch_re_eng_0 == 'text_too_short'
     assert flesch_re_eng_12_psk == -2.2029 + 4.55 * (15 / 12) + 0.0778 * (12 / 3)
     assert flesch_re_eng_12 == 206.835 - 84.6 * (15 / 12) - 1.015 * (12 / 3)
@@ -415,13 +322,6 @@ def test_re_farr_jenkins_paterson():
     re_farr_jenkins_paterson_spa_12 = wl_measures_readability.re_farr_jenkins_paterson(main, test_text_spa_12)
     re_farr_jenkins_paterson_other_12 = wl_measures_readability.re_farr_jenkins_paterson(main, test_text_other_12)
 
-    print('Flesch Reading Ease (Farr-Jenkins-Paterson):')
-    print(f'\teng/0: {re_farr_jenkins_paterson_eng_0}')
-    print(f'\teng/12: {re_farr_jenkins_paterson_eng_12}')
-    print(f'\teng/12-psk: {re_farr_jenkins_paterson_eng_12_psk}')
-    print(f'\tspa/12: {re_farr_jenkins_paterson_spa_12}')
-    print(f'\tother/12: {re_farr_jenkins_paterson_other_12}')
-
     assert re_farr_jenkins_paterson_eng_0 == 'text_too_short'
     assert re_farr_jenkins_paterson_eng_12 == 1.599 * (9 / 12 * 100) - 1.015 * (12 / 3) - 31.517
     assert re_farr_jenkins_paterson_eng_12_psk == 8.4335 - 0.0648 * (9 / 12 * 100) + 0.0923 * (12 / 3)
@@ -434,70 +334,25 @@ def test_rgl():
     rgl_spa_150 = wl_measures_readability.rgl(main, test_text_spa_150)
     rgl_other_12 = wl_measures_readability.rgl(main, test_text_other_12)
 
-    print('FORCAST Grade Level:')
-    print(f'\teng/12: {rgl_eng_12}')
-    print(f'\teng/150: {rgl_eng_150}')
-    print(f'\tspa/150: {rgl_spa_150}')
-    print(f'\tother/12: {rgl_other_12}')
-
     assert rgl_eng_12 == 'text_too_short'
     assert rgl_eng_150 == rgl_spa_150 == 20.43 - 0.11 * (6 * 18 + 4)
     assert rgl_other_12 == 'no_support'
 
-def test_cp():
-    cp_spa_0 = wl_measures_readability.cp(main, test_text_spa_0)
-    cp_spa_12 = wl_measures_readability.cp(main, test_text_spa_12)
-    cp_eng_12 = wl_measures_readability.cp(main, test_text_eng_12)
-
-    print('Fórmula de Comprensibilidad de Gutiérrez de Polini:')
-    print(f'\tspa/0: {cp_spa_0}')
-    print(f'\tspa/12: {cp_spa_12}')
-    print(f'\teng/12: {cp_eng_12}')
-
-    assert cp_spa_0 == 'text_too_short'
-    assert cp_spa_12 == 95.2 - 9.7 * (45 / 12) - 0.35 * (12 / 3)
-    assert cp_eng_12 == 'no_support'
-
-def test_formula_de_crawford():
-    grade_level_spa_0 = wl_measures_readability.formula_de_crawford(main, test_text_spa_0)
-    grade_level_spa_12 = wl_measures_readability.formula_de_crawford(main, test_text_spa_12)
-    grade_level_eng_12 = wl_measures_readability.formula_de_crawford(main, test_text_eng_12)
-
-    print('Fórmula de Crawford:')
-    print(f'\tspa/0: {grade_level_spa_0}')
-    print(f'\tspa/12: {grade_level_spa_12}')
-    print(f'\teng/12: {grade_level_eng_12}')
-
-    assert grade_level_spa_0 == 'text_too_short'
-    assert grade_level_spa_12 == 3 / 12 * 100 * (-0.205) + 18 / 12 * 100 * 0.049 - 3.407
-    assert grade_level_eng_12 == 'no_support'
-
 def test_fuckss_stilcharakteristik():
     stilcharakteristik_eng_0 = wl_measures_readability.fuckss_stilcharakteristik(main, test_text_eng_0)
     stilcharakteristik_eng_12 = wl_measures_readability.fuckss_stilcharakteristik(main, test_text_eng_12)
     stilcharakteristik_spa_12 = wl_measures_readability.fuckss_stilcharakteristik(main, test_text_spa_12)
     stilcharakteristik_other_12 = wl_measures_readability.fuckss_stilcharakteristik(main, test_text_other_12)
 
-    print("Fucks's Stilcharakteristik:")
-    print(f'\teng/0: {stilcharakteristik_eng_0}')
-    print(f'\teng/12: {stilcharakteristik_eng_12}')
-    print(f'\tspa/12: {stilcharakteristik_spa_12}')
-    print(f'\tother/12: {stilcharakteristik_other_12}')
-
     assert stilcharakteristik_eng_0 == 'text_too_short'
     assert stilcharakteristik_eng_12 == 15 / 3
     assert stilcharakteristik_spa_12 != 'no_support'
     assert stilcharakteristik_other_12 == 'no_support'
 
-def test_gulpease_index():
-    gulpease_index_ita_0 = wl_measures_readability.gulpease_index(main, test_text_ita_0)
-    gulpease_index_ita_12 = wl_measures_readability.gulpease_index(main, test_text_ita_12)
-    gulpease_index_eng_12 = wl_measures_readability.gulpease_index(main, test_text_eng_12)
-
-    print('Gulpease Index:')
-    print(f'\tita/0: {gulpease_index_ita_0}')
-    print(f'\tita/12: {gulpease_index_ita_12}')
-    print(f'\teng/12: {gulpease_index_eng_12}')
+def test_gulpease():
+    gulpease_index_ita_0 = wl_measures_readability.gulpease(main, test_text_ita_0)
+    gulpease_index_ita_12 = wl_measures_readability.gulpease(main, test_text_ita_12)
+    gulpease_index_eng_12 = wl_measures_readability.gulpease(main, test_text_eng_12)
 
     assert gulpease_index_ita_0 == 'text_too_short'
     assert gulpease_index_ita_12 == 89 + (300 * 3 - 10 * 45) / 12
@@ -513,44 +368,35 @@ def test_fog_index():
     fog_index_eng_12_navy = wl_measures_readability.fog_index(main, test_text_eng_12)
     fog_index_spa_12 = wl_measures_readability.fog_index(main, test_text_spa_12)
 
-    print('Gunning Fog Index:')
-    print(f'\teng/0: {fog_index_eng_0}')
-    print(f'\teng/12-orig: {fog_index_eng_12_propn_orig}')
-    print(f'\teng/12-psk: {fog_index_eng_12_pron_psk}')
-    print(f'\teng/12-navy: {fog_index_eng_12_navy}')
-    print(f'\tspa/12: {fog_index_spa_12}')
-
     assert fog_index_eng_0 == 'text_too_short'
     assert fog_index_eng_12_propn_orig == 0.4 * (12 / 3 + 1 / 12 * 100)
     assert fog_index_eng_12_pron_psk == 3.0680 + 0.0877 * (12 / 3) + 0.0984 * (1 / 12 * 100)
     assert fog_index_eng_12_navy == ((12 + 2 * 0) / 3 - 3) / 2
     assert fog_index_spa_12 == 'no_support'
 
+def test_cp():
+    cp_spa_0 = wl_measures_readability.cp(main, test_text_spa_0)
+    cp_spa_12 = wl_measures_readability.cp(main, test_text_spa_12)
+    cp_eng_12 = wl_measures_readability.cp(main, test_text_eng_12)
+
+    assert cp_spa_0 == 'text_too_short'
+    assert cp_spa_12 == 95.2 - 9.7 * (45 / 12) - 0.35 * (12 / 3)
+    assert cp_eng_12 == 'no_support'
+
 def test_mu():
     mu_spa_0 = wl_measures_readability.mu(main, test_text_spa_0)
     mu_spa_12 = wl_measures_readability.mu(main, test_text_spa_12)
     mu_eng_12 = wl_measures_readability.mu(main, test_text_eng_12)
 
-    print('Legibilidad µ:')
-    print(f'\tspa/0: {mu_spa_0}')
-    print(f'\tspa/12: {mu_spa_12}')
-    print(f'\teng/12: {mu_eng_12}')
-
     assert mu_spa_0 == 'text_too_short'
     assert mu_spa_12 == (12 / 11) * (3.75 / 7.1875) * 100
     assert mu_eng_12 == 'no_support'
 
-def test_lensear_write():
-    score_eng_0 = wl_measures_readability.lensear_write(main, test_text_eng_0)
-    score_eng_12 = wl_measures_readability.lensear_write(main, test_text_eng_12)
-    score_eng_100 = wl_measures_readability.lensear_write(main, test_text_eng_100)
-    score_spa_100 = wl_measures_readability.lensear_write(main, test_text_spa_100)
-
-    print('Lensear Write:')
-    print(f'\teng/0: {score_eng_0}')
-    print(f'\teng/12: {score_eng_12}')
-    print(f'\teng/100: {score_eng_100}')
-    print(f'\tspa/100: {score_spa_100}')
+def test_lensear_write_formula():
+    score_eng_0 = wl_measures_readability.lensear_write_formula(main, test_text_eng_0)
+    score_eng_12 = wl_measures_readability.lensear_write_formula(main, test_text_eng_12)
+    score_eng_100 = wl_measures_readability.lensear_write_formula(main, test_text_eng_100)
+    score_spa_100 = wl_measures_readability.lensear_write_formula(main, test_text_spa_100)
 
     assert score_eng_0 == 'text_too_short'
     assert score_eng_12 == 6 * (100 / 12) + 3 * 3 * (100 / 12)
@@ -562,11 +408,6 @@ def test_lix():
     lix_eng_12 = wl_measures_readability.lix(main, test_text_eng_12)
     lix_spa_12 = wl_measures_readability.lix(main, test_text_spa_12)
 
-    print('Lix:')
-    print(f'\teng/0: {lix_eng_0}')
-    print(f'\teng/12: {lix_eng_12}')
-    print(f'\tspa/12: {lix_spa_12}')
-
     assert lix_eng_0 == 'text_too_short'
     assert lix_eng_12 == 12 / 3 + 100 * (3 / 12)
     assert lix_spa_12 != 'no_support'
@@ -577,32 +418,18 @@ def test_lorge_readability_index():
     lorge_eng_12_corrected = wl_measures_readability.lorge_readability_index(main, test_text_eng_12_prep)
     settings['lorge_readability_index']['use_corrected_formula'] = False
     lorge_eng_12 = wl_measures_readability.lorge_readability_index(main, test_text_eng_12_prep)
-    lorge_tha_12 = wl_measures_readability.lorge_readability_index(main, test_text_tha_12)
-    lorge_other_12 = wl_measures_readability.lorge_readability_index(main, test_text_other_12)
-
-    print('Lorge Readability Index:')
-    print(f'\teng/0: {lorge_eng_0}')
-    print(f'\teng/12-corrected: {lorge_eng_12_corrected}')
-    print(f'\teng/12: {lorge_eng_12}')
-    print(f'\ttha/12: {lorge_tha_12}')
-    print(f'\tother/12: {lorge_other_12}')
+    lorge_spa_12 = wl_measures_readability.lorge_readability_index(main, test_text_spa_12)
 
     assert lorge_eng_0 == 'text_too_short'
     assert lorge_eng_12_corrected == 12 / 3 * 0.06 + 2 / 12 * 0.1 + 2 / 12 * 0.1 + 1.99
     assert lorge_eng_12 == 12 / 3 * 0.07 + 2 / 12 * 13.01 + 2 / 12 * 10.73 + 1.6126
-    assert lorge_tha_12 != 'no_support'
-    assert lorge_other_12 == 'no_support'
+    assert lorge_spa_12 == 'no_support'
 
 def test_luong_nguyen_dinhs_readability_formula():
     readability_vie_0 = wl_measures_readability.luong_nguyen_dinhs_readability_formula(main, test_text_vie_0)
     readability_vie_12 = wl_measures_readability.luong_nguyen_dinhs_readability_formula(main, test_text_vie_12)
     readability_eng_12 = wl_measures_readability.luong_nguyen_dinhs_readability_formula(main, test_text_eng_12)
 
-    print("Luong-Nguyen-Dinh's Readability Formula:")
-    print(f'\tvie/0: {readability_vie_0}')
-    print(f'\tvie/12: {readability_vie_12}')
-    print(f'\teng/12: {readability_eng_12}')
-
     assert readability_vie_0 == 'text_too_short'
     assert readability_vie_12 == 0.004 * (46 / 3) + 0.1905 * (46 / 12) + 2.7147 * 12 / 12 - 0.7295
     assert readability_eng_12 == 'no_support'
@@ -612,11 +439,6 @@ def test_eflaw():
     eflaw_eng_12 = wl_measures_readability.eflaw(main, test_text_eng_12)
     eflaw_spa_12 = wl_measures_readability.eflaw(main, test_text_spa_12)
 
-    print('McAlpine EFLAW Readability Score:')
-    print(f'\teng/0: {eflaw_eng_0}')
-    print(f'\teng/12: {eflaw_eng_12}')
-    print(f'\tspa/12: {eflaw_spa_12}')
-
     assert eflaw_eng_0 == 'text_too_short'
     assert eflaw_eng_12 == (12 + 6) / 3
     assert eflaw_spa_12 == 'no_support'
@@ -631,13 +453,6 @@ def test_nwl():
     nwl_deu_12_3 = wl_measures_readability.nwl(main, test_text_deu_12)
     nwl_eng_12 = wl_measures_readability.nwl(main, test_text_eng_12)
 
-    print('neue Wiener Literaturformeln:')
-    print(f'\tdeu/0: {nwl_deu_0}')
-    print(f'\tdeu/12-1: {nwl_deu_12_1}')
-    print(f'\tdeu/12-2: {nwl_deu_12_2}')
-    print(f'\tdeu/12-3: {nwl_deu_12_3}')
-    print(f'\teng/12: {nwl_eng_12}')
-
     sw = 5 / 5 * 100
     s_100 = 3 / 12 * 100
     ms = 0 / 12 * 100
@@ -660,13 +475,6 @@ def test_nws():
     nws_deu_12_3 = wl_measures_readability.nws(main, test_text_deu_12)
     nws_eng_12 = wl_measures_readability.nws(main, test_text_eng_12)
 
-    print('neue Wiener Sachtextformel:')
-    print(f'\tdeu/0: {nws_deu_0}')
-    print(f'\tdeu/12-1: {nws_deu_12_1}')
-    print(f'\tdeu/12-2: {nws_deu_12_2}')
-    print(f'\tdeu/12-3: {nws_deu_12_3}')
-    print(f'\teng/12: {nws_eng_12}')
-
     ms = 0 / 12 * 100
     sl = 12 / 3
     iw = 3 / 12 * 100
@@ -691,12 +499,6 @@ def test_osman():
     osman_ara_faseeh = wl_measures_readability.osman(main, test_text_ara_faseeh)
     osman_eng_12 = wl_measures_readability.osman(main, test_text_eng_12)
 
-    print('OSMAN:')
-    print(f'\tara/0: {osman_ara_0}')
-    print(f'\tara/12: {osman_ara_12}')
-    print(f'\tara/faseeh: {osman_ara_faseeh}')
-    print(f'\teng/12: {osman_eng_12}')
-
     assert osman_ara_0 == 'text_too_short'
     assert osman_ara_12 == 200.791 - 1.015 * (12 / 3) - 24.181 * ((3 + 26 + 3 + 0) / 12)
     assert osman_ara_faseeh == 200.791 - 1.015 * (1 / 1) - 24.181 * ((0 + 5 + 1 + 1) / 1)
@@ -707,28 +509,16 @@ def test_rix():
     rix_eng_12 = wl_measures_readability.rix(main, test_text_eng_12)
     rix_spa_12 = wl_measures_readability.rix(main, test_text_spa_12)
 
-    print('Rix:')
-    print(f'\teng/0: {rix_eng_0}')
-    print(f'\teng/12: {rix_eng_12}')
-    print(f'\tspa/12: {rix_spa_12}')
-
     assert rix_eng_0 == 'text_too_short'
     assert rix_eng_12 == rix_spa_12 == 3 / 3
 
-def test_smog_grade():
-    g_eng_12 = wl_measures_readability.smog_grade(main, test_text_eng_12)
-    g_eng_120 = wl_measures_readability.smog_grade(main, test_text_eng_120)
-    g_eng_120 = wl_measures_readability.smog_grade(main, test_text_eng_120)
-    g_deu_120 = wl_measures_readability.smog_grade(main, test_text_deu_120)
-    g_spa_120 = wl_measures_readability.smog_grade(main, test_text_spa_120)
-    g_other_12 = wl_measures_readability.smog_grade(main, test_text_other_12)
-
-    print('SMOG Grade:')
-    print(f'\teng/12: {g_eng_12}')
-    print(f'\teng/120: {g_eng_120}')
-    print(f'\tdeu/120: {g_deu_120}')
-    print(f'\tspa/120: {g_spa_120}')
-    print(f'\tother/12: {g_other_12}')
+def test_smog_grading():
+    g_eng_12 = wl_measures_readability.smog_grading(main, test_text_eng_12)
+    g_eng_120 = wl_measures_readability.smog_grading(main, test_text_eng_120)
+    g_eng_120 = wl_measures_readability.smog_grading(main, test_text_eng_120)
+    g_deu_120 = wl_measures_readability.smog_grading(main, test_text_deu_120)
+    g_spa_120 = wl_measures_readability.smog_grading(main, test_text_spa_120)
+    g_other_12 = wl_measures_readability.smog_grading(main, test_text_other_12)
 
     assert g_eng_12 == 'text_too_short'
     assert g_eng_120 == 3.1291 + 1.043 * numpy.sqrt(15)
@@ -736,19 +526,13 @@ def test_smog_grade():
     assert g_spa_120 != 'no_support'
     assert g_other_12 == 'no_support'
 
-def test_spache_grade_lvl():
-    grade_lvl_eng_12 = wl_measures_readability.spache_grade_lvl(main, test_text_eng_12)
-    settings['spache_grade_lvl']['use_rev_formula'] = True
-    grade_lvl_eng_100_rev = wl_measures_readability.spache_grade_lvl(main, test_text_eng_100)
-    settings['spache_grade_lvl']['use_rev_formula'] = False
-    grade_lvl_eng_100 = wl_measures_readability.spache_grade_lvl(main, test_text_eng_100)
-    grade_lvl_spa_100 = wl_measures_readability.spache_grade_lvl(main, test_text_spa_100)
-
-    print('Spache Grade Level:')
-    print(f'\teng/12: {grade_lvl_eng_12}')
-    print(f'\teng/100-rev: {grade_lvl_eng_100_rev}')
-    print(f'\teng/100: {grade_lvl_eng_100}')
-    print(f'\tspa/100: {grade_lvl_spa_100}')
+def test_spache_readability_formula():
+    grade_lvl_eng_12 = wl_measures_readability.spache_readability_formula(main, test_text_eng_12)
+    settings['spache_readability_formula']['use_rev_formula'] = True
+    grade_lvl_eng_100_rev = wl_measures_readability.spache_readability_formula(main, test_text_eng_100)
+    settings['spache_readability_formula']['use_rev_formula'] = False
+    grade_lvl_eng_100 = wl_measures_readability.spache_readability_formula(main, test_text_eng_100)
+    grade_lvl_spa_100 = wl_measures_readability.spache_readability_formula(main, test_text_spa_100)
 
     assert grade_lvl_eng_12 == 'text_too_short'
     assert grade_lvl_eng_100_rev == numpy.mean([0.121 * (100 / 25) + 0.082 * 25 + 0.659] * 3)
@@ -761,12 +545,6 @@ def test_strain_index():
     strain_index_spa_12 = wl_measures_readability.strain_index(main, test_text_spa_12)
     strain_index_other_12 = wl_measures_readability.strain_index(main, test_text_other_12)
 
-    print('Strain Index:')
-    print(f'\teng/0: {strain_index_eng_0}')
-    print(f'\teng/12: {strain_index_eng_12}')
-    print(f'\tspa/12: {strain_index_spa_12}')
-    print(f'\tother/12: {strain_index_other_12}')
-
     assert strain_index_eng_0 == 'text_too_short'
     assert strain_index_eng_12 == 15 / 10
     assert strain_index_spa_12 != 'no_support'
@@ -781,13 +559,6 @@ def test_trankle_bailers_readability_formula():
     trankle_bailers_tha_100 = wl_measures_readability.trankle_bailers_readability_formula(main, test_text_tha_100)
     trankle_bailers_other_100 = wl_measures_readability.trankle_bailers_readability_formula(main, test_text_other_100)
 
-    print("Tränkle & Bailer's Readability Formula:")
-    print(f'\teng/0: {trankle_bailers_eng_0}')
-    print(f'\teng/100-prep: {trankle_bailers_eng_100_prep_1}')
-    print(f'\teng/100-conj: {trankle_bailers_eng_100_conj_2}')
-    print(f'\ttha/100: {trankle_bailers_tha_100}')
-    print(f'\tother/100: {trankle_bailers_other_100}')
-
     assert trankle_bailers_eng_0 == 'text_too_short'
     assert trankle_bailers_eng_100_prep_1 == 224.6814 - numpy.log(372 / 100 + 1) * 79.8304 - numpy.log(100 / 25 + 1) * 12.24032 - 1 * 1.292857
     assert trankle_bailers_eng_100_conj_2 == 234.1063 - numpy.log(374 / 100 + 1) * 96.11069 - 0 * 2.05444 - 1 * 1.02805
@@ -800,12 +571,6 @@ def test_td():
     td_spa_12 = wl_measures_readability.td(main, test_text_spa_12)
     td_other_12 = wl_measures_readability.td(main, test_text_other_12)
 
-    print("Tuldava's Text Difficulty:")
-    print(f'\teng/0: {td_eng_0}')
-    print(f'\teng/12: {td_eng_12}')
-    print(f'\tspa/12: {td_spa_12}')
-    print(f'\tother/12: {td_other_12}')
-
     assert td_eng_0 == 'text_too_short'
     assert td_eng_12 == (15 / 12) * numpy.log(12 / 3)
     assert td_spa_12 != 'no_support'
@@ -817,12 +582,6 @@ def test_wheeler_smiths_readability_formula():
     wheeler_smith_spa_12 = wl_measures_readability.wheeler_smiths_readability_formula(main, test_text_spa_12)
     wheeler_smith_other_12 = wl_measures_readability.wheeler_smiths_readability_formula(main, test_text_other_12)
 
-    print("Wheeler & Smith's Readability Formula:")
-    print(f'\teng/0: {wheeler_smith_eng_0}')
-    print(f'\teng/12: {wheeler_smith_eng_12}')
-    print(f'\tspa/12: {wheeler_smith_spa_12}')
-    print(f'\tother/12: {wheeler_smith_other_12}')
-
     assert wheeler_smith_eng_0 == 'text_too_short'
     assert wheeler_smith_eng_12 == (12 / 4) * (3 / 12) * 10
     assert wheeler_smith_spa_12 != 'no_support'
@@ -836,6 +595,7 @@ def test_wheeler_smiths_readability_formula():
     test_bormuths_gp()
     test_coleman_liau_index()
     test_colemans_readability_formula()
+    test_crawfords_readability_formula()
     test_x_c50()
     test_danielson_bryans_readability_formula()
     test_dawoods_readability_formula()
@@ -847,13 +607,12 @@ def test_wheeler_smiths_readability_formula():
     test_re_flesch()
     test_re_farr_jenkins_paterson()
     test_rgl()
-    test_cp()
-    test_formula_de_crawford()
     test_fuckss_stilcharakteristik()
-    test_gulpease_index()
+    test_gulpease()
     test_fog_index()
+    test_cp()
     test_mu()
-    test_lensear_write()
+    test_lensear_write_formula()
     test_lix()
     test_lorge_readability_index()
     test_luong_nguyen_dinhs_readability_formula()
@@ -863,8 +622,8 @@ def test_wheeler_smiths_readability_formula():
     test__get_num_syls_ara()
     test_osman()
     test_rix()
-    test_smog_grade()
-    test_spache_grade_lvl()
+    test_smog_grading()
+    test_spache_readability_formula()
     test_strain_index()
     test_trankle_bailers_readability_formula()
     test_td()
diff --git a/tests/tests_measures/test_measures_statistical_significance.py b/tests/tests_measures/test_measures_statistical_significance.py
index 77bf7878f..d9d925cfa 100644
--- a/tests/tests_measures/test_measures_statistical_significance.py
+++ b/tests/tests_measures/test_measures_statistical_significance.py
@@ -263,22 +263,22 @@ def test_students_t_test_2_sample():
     numpy.testing.assert_array_equal(t_stats, numpy.array([0] * 2))
     numpy.testing.assert_array_equal(p_vals, numpy.array([1] * 2))
 
-def test__z_score_p_val():
+def test__z_test_p_val():
     numpy.testing.assert_array_equal(
-        wl_measures_statistical_significance._z_score_p_val(numpy.array([0] * 2), 'Two-tailed'),
+        wl_measures_statistical_significance._z_test_p_val(numpy.array([0] * 2), 'Two-tailed'),
         numpy.array([1] * 2)
     )
     numpy.testing.assert_array_equal(
-        wl_measures_statistical_significance._z_score_p_val(numpy.array([0] * 2), 'Left-tailed'),
+        wl_measures_statistical_significance._z_test_p_val(numpy.array([0] * 2), 'Left-tailed'),
         numpy.array([0] * 2)
     )
     numpy.testing.assert_array_equal(
-        wl_measures_statistical_significance._z_score_p_val(numpy.array([0] * 2), 'Right-tailed'),
+        wl_measures_statistical_significance._z_test_p_val(numpy.array([0] * 2), 'Right-tailed'),
         numpy.array([0] * 2)
     )
 
-def test_z_score():
-    z_scores, p_vals = wl_measures_statistical_significance.z_score(
+def test_z_test():
+    z_scores, p_vals = wl_measures_statistical_significance.z_test(
         main,
         numpy.array([0] * 2),
         numpy.array([0] * 2),
@@ -289,8 +289,8 @@ def test_z_score():
     numpy.testing.assert_array_equal(z_scores, numpy.array([0] * 2))
     numpy.testing.assert_array_equal(p_vals, numpy.array([1] * 2))
 
-def test_z_score_berry_rogghe():
-    z_scores, p_vals = wl_measures_statistical_significance.z_score_berry_rogghe(
+def test_z_test_berry_rogghe():
+    z_scores, p_vals = wl_measures_statistical_significance.z_test_berry_rogghe(
         main,
         numpy.array([0] * 2),
         numpy.array([0] * 2),
@@ -314,6 +314,6 @@ def test_z_score_berry_rogghe():
     test_students_t_test_1_sample()
     test_students_t_test_2_sample()
 
-    test__z_score_p_val()
-    test_z_score()
-    test_z_score_berry_rogghe()
+    test__z_test_p_val()
+    test_z_test()
+    test_z_test_berry_rogghe()
diff --git a/wordless/wl_colligation_extractor.py b/wordless/wl_colligation_extractor.py
index 7fedc7986..c61cb65c2 100644
--- a/wordless/wl_colligation_extractor.py
+++ b/wordless/wl_colligation_extractor.py
@@ -1176,7 +1176,7 @@ def run(self):
                         test_stats = [None] * num_colligations_all
                         p_vals = [None] * num_colligations_all
                     else:
-                        if test_statistical_significance == 'z_score_berry_rogghe':
+                        if test_statistical_significance == 'z_test_berry_rogghe':
                             test_stats, p_vals = func_statistical_significance(self.main, o11s, o12s, o21s, o22s, span)
                         else:
                             test_stats, p_vals = func_statistical_significance(self.main, o11s, o12s, o21s, o22s)
diff --git a/wordless/wl_collocation_extractor.py b/wordless/wl_collocation_extractor.py
index fa4859be0..4c13aff35 100644
--- a/wordless/wl_collocation_extractor.py
+++ b/wordless/wl_collocation_extractor.py
@@ -1174,7 +1174,7 @@ def run(self):
                         test_stats = [None] * num_collocations_all
                         p_vals = [None] * num_collocations_all
                     else:
-                        if test_statistical_significance == 'z_score_berry_rogghe':
+                        if test_statistical_significance == 'z_test_berry_rogghe':
                             test_stats, p_vals = func_statistical_significance(self.main, o11s, o12s, o21s, o22s, span)
                         else:
                             test_stats, p_vals = func_statistical_significance(self.main, o11s, o12s, o21s, o22s)
diff --git a/wordless/wl_measures/wl_measures_adjusted_freq.py b/wordless/wl_measures/wl_measures_adjusted_freq.py
index f7f020a1d..eff4fabd4 100644
--- a/wordless/wl_measures/wl_measures_adjusted_freq.py
+++ b/wordless/wl_measures/wl_measures_adjusted_freq.py
@@ -27,7 +27,7 @@
 C = -scipy.special.digamma(1)
 
 # Reference: Savický, P., & Hlaváčová, J. (2002). Measures of word commonness. Journal of Quantitative Linguistics, 9(3), 215–231. https://doi.org/10.1076/jqul.9.3.215.14124
-# Average Logarithmic Distance
+# Average logarithmic distance
 def fald(main, tokens, search_term):
     dists = wl_measures_dispersion._get_dists(tokens, search_term)
 
@@ -39,11 +39,11 @@ def fald(main, tokens, search_term):
 
     return fald
 
-# Average Reduced Frequency
+# Average reduced frequency
 def farf(main, tokens, search_term):
     return wl_measures_dispersion.arf(main, tokens, search_term)
 
-# Average Waiting Time
+# Average waiting time
 def fawt(main, tokens, search_term):
     dists = wl_measures_dispersion._get_dists(tokens, search_term)
 
diff --git a/wordless/wl_measures/wl_measures_bayes_factor.py b/wordless/wl_measures/wl_measures_bayes_factor.py
index cef94b15c..d144fb1d6 100644
--- a/wordless/wl_measures/wl_measures_bayes_factor.py
+++ b/wordless/wl_measures/wl_measures_bayes_factor.py
@@ -20,8 +20,8 @@
 
 from wordless.wl_measures import wl_measures_statistical_significance, wl_measure_utils
 
-# Log-likelihood Ratio
-# Reference: Wilson, A. (2013). Embracing Bayes Factors for key item analysis in corpus linguistics. In M. Bieswanger & A. Koll-Stobbe (Eds.), New Approaches to the Study of Linguistic Variability (pp. 3–11). Peter Lang.
+# Log-likelihood ratio test
+# Reference: Wilson, A. (2013). Embracing Bayes factors for key item analysis in corpus linguistics. In M. Bieswanger & A. Koll-Stobbe (Eds.), New approaches to the study of linguistic variability (pp. 3–11). Peter Lang.
 def bayes_factor_log_likelihood_ratio_test(main, o11s, o12s, o21s, o22s):
     oxxs = o11s + o12s + o21s + o22s
 
@@ -38,7 +38,7 @@ def bayes_factor_log_likelihood_ratio_test(main, o11s, o12s, o21s, o22s):
     return bics
 
 # Student's t-test (2-sample)
-# Reference: Wilson, A. (2013). Embracing Bayes Factors for key item analysis in corpus linguistics. In M. Bieswanger & A. Koll-Stobbe (Eds.), New Approaches to the Study of Linguistic Variability (pp. 3–11). Peter Lang.
+# Reference: Wilson, A. (2013). Embracing Bayes factors for key item analysis in corpus linguistics. In M. Bieswanger & A. Koll-Stobbe (Eds.), New approaches to the study of linguistic variability (pp. 3–11). Peter Lang.
 def bayes_factor_students_t_test_2_sample(main, freqs_x1s, freqs_x2s):
 
     # Modify settings temporarily
diff --git a/wordless/wl_measures/wl_measures_dispersion.py b/wordless/wl_measures/wl_measures_dispersion.py
index 19283922f..5a2572e92 100644
--- a/wordless/wl_measures/wl_measures_dispersion.py
+++ b/wordless/wl_measures/wl_measures_dispersion.py
@@ -36,7 +36,7 @@ def _get_dists(tokens, search_term):
 
     return dists
 
-# Average Logarithmic Distance
+# Average logarithmic distance
 def ald(main, tokens, search_term):
     dists = _get_dists(tokens, search_term)
 
@@ -47,7 +47,7 @@ def ald(main, tokens, search_term):
 
     return ald
 
-# Average Reduced Frequency
+# Average reduced frequency
 def arf(main, tokens, search_term):
     dists = _get_dists(tokens, search_term)
 
@@ -59,7 +59,7 @@ def arf(main, tokens, search_term):
 
     return arf
 
-# Average Waiting Time
+# Average waiting time
 def awt(main, tokens, search_term):
     dists = _get_dists(tokens, search_term)
 
@@ -121,7 +121,7 @@ def juillands_d(main, freqs):
     return max(0, d)
 
 # Lyne's D₃
-# Reference: Lyne, A. A. (1985). Dispersion. In The vocabulary of French business correspondence: Word frequencies, collocations, and problems of lexicometric method (pp. 101–124). Slatkine/Champion.
+# Reference: Lyne, A. A. (1985). Dispersion. In A. A. Lyne (Ed.), The vocabulary of French business correspondence: Word frequencies, collocations, and problems of lexicometric method (pp. 101–124). Slatkine.
 def lynes_d3(main, freqs):
     freqs = numpy.array(freqs)
 
@@ -146,7 +146,7 @@ def rosengrens_s(main, freqs):
     return s
 
 # Zhang's Distributional Consistency
-# Reference: Zhang, H., Huang, C., & Yu, S. (2004). Distributional consistency: As a general method for defining a core lexicon. In M. T. Lino, M. F. Xavier, F. Ferreira, R. Costa, & R. Silva (Eds.), Proceedings of Fourth International Conference on Language Resources and Evaluation (pp. 1119–1122). European Language Resources Association.
+# Reference: Zhang, H., Huang, C., & Yu, S. (2004). Distributional Consistency: As a general method for defining a core lexicon. In M. T. Lino, M. F. Xavier, F. Ferreira, R. Costa, & R. Silva (Eds.), Proceedings of Fourth International Conference on Language Resources and Evaluation (pp. 1119–1122). European Language Resources Association.
 def zhangs_distributional_consistency(main, freqs):
     freqs = numpy.array(freqs)
 
diff --git a/wordless/wl_measures/wl_measures_effect_size.py b/wordless/wl_measures/wl_measures_effect_size.py
index daf10b37e..5073da287 100644
--- a/wordless/wl_measures/wl_measures_effect_size.py
+++ b/wordless/wl_measures/wl_measures_effect_size.py
@@ -23,7 +23,7 @@
 from wordless.wl_measures import wl_measures_statistical_significance, wl_measure_utils
 
 # %DIFF
-# Reference: Gabrielatos, C., & Marchi, A. (2012, September 13–14). Keyness: Appropriate metrics and practical issues [Conference session]. CADS International Conference 2012, University of Bologna, Italy.
+# Reference: Gabrielatos, C., & Marchi, A. (2011, November 5). Keyness: Matching metrics to definitions [Conference session]. Corpus Linguistics in the South 1, University of Portsmouth, United Kingdom. https://eprints.lancs.ac.uk/id/eprint/51449/4/Gabrielatos_Marchi_Keyness.pdf
 def pct_diff(main, o11s, o12s, o21s, o22s):
     _, _, ox1s, ox2s = wl_measures_statistical_significance.get_freqs_marginal(o11s, o12s, o21s, o22s)
 
@@ -40,23 +40,21 @@ def pct_diff(main, o11s, o12s, o21s, o22s):
         )
     )
 
-# Cubic Association Ratio
-# References:
-#     Daille, B. (1994). Approche mixte pour l'extraction automatique de terminologie: statistiques lexicales et filtres linguistiques [Doctoral thesis, Paris Diderot University]. Béatrice Daille. http://www.bdaille.com/index.php?option=com_docman&task=doc_download&gid=8&Itemid=
-#     Daille, B. (1995). Combined approach for terminology extraction: Lexical statistics and linguistic filtering. UCREL technical papers (Vol. 5). Lancaster University.
+# Cubic association ratio
+# Reference: Daille, B. (1994). Approche mixte pour l'extraction automatique de terminologie: statistiques lexicales et filtres linguistiques [Doctoral thesis, Paris Diderot University]. Béatrice Daille. http://www.bdaille.com/index.php?option=com_docman&task=doc_download&gid=8&Itemid=
 def im3(main, o11s, o12s, o21s, o22s):
     e11s, _, _, _ = wl_measures_statistical_significance.get_freqs_expected(o11s, o12s, o21s, o22s)
 
     return wl_measure_utils.numpy_log2(wl_measure_utils.numpy_divide(o11s ** 3, e11s))
 
-# Dice's Coefficient
+# Dice-Sørensen coefficient
 # Reference: Smadja, F., McKeown, K. R., & Hatzivassiloglou, V. (1996). Translating collocations for bilingual lexicons: A statistical approach. Computational Linguistics, 22(1), 1–38.
-def dices_coeff(main, o11s, o12s, o21s, o22s):
+def dice_sorensen_coeff(main, o11s, o12s, o21s, o22s):
     o1xs, _, ox1s, _ = wl_measures_statistical_significance.get_freqs_marginal(o11s, o12s, o21s, o22s)
 
     return wl_measure_utils.numpy_divide(2 * o11s, o1xs + ox1s)
 
-# Difference Coefficient
+# Difference coefficient
 # References:
 #     Hofland, K., & Johanson, S. (1982). Word frequencies in British and American English. Norwegian Computing Centre for the Humanities.
 #     Gabrielatos, C. (2018). Keyness analysis: Nature, metrics and techniques. In C. Taylor & A. Marchi (Eds.), Corpus approaches to discourse: A critical review (pp. 225–258). Routledge.
@@ -72,12 +70,12 @@ def diff_coeff(main, o11s, o12s, o21s, o22s):
         0
     )
 
-# Jaccard Index
-# Reference: Dunning, T. E. (1998). Finding structure in text, genome and other symbolic sequences [Doctoral dissertation, University of Sheffield]. arXiv. arxiv.org/pdf/1207.1847.pdf
+# Jaccard index
+# Reference: Dunning, T. E. (1998). Finding structure in text, genome and other symbolic sequences [Doctoral dissertation, University of Sheffield]. arXiv. https://arxiv.org/pdf/1207.1847
 def jaccard_index(main, o11s, o12s, o21s, o22s):
     return wl_measure_utils.numpy_divide(o11s, o11s + o12s + o21s)
 
-# Kilgarriff's Ratio
+# Kilgarriff's ratio
 # Reference: Kilgarriff, A. (2009). Simple maths for keywords. In M. Mahlberg, V. González-Díaz, & C. Smith (Eds.), Proceedings of the Corpus Linguistics Conference 2009 (p. 171). University of Liverpool.
 def kilgarriffs_ratio(main, o11s, o12s, o21s, o22s):
     smoothing_param = main.settings_custom['measures']['effect_size']['kilgarriffs_ratio']['smoothing_param']
@@ -87,8 +85,22 @@ def kilgarriffs_ratio(main, o11s, o12s, o21s, o22s):
         wl_measure_utils.numpy_divide(o12s, o12s + o22s) * 1000000 + smoothing_param
     )
 
+# logDice
+# Reference: Rychlý, P. (2008). A lexicographyer-friendly association score. In P. Sojka & A. Horák (Eds.), Proceedings of Second Workshop on Recent Advances in Slavonic Natural Languages Processing. Masaryk University
+def log_dice(main, o11s, o12s, o21s, o22s):
+    o1xs, _, ox1s, _ = wl_measures_statistical_significance.get_freqs_marginal(o11s, o12s, o21s, o22s)
+
+    return wl_measure_utils.numpy_log2(wl_measure_utils.numpy_divide(2 * o11s, o1xs + ox1s), default = 14)
+
+# Log-frequency biased MD
+# Reference: Thanopoulos, A., Fakotakis, N., & Kokkinakis, G. (2002). Comparative evaluation of collocation extraction metrics. In M. G. González & C. P. S. Araujo (Eds.), Proceedings of the Third International Conference on Language Resources and Evaluation (pp. 620–625). European Language Resources Association.
+def lfmd(main, o11s, o12s, o21s, o22s):
+    e11s, _, _, _ = wl_measures_statistical_significance.get_freqs_expected(o11s, o12s, o21s, o22s)
+
+    return wl_measure_utils.numpy_log2(wl_measure_utils.numpy_divide(o11s ** 2, e11s)) + wl_measure_utils.numpy_log2(o11s)
+
 # Log Ratio
-# Reference: Hardie, A. (2014, April 28). Log ratio: An informal introduction. ESRC Centre for Corpus Approaches to Social Science (CASS). http://cass.lancs.ac.uk/log-ratio-an-informal-introduction/
+# Reference: Hardie, A. (2014, April 28). Log Ratio: An informal introduction. ESRC Centre for Corpus Approaches to Social Science (CASS). http://cass.lancs.ac.uk/log-ratio-an-informal-introduction/
 def log_ratio(main, o11s, o12s, o21s, o22s):
     _, _, ox1s, ox2s = wl_measures_statistical_significance.get_freqs_marginal(o11s, o12s, o21s, o22s)
 
@@ -107,30 +119,16 @@ def log_ratio(main, o11s, o12s, o21s, o22s):
         )
     )
 
-# Log-Frequency Biased MD
-# Reference: Thanopoulos, A., Fakotakis, N., & Kokkinakis, G. (2002). Comparative evaluation of collocation extraction metrics. In M. G. González & C. P. S. Araujo (Eds.), Proceedings of the Third International Conference on Language Resources and Evaluation (pp. 620–625). European Language Resources Association.
-def lfmd(main, o11s, o12s, o21s, o22s):
-    e11s, _, _, _ = wl_measures_statistical_significance.get_freqs_expected(o11s, o12s, o21s, o22s)
-
-    return wl_measure_utils.numpy_log2(wl_measure_utils.numpy_divide(o11s ** 2, e11s)) + wl_measure_utils.numpy_log2(o11s)
-
-# logDice
-# Reference: Rychlý, P. (2008). A lexicographyer-friendly association score. In P. Sojka & A. Horák (Eds.), Proceedings of Second Workshop on Recent Advances in Slavonic Natural Languages Processing. Masaryk University
-def log_dice(main, o11s, o12s, o21s, o22s):
-    o1xs, _, ox1s, _ = wl_measures_statistical_significance.get_freqs_marginal(o11s, o12s, o21s, o22s)
-
-    return wl_measure_utils.numpy_log2(wl_measure_utils.numpy_divide(2 * o11s, o1xs + ox1s), default = 14)
-
 # MI.log-f
 # References:
-#     Lexical Computing. (2015, July 8). Statistics used in Sketch Engine. Sketch Engine. https://www.sketchengine.eu/documentation/statistics-used-in-sketch-engine/
 #     Kilgarriff, A., & Tugwell, D. (2002). WASP-bench: An MT lexicographers' workstation supporting state-of-the-art lexical disambiguation. In Proceedings of the 8th Machine Translation Summit (pp. 187–190). European Association for Machine Translation.
+#     Lexical Computing. (2015, July 8). Statistics used in Sketch Engine. Sketch Engine. https://www.sketchengine.eu/documentation/statistics-used-in-sketch-engine/
 def mi_log_f(main, o11s, o12s, o21s, o22s):
     e11s, _, _, _ = wl_measures_statistical_significance.get_freqs_expected(o11s, o12s, o21s, o22s)
 
     return wl_measure_utils.numpy_log2(wl_measure_utils.numpy_divide(o11s ** 2, e11s)) * wl_measure_utils.numpy_log(o11s + 1)
 
-# Minimum Sensitivity
+# Minimum sensitivity
 # Reference: Pedersen, T. (1998). Dependent bigram identification. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (p. 1197). AAAI Press.
 def min_sensitivity(main, o11s, o12s, o21s, o22s):
     o1xs, _, ox1s, _ = wl_measures_statistical_significance.get_freqs_marginal(o11s, o12s, o21s, o22s)
@@ -154,8 +152,8 @@ def me(main, o11s, o12s, o21s, o22s):
 
     return o11s * wl_measure_utils.numpy_divide(2 * o11s, o1xs + ox1s)
 
-# Mutual Information
-# Reference: Dunning, T. E. (1998). Finding structure in text, genome and other symbolic sequences [Doctoral dissertation, University of Sheffield]. arXiv. arxiv.org/pdf/1207.1847.pdf
+# Mutual information
+# Reference: Dunning, T. E. (1998). Finding structure in text, genome and other symbolic sequences [Doctoral dissertation, University of Sheffield]. arXiv. https://arxiv.org/pdf/1207.1847
 def mi(main, o11s, o12s, o21s, o22s):
     oxxs = o11s + o12s + o21s + o22s
     e11s, e12s, e21s, e22s = wl_measures_statistical_significance.get_freqs_expected(o11s, o12s, o21s, o22s)
@@ -167,7 +165,7 @@ def mi(main, o11s, o12s, o21s, o22s):
 
     return mi_11 + mi_12 + mi_21 + mi_22
 
-# Odds Ratio
+# Odds ratio
 # Reference: Pojanapunya, P., & Todd, R. W. (2016). Log-likelihood and odds ratio keyness statistics for different purposes of keyword analysis. Corpus Linguistics and Linguistic Theory, 15(1), 133–167. https://doi.org/10.1515/cllt-2015-0030
 def odds_ratio(main, o11s, o12s, o21s, o22s):
     return numpy.where(
@@ -183,14 +181,14 @@ def odds_ratio(main, o11s, o12s, o21s, o22s):
         )
     )
 
-# Pointwise Mutual Information
+# Pointwise mutual information
 # Reference: Church, K. W., & Hanks, P. (1990). Word association norms, mutual information, and lexicography. Computational Linguistics, 16(1), 22–29.
 def pmi(main, o11s, o12s, o21s, o22s):
     e11s, _, _, _ = wl_measures_statistical_significance.get_freqs_expected(o11s, o12s, o21s, o22s)
 
     return wl_measure_utils.numpy_log2(wl_measure_utils.numpy_divide(o11s, e11s))
 
-# Poisson Collocation Measure
+# Poisson collocation measure
 # Reference: Quasthoff, U., & Wolff, C. (2002). The poisson collocation measure and its applications. Proceedings of 2nd International Workshop on Computational Approaches to Collocations. IEEE.
 def poisson_collocation_measure(main, o11s, o12s, o21s, o22s):
     oxxs = o11s + o12s + o21s + o22s
@@ -201,7 +199,14 @@ def poisson_collocation_measure(main, o11s, o12s, o21s, o22s):
         wl_measure_utils.numpy_log(oxxs)
     )
 
-# Squared Phi Coefficient
+# Squared association ratio
+# Reference: Daille, B. (1995). Combined approach for terminology extraction: Lexical statistics and linguistic filtering. UCREL technical papers (Vol. 5). Lancaster University.
+def im2(main, o11s, o12s, o21s, o22s):
+    e11s, _, _, _ = wl_measures_statistical_significance.get_freqs_expected(o11s, o12s, o21s, o22s)
+
+    return wl_measure_utils.numpy_log2(wl_measure_utils.numpy_divide(o11s ** 2, e11s))
+
+# Squared phi coefficient
 # Reference: Church, K. W., & Gale, W. A. (1991, September 29–October 1). Concordances for parallel text [Paper presentation]. Using Corpora: Seventh Annual Conference of the UW Centre for the New OED and Text Research, St. Catherine's College, Oxford, United Kingdom.
 def squared_phi_coeff(main, o11s, o12s, o21s, o22s):
     o1xs, o2xs, ox1s, ox2s = wl_measures_statistical_significance.get_freqs_marginal(o11s, o12s, o21s, o22s)
diff --git a/wordless/wl_measures/wl_measures_lexical_density_diversity.py b/wordless/wl_measures/wl_measures_lexical_density_diversity.py
index 7ca417647..e0491c5cb 100644
--- a/wordless/wl_measures/wl_measures_lexical_density_diversity.py
+++ b/wordless/wl_measures/wl_measures_lexical_density_diversity.py
@@ -29,7 +29,7 @@
 
 _tr = QCoreApplication.translate
 
-# Brunét's Index
+# Brunét's index
 # References:
 #     Brunét, E. (1978). Le vocabulaire de Jean Giraudoux: Structure et evolution. Slatkine.
 #     Bucks, R. S., Singh, S., Cuerden, J. M., & Wilcock, G. K. (2000). Analysis of spontaneous, conversational speech in dementia of Alzheimer type: Evaluation of an objective technique for analysing lexical performance. Aphasiology, 14(1), 71–91. https://doi.org/10.1080/026870300401603
@@ -66,7 +66,7 @@ def fishers_index_of_diversity(main, text):
 
     return alpha
 
-# Herdan's Vₘ
+# Herdan's vₘ
 # Reference: Herdan, G. (1955). A new derivation and interpretation of Yule's ‘Characteristic’ K. Zeitschrift für Angewandte Mathematik und Physik (ZAMP), 6(4), 332–339. https://doi.org/10.1007/BF01587632
 def herdans_vm(main, text):
     types_freqs = collections.Counter(text.get_tokens_flat())
@@ -99,7 +99,7 @@ def hdd(main, text):
 
     return sum(ttrs)
 
-# Honoré's Statistic
+# Honoré's statistic
 # References:
 #     Honoré, A. (1979). Some simple measures of richness of vocabulary. Association of Literary and Linguistic Computing Bulletin, 7(2), 172–177.
 #     Bucks, R. S., Singh, S., Cuerden, J. M., & Wilcock, G. K. (2000). Analysis of spontaneous, conversational speech in dementia of Alzheimer type: Evaluation of an objective technique for analysing lexical performance. Aphasiology, 14(1), 71–91. https://doi.org/10.1080/026870300401603
@@ -114,8 +114,8 @@ def honores_stat(main, text):
 
     return r
 
-# Lexical Density
-# Reference: Halliday, M. A. K. (1989). Spoken and written language (2nd ed., p. 64).
+# Lexical density
+# Reference: Halliday, M. A. K. (1989). Spoken and written language (2nd ed., p. 64). Oxford University Press.
 def lexical_density(main, text):
     if text.lang in main.settings_global['pos_taggers']:
         wl_pos_tagging.wl_pos_tag_universal(main, text.get_tokens_flat(), lang = text.lang, tagged = text.tagged)
@@ -164,7 +164,7 @@ def logttr(main, text):
 
     return logttr
 
-# Mean Segmental TTR
+# Mean segmental TTR
 # References:
 #     Johnson, W. (1944). Studies in language behavior: I. a program of research. Psychological Monographs, 56(2), 1–15. https://doi.org/10.1037/h0093508
 #     McCarthy, P. M. (2005). An assessment of the range and usefulness of lexical diversity measures and the potential of the measure of textual, lexical diversity (MTLD) [Doctoral dissertation, The University of Memphis] (p. 37). ProQuest Dissertations and Theses Global.
@@ -185,7 +185,7 @@ def msttr(main, text):
 
     return msttr
 
-# Measure of Textual Lexical Diversity
+# Measure of textual lexical diversity
 # References:
 #     McCarthy, P. M. (2005). An assessment of the range and usefulness of lexical diversity measures and the potential of the measure of textual, lexical diversity (MTLD) [Doctoral dissertation, The University of Memphis] (pp. 95–96, 99–100). ProQuest Dissertations and Theses Global.
 #     McCarthy, P. M., & Jarvis, S. (2010). MTLD, vocd-D, and HD-D: A validation study of sophisticated approaches to lexical diversity assessment. Behavior Research Methods, 42(2), 381–392. https://doi.org/10.3758/BRM.42.2.381
@@ -388,7 +388,7 @@ def popescus_r4(main, text):
 
     return r4
 
-# Repeat Rate
+# Repeat rate
 # Reference: Popescu, I.-I. (2009). Word frequency studies (p. 166). Mouton de Gruyter.
 def repeat_rate(main, text):
     use_data = main.settings_custom['measures']['lexical_density_diversity']['repeat_rate']['use_data']
@@ -408,12 +408,12 @@ def repeat_rate(main, text):
 
 # Root TTR
 # References:
-#     Guiraud, P. (1954). Les caractères statistiques du vocabulaire: Essai de méthodologie. Presses universitaires de France.
+#     Guiraud, P. (1954). Les caractères statistiques du vocabulaire: Essai de méthodologie. Presses Universitaires de France.
 #     Malvern, D., Richards, B., Chipere, N., & Durán, P. (2004). Lexical diversity and language development: Quantification and assessment (p. 26). Palgrave Macmillan.
 def rttr(main, text):
     return text.num_types / numpy.sqrt(text.num_tokens)
 
-# Shannon Entropy
+# Shannon entropy
 # Reference: Popescu, I.-I. (2009). Word frequency studies (p. 173). Mouton de Gruyter.
 def shannon_entropy(main, text):
     use_data = main.settings_custom['measures']['lexical_density_diversity']['shannon_entropy']['use_data']
@@ -432,7 +432,7 @@ def shannon_entropy(main, text):
     return h
 
 # Simpson's l
-# Reference: Simpson, E. H. (1949). Measurement of diversity. Nature, 163, p. 688. https://doi.org/10.1038/163688a0
+# Reference: Simpson, E. H. (1949). Measurement of diversity. Nature, 163, 688. https://doi.org/10.1038/163688a0
 def simpsons_l(main, text):
     types_freqs = collections.Counter(text.get_tokens_flat())
     freqs_nums_types = collections.Counter(types_freqs.values())
@@ -444,7 +444,7 @@ def simpsons_l(main, text):
 
     return l
 
-# Type-token Ratio
+# Type-token ratio
 # Reference: Johnson, W. (1944). Studies in language behavior: I. a program of research. Psychological Monographs, 56(2), 1–15. https://doi.org/10.1037/h0093508
 def ttr(main, text):
     return text.num_types / text.num_tokens
@@ -479,7 +479,7 @@ def ttr(n, d):
 
     return popt[0]
 
-# Yule's Characteristic K
+# Yule's characteristic K
 # Reference: Yule, G. U. (1944). The statistical study of literary vocabulary (pp. 52–53). Cambridge University Press.
 def yules_characteristic_k(main, text):
     types_freqs = collections.Counter(text.get_tokens_flat())
diff --git a/wordless/wl_measures/wl_measures_readability.py b/wordless/wl_measures/wl_measures_readability.py
index df6af4392..d5faaf697 100644
--- a/wordless/wl_measures/wl_measures_readability.py
+++ b/wordless/wl_measures/wl_measures_readability.py
@@ -182,7 +182,7 @@ def get_num_sentences_sample(text, sample, sample_start):
         + 1
     )
 
-# Al-Heeti's Readability Prediction Formula
+# Al-Heeti's readability formula
 # Reference: Al-Heeti, K. N. (1984). Judgment analysis technique applied to readability prediction of Arabic reading material [Doctoral dissertation, University of Northern Colorado] (pp. 102, 104, 106). ProQuest Dissertations and Theses Global.
 def rd(main, text):
     if text.lang == 'ara':
@@ -256,7 +256,7 @@ def ari(main, text):
 
     return ari
 
-# Bormuth's Cloze Mean & Grade Placement
+# Bormuth's cloze mean & grade placement
 # Reference: Bormuth, J. R. (1969). Development of readability analyses (pp. 152, 160). U.S. Department of Health, Education, and Welfare. http://files.eric.ed.gov/fulltext/ED029166.pdf
 def bormuths_cloze_mean(main, text):
     if text.lang.startswith('eng_'):
@@ -294,7 +294,7 @@ def bormuths_gp(main, text):
 
     return gp
 
-# Coleman-Liau Index
+# Coleman-Liau index
 # Reference: Coleman, M., & Liau, T. L. (1975). A computer readability formula designed for machine scoring. Journal of Applied Psychology, 60(2), 283–284. https://doi.org/10.1037/h0076540
 def coleman_liau_index(main, text):
     text = get_nums(main, text)
@@ -311,7 +311,7 @@ def coleman_liau_index(main, text):
 
     return grade_level
 
-# Coleman's Readability Formula
+# Coleman's readability formula
 # Reference: Liau, T. L., Bassin, C. B., Martin, C. J., & Coleman, E. B. (1976). Modification of the Coleman readability formulas. Journal of Reading Behavior, 8(4), 381–386. https://journals.sagepub.com/doi/pdf/10.1080/10862967609547193
 def colemans_readability_formula(main, text):
     variant = main.settings_custom['measures']['readability']['colemans_readability_formula']['variant']
@@ -374,7 +374,26 @@ def colemans_readability_formula(main, text):
 
     return cloze_pct
 
-# Dale-Chall Readability Formula
+# Crawford's readability formula
+# Reference: Crawford, A. N. (1985). Fórmula y gráfico para determinar la comprensibilidad de textos de nivel primario en castellano. Lectura y Vida, 6(4). http://www.lecturayvida.fahce.unlp.edu.ar/numeros/a6n4/06_04_Crawford.pdf
+def crawfords_readability_formula(main, text):
+    if text.lang == 'spa' and text.lang in main.settings_global['syl_tokenizers']:
+        text = get_nums(main, text)
+
+        if text.num_words:
+            grade_level = (
+                text.num_sentences / text.num_words * 100 * (-0.205)
+                + text.num_syls / text.num_words * 100 * 0.049
+                - 3.407
+            )
+        else:
+            grade_level = 'text_too_short'
+    else:
+        grade_level = 'no_support'
+
+    return grade_level
+
+# Dale-Chall readability formula
 # References:
 #     Dale, E., & Chall, J. S. (1948a). A formula for predicting readability. Educational Research Bulletin, 27(1), 11–20, 28.
 #     Dale, E., & Chall, J. S. (1948b). A formula for predicting readability: Instructions. Educational Research Bulletin, 27(2), 37–54.
@@ -382,7 +401,7 @@ def colemans_readability_formula(main, text):
 #     Powers, R. D., Sumner, W. A., & Kearl, B. E. (1958). A recalculation of four adult readability formulas. Journal of Educational Psychology, 49(2), 99–105. https://doi.org/10.1037/h0043254
 # New:
 #     Chall, J. S., & Dale, E. (1995). Readability revisited: The new Dale-Chall readability formula. Brookline Books.
-#     清川英男. (1996). CHALL, J. S. and DALE, E.(1995) Readability Revisited: The New Dale-Chall Readability Formula. Brookline Books. 教育メディア研究, 3(1), 59. https://www.jstage.jst.go.jp/article/jaems/3/1/3_KJ00009004543/_pdf
+#     清川英男. (1996). CHALL, J. S. and DALE, E.(1995) Readability revisited: The new Dale-Chall readability formula. Brookline Books. 教育メディア研究, 3(1), 59. https://www.jstage.jst.go.jp/article/jaems/3/1/3_KJ00009004543/_pdf
 def x_c50(main, text):
     if text.lang.startswith('eng_'):
         text = get_nums(main, text)
@@ -416,7 +435,7 @@ def x_c50(main, text):
 
     return x_c50
 
-# Danielson-Bryan's Readability Formula
+# Danielson-Bryan's readability formula
 # Reference: Danielson, W. A., & Bryan, S. D. (1963). Computer automation of two readability formulas. Journalism Quarterly, 40(2), 201–206. https://doi.org/10.1177/107769906304000207
 def danielson_bryans_readability_formula(main, text):
     text = get_nums(main, text)
@@ -441,10 +460,10 @@ def danielson_bryans_readability_formula(main, text):
 
     return danielson_bryan
 
-# Dawood's Readability Formula
+# Dawood's readability formula
 # References:
 #     Dawood, B.A.K. (1977). The relationship between readability and selected language variables [Unpublished master’s thesis]. University of Baghdad.
-#     Cavalli-Sforza, V., Saddiki, H., & Nassiri, N. (2018). Arabic readability research—Current state and future directions. Procedia Computer Science, 142, 38–49.
+#     Cavalli-Sforza, V., Saddiki, H., & Nassiri, N. (2018). Arabic readability research: Current state and future directions. Procedia Computer Science, 142, 38–49.
 def dawoods_readability_formula(main, text):
     if text.lang == 'ara':
         text = get_nums(main, text)
@@ -527,7 +546,7 @@ def elf(main, text):
 
     return elf
 
-# Flesch-Kincaid Grade Level
+# Flesch-Kincaid grade level
 # Reference: Kincaid, J. P., Fishburne, R. P., Rogers, R. L., & Chissom, B. S. (1975). Derivation of new readability formulas (automated readability index, fog count, and Flesch reading ease formula) for Navy enlisted personnel (Report No. RBR 8-75, p. 14). Naval Air Station Memphis. https://apps.dtic.mil/sti/pdfs/ADA006655.pdf
 def gl(main, text):
     if text.lang in main.settings_global['syl_tokenizers']:
@@ -546,18 +565,18 @@ def gl(main, text):
 
     return gl
 
-# Flesch Reading Ease
+# Flesch reading ease
 # References:
 #     Flesch, R. (1948). A new readability yardstick. Journal of Applied Psychology, 32(3), 221–233. https://doi.org/10.1037/h0057532
 # Powers-Sumner-Kearl:
 #     Powers, R. D., Sumner, W. A., & Kearl, B. E. (1958). A recalculation of four adult readability formulas. Journal of Educational Psychology, 49(2), 99–105. https://doi.org/10.1037/h0043254
 # Dutch (Douma):
-#     Douma, W. H. (1960). De leesbaarheid van landbouwbladen: Een onderzoek naar en een toepassing van leesbaarheidsformules [Readability of Dutch farm papers: A discussion and application of readability-formulas] (p. 453). Afdeling sociologie en sociografie van de Landbouwhogeschool Wageningen. https://edepot.wur.nl/276323
+#     Douma, W. H. (1960). De leesbaarheid van landbouwbladen: Een onderzoek naar en een toepassing van leesbaarheidsformules [Readability of Dutch farm papers: A discussion and application of readability-formulas] (p. 453). Afdeling Sociologie en Sociografie van de Landbouwhogeschool Wageningen. https://edepot.wur.nl/276323
 # Dutch (Brouwer's Leesindex A):
-#     Brouwer, R. H. M. (1963). Onderzoek naar de leesmoeilijkheid van Nederlands proza. Paedagogische studiën, 40, 454–464. https://objects.library.uu.nl/reader/index.php?obj=1874-205260&lan=en
+#     Brouwer, R. H. M. (1963). Onderzoek naar de leesmoeilijkheid van Nederlands proza. Paedagogische Studiën, 40, 454–464. https://objects.library.uu.nl/reader/index.php?obj=1874-205260&lan=en
 # French:
-#     Kandel, L., & Moles A. (1958). Application de l’indice de flesch la langue francaise [applying flesch index to french language]. The Journal of Educational Research, 21, 283–287.
-#     Kopient, A., & Grabar, N. (2020). Rated lexicon for the simplification of medical texts. In B.  Gersbeck-Schierholz (ed.), HEALTHINFO 2020: The fifth international conference on informatics and assistive technologies for health-care, medical support and wellbeing (pp. 11–17). IARIA. https://hal.science/hal-03095275/document
+#     Kandel, L., & Moles, A. (1958). Application de l’indice de flesch à la langue française. The Journal of Educational Research, 21, 283–287.
+#     Sitbon, L., Bellot, P., & Blache, P. (2007). Eléments pour adapter les systèmes de recherche d’information aux dyslexiques. Revue TAL : traitement automatique des langues, 48(2), 123–147.
 # German:
 #     Amstad, T. (1978). Wie verständlich sind unsere Zeitungen? [Unpublished doctoral dissertation]. University of Zurich.
 #     Bamberger, R., & Vanecek, E. (1984). Lesen-verstehen-lernen-schreiben: Die schwierigkeitsstufen von texten in deutscher sprache (p. 56). Jugend und Volk.
@@ -656,7 +675,7 @@ def re_flesch(main, text):
 
     return re
 
-# Flesch Reading Ease (Farr-Jenkins-Paterson)
+# Flesch reading ease (Farr-Jenkins-Paterson)
 # References:
 #     Farr, J. N., Jenkins, J. J., & Paterson, D. G. (1951). Simplification of Flesch reading ease formula. Journal of Applied Psychology, 35(5), 333–337. https://doi.org/10.1037/h0062427
 # Powers-Sumner-Kearl:
@@ -687,7 +706,7 @@ def re_farr_jenkins_paterson(main, text):
 
     return re
 
-# FORCAST Grade Level
+# FORCAST
 # Reference: Caylor, J. S., & Sticht, T. G. (1973). Development of a simple readability index for job reading material (p. 3). Human Resource Research Organization. https://ia902703.us.archive.org/31/items/ERIC_ED076707/ERIC_ED076707.pdf
 def rgl(main, text):
     if text.lang in main.settings_global['syl_tokenizers']:
@@ -706,49 +725,9 @@ def rgl(main, text):
 
     return rgl
 
-# Fórmula de Comprensibilidad de Gutiérrez de Polini
-# References:
-#     Gutiérrez de Polini, L. E. (1972). Investigación sobre lectura en Venezuela [Paper presentation]. Primeras Jornadas de Educación Primaria, Ministerio de Educación, Caracas, Venezuela.
-#     Rodríguez Trujillo, N. (1980). Determinación de la comprensibilidad de materiales de lectura por medio de variables lingüísticas. Lectura y Vida, 1(1). http://www.lecturayvida.fahce.unlp.edu.ar/numeros/a1n1/01_01_Rodriguez.pdf
-def cp(main, text):
-    if text.lang == 'spa':
-        text = get_nums(main, text)
-
-        if text.num_words and text.num_sentences:
-            cp = (
-                95.2
-                - 9.7 * (text.num_chars_alpha / text.num_words)
-                - 0.35 * (text.num_words / text.num_sentences)
-            )
-        else:
-            cp = 'text_too_short'
-    else:
-        cp = 'no_support'
-
-    return cp
-
-# Fórmula de Crawford
-# Reference: Crawford, A. N. (1985). Fórmula y gráfico para determinar la comprensibilidad de textos de nivel primario en castellano. Lectura y Vida, 6(4). http://www.lecturayvida.fahce.unlp.edu.ar/numeros/a6n4/06_04_Crawford.pdf
-def formula_de_crawford(main, text):
-    if text.lang == 'spa' and text.lang in main.settings_global['syl_tokenizers']:
-        text = get_nums(main, text)
-
-        if text.num_words:
-            grade_level = (
-                text.num_sentences / text.num_words * 100 * (-0.205)
-                + text.num_syls / text.num_words * 100 * 0.049
-                - 3.407
-            )
-        else:
-            grade_level = 'text_too_short'
-    else:
-        grade_level = 'no_support'
-
-    return grade_level
-
 # Fucks's Stilcharakteristik
 # References:
-#     Fucks, W. (1955). Unterschied des Prosastils von Dichtern und anderen Schriftstellern: ein Beispiel mathematischer Stilanalyse. Bouvier.
+#     Fucks, W. (1955). Unterschied des prosastils von dichtern und anderen schriftstellern: Ein beispiel mathematischer stilanalyse. Bouvier.
 #     Briest, W. (1974). Kann man Verständlichkeit messen? STUF - Language Typology and Universals, 27(1-3), 543–563. https://doi.org/10.1524/stuf.1974.27.13.543
 def fuckss_stilcharakteristik(main, text):
     if text.lang in main.settings_global['syl_tokenizers']:
@@ -763,25 +742,25 @@ def fuckss_stilcharakteristik(main, text):
 
     return stilcharakteristik
 
-# Gulpease Index
+# GULPEASE
 # References:
 #     Lucisano, P., & Emanuela Piemontese, M. (1988). GULPEASE: A formula for the prediction of the difficulty of texts in Italian. Scuola e Città, 39(3), 110–124.
-#     Indice Gulpease. (2021, July 9). In Wikipedia.https://it.wikipedia.org/w/index.php?title=Indice_Gulpease&oldid=121763335.
-def gulpease_index(main, text):
+#     Indice Gulpease. (2021, July 9). In Wikipedia. https://it.wikipedia.org/w/index.php?title=Indice_Gulpease&oldid=121763335.
+def gulpease(main, text):
     if text.lang == 'ita':
         text = get_nums(main, text)
 
         if text.num_words:
-            gulpease_index = (
+            gulpease = (
                 89
                 + (300 * text.num_sentences - 10 * text.num_chars_alpha) / text.num_words
             )
         else:
-            gulpease_index = 'text_too_short'
+            gulpease = 'text_too_short'
     else:
-        gulpease_index = 'no_support'
+        gulpease = 'no_support'
 
-    return gulpease_index
+    return gulpease
 
 # Gunning Fog Index
 # References:
@@ -863,6 +842,27 @@ def fog_index(main, text):
 
     return fog_index
 
+# Gutiérrez de Polini's readability formula
+# References:
+#     Gutiérrez de Polini, L. E. (1972). Investigación sobre lectura en Venezuela [Paper presentation]. Primeras Jornadas de Educación Primaria, Ministerio de Educación, Caracas, Venezuela.
+#     Rodríguez Trujillo, N. (1980). Determinación de la comprensibilidad de materiales de lectura por medio de variables lingüísticas. Lectura y Vida, 1(1). http://www.lecturayvida.fahce.unlp.edu.ar/numeros/a1n1/01_01_Rodriguez.pdf
+def cp(main, text):
+    if text.lang == 'spa':
+        text = get_nums(main, text)
+
+        if text.num_words and text.num_sentences:
+            cp = (
+                95.2
+                - 9.7 * (text.num_chars_alpha / text.num_words)
+                - 0.35 * (text.num_words / text.num_sentences)
+            )
+        else:
+            cp = 'text_too_short'
+    else:
+        cp = 'no_support'
+
+    return cp
+
 # Legibilidad µ
 # Reference: Muñoz Baquedano, M. (2006). Legibilidad y variabilidad de los textos. Boletín de Investigación Educacional, Pontificia Universidad Católica de Chile, 21(2), 13–26.
 def mu(main, text):
@@ -888,9 +888,9 @@ def mu(main, text):
 
     return mu
 
-# Lensear Write
+# Lensear Write Formula
 # Reference: O’Hayre, J. (1966). Gobbledygook has gotta go (p. 8). U.S. Government Printing Office. https://www.governmentattic.org/15docs/Gobbledygook_Has_Gotta_Go_1966.pdf
-def lensear_write(main, text):
+def lensear_write_formula(main, text):
     if text.lang.startswith('eng_') and text.lang in main.settings_global['syl_tokenizers']:
         text = get_nums(main, text)
 
@@ -950,7 +950,7 @@ def lix(main, text):
 #     Lorge, I. (1948). The Lorge and Flesch readability formulae: A correction. School and Society, 67, 141–142.
 #     DuBay, W. H. (2006). In W. H. DuBay (Ed.), The classic readability studies (pp. 46–60). Impact Information. https://files.eric.ed.gov/fulltext/ED506404.pdf
 def lorge_readability_index(main, text):
-    if text.lang in main.settings_global['pos_taggers']:
+    if text.lang.startswith('eng_'):
         text = get_nums(main, text)
 
         if text.num_sentences and text.num_words:
@@ -986,7 +986,7 @@ def lorge_readability_index(main, text):
 
     return lorge
 
-# Luong-Nguyen-Dinh's Readability Formula
+# Luong-Nguyen-Dinh's readability formula
 # Reference: Luong, A.-V., Nguyen, D., & Dinh, D. (2018). A new formula for Vietnamese text readability assessment. 2018 10th International Conference on Knowledge and Systems Engineering (KSE) (pp. 198–202). IEEE. https://doi.org/10.1109/KSE.2018.8573379
 def luong_nguyen_dinhs_readability_formula(main, text):
     if text.lang == 'vie':
@@ -1010,7 +1010,7 @@ def luong_nguyen_dinhs_readability_formula(main, text):
     return readability
 
 # McAlpine EFLAW Readability Score
-# Reference: Nirmaldasan. (2009, April 30). McAlpine EFLAW readability score. Readability Monitor. Retrieved November 15, 2022, from https://strainindex.wordpress.com/2009/04/30/mcalpine-eflaw-readability-score/
+# Reference: McAlpine, R. (2006). From plain English to global English. Journalism Online. Retrieved October 31, 2024, from https://www.angelfire.com/nd/nirmaldasan/journalismonline/fpetge.html
 def eflaw(main, text):
     if text.lang.startswith('eng_'):
         text = get_nums(main, text)
@@ -1169,12 +1169,12 @@ def rix(main, text):
 
     return rix
 
-# SMOG Grade
+# SMOG Grading
 # References:
-#     McLaughlin, G. H. (1969). SMOG grading: A new readability formula. Journal of Reading, 12(8), 639–646.
+#     McLaughlin, G. H. (1969). SMOG Grading: A new readability formula. Journal of Reading, 12(8), 639–646.
 # German:
 #     Bamberger, R., & Vanecek, E. (1984). Lesen-verstehen-lernen-schreiben: Die schwierigkeitsstufen von texten in deutscher sprache (p. 78). Jugend und Volk.
-def smog_grade(main, text):
+def smog_grading(main, text):
     if text.lang in main.settings_global['syl_tokenizers']:
         text = get_nums(main, text)
 
@@ -1206,14 +1206,15 @@ def smog_grade(main, text):
 
     return g
 
-# Spache Grade Level
+# Spache readability formula
 # References:
 #     Spache, G. (1953). A new readability formula for primary-grade reading materials. Elementary School Journal, 53(7), 410–413. https://doi.org/10.1086/458513
+# Revised:
 #     Spache, G. (1974). Good reading for poor readers (Rev. 9th ed.). Garrard.
 #     Michalke, M., Brown, E., Mirisola, A., Brulet, A., & Hauser, L. (2021, May 17). Measure readability. Documentation for package ‘koRpus’ version 0.13-8. Retrieved August 3, 2023, from https://search.r-project.org/CRAN/refmans/koRpus/html/readability-methods.html
 # Spache word list:
 #     Benoit, K., Watanabe, K., Wang, H., Nulty, P., Obeng, A., Müller, S., & Matsuo, A. (2020, November 17). data_char_wordlists.rda. quanteda.textstats. Retrieved August 3, 2023, from https://github.com/quanteda/quanteda.textstats/raw/master/data/data_char_wordlists.rda
-def spache_grade_lvl(main, text):
+def spache_readability_formula(main, text):
     if text.lang.startswith('eng_'):
         text = get_nums(main, text)
 
@@ -1227,7 +1228,7 @@ def spache_grade_lvl(main, text):
 
                 num_sentences = get_num_sentences_sample(text, sample, sample_start)
 
-                if main.settings_custom['measures']['readability']['spache_grade_lvl']['use_rev_formula']:
+                if main.settings_custom['measures']['readability']['spache_readability_formula']['use_rev_formula']:
                     num_difficult_words = get_num_words_outside_list(sample, wordlist = 'spache')
                     grade_lvls.append(
                         0.121 * (100 / num_sentences)
@@ -1252,8 +1253,8 @@ def spache_grade_lvl(main, text):
 
 # Strain Index
 # References:
-#     Solomon, N. W. (2006). Qualitative analysis of media language [Unpublished doctoral dissertation]. Madurai Kamaraj University.
-#     Nirmaldasan. (2007, September 25). Strain index: A new readability formula. Readability Monitor. Retrieved August 3, 2023, from https://strainindex.wordpress.com/2007/09/25/hello-world/
+#     Nathaniel, W. S. (2017). A quantitative analysis of media language [Master’s thesis, Madurai Kamaraj University]. LAMBERT Academic Publishing.
+#     Nirmaldasan. (2007, July). Strain Index: A new readability formula. Journalism Online. Retrieved October 31, 2024, from https://www.angelfire.com/nd/nirmaldasan/readability/si.html
 def strain_index(main, text):
     if text.lang in main.settings_global['syl_tokenizers']:
         text = get_nums(main, text)
@@ -1277,9 +1278,9 @@ def strain_index(main, text):
 
     return strain_index
 
-# Tränkle & Bailer's Readability Formula
+# Tränkle-Bailer's readability formula
 # References:
-#     Tränkle, U., & Bailer, H. (1984). Kreuzvalidierung und Neuberechnung von Lesbarkeitsformeln für die Deutsche Sprache [Cross-validation and recalculation of the readability formulas for the German language]. Zeitschrift für Entwicklungspsychologie und Pädagogische Psychologie, 16(3), 231–244.
+#     Tränkle, U., & Bailer, H. (1984). Kreuzvalidierung und neuberechnung von lesbarkeitsformeln für die Deutsche sprache. Zeitschrift für Entwicklungspsychologie und Pädagogische Psychologie, 16(3), 231–244.
 #     Benoit, K. (2020, November 24). Calculate readability. quanteda: Quantitative Analysis of Textual Data. Retrieved August 3, 2023, from https://quanteda.io/reference/textstat_readability.html
 def trankle_bailers_readability_formula(main, text):
     if text.lang in main.settings_global['pos_taggers']:
@@ -1322,9 +1323,9 @@ def trankle_bailers_readability_formula(main, text):
 
     return trankle_bailers
 
-# Tuldava's Text Difficulty
+# Tuldava's readability formula
 # References:
-#     Tuldava, J. (1975). Ob izmerenii trudnosti tekstov [On measuring the complexity of the text]. Uchenye zapiski Tartuskogo universiteta. Trudy po metodike prepodavaniya inostrannykh yazykov, 345, 102–120.
+#     Tuldava, J. (1975). Ob izmerenii trudnosti tekstov. Uchenye zapiski Tartuskogo universiteta. Trudy po metodike prepodavaniya inostrannykh yazykov, 345, 102–120.
 #     Grzybek, P. (2010). Text difficulty and the Arens-Altmann law. In P. Grzybek, E. Kelih, & J. Mačutek (eds.), Text and language: Structures · functions · interrelations quantitative perspectives. Praesens Verlag. https://www.iqla.org/includes/basic_references/qualico_2009_proceedings_Grzybek_Kelih_Macutek_2009.pdf
 def td(main, text):
     if text.lang in main.settings_global['syl_tokenizers']:
@@ -1342,7 +1343,7 @@ def td(main, text):
 
     return td
 
-# Wheeler & Smith's Readability Formula
+# Wheeler-Smith's readability formula
 # Reference: Wheeler, L. R., & Smith, E. H. (1954). A practical readability formula for the classroom teacher in the primary grades. Elementary English, 31(7), 397–399.
 UNIT_TERMINATORS = ''.join(list(wl_sentence_tokenization.SENTENCE_TERMINATORS) + list(dict.fromkeys([
     # Colons and semicolons: https://util.unicode.org/UnicodeJsps/list-unicodeset.jsp?a=[:name=/COLON/:]%26[:General_Category=/Punctuation/:]
diff --git a/wordless/wl_measures/wl_measures_statistical_significance.py b/wordless/wl_measures/wl_measures_statistical_significance.py
index 512414a4b..39aab6407 100644
--- a/wordless/wl_measures/wl_measures_statistical_significance.py
+++ b/wordless/wl_measures/wl_measures_statistical_significance.py
@@ -70,7 +70,7 @@ def get_alt(direction):
 
     return alt
 
-# Fisher's Exact Test
+# Fisher's exact test
 # References: Pedersen, T. (1996). Fishing for exactness. In T. Winn (Ed.), Proceedings of the Sixth Annual South-Central Regional SAS Users' Group Conference (pp. 188–200). The South–Central Regional SAS Users' Group.
 def fishers_exact_test(main, o11s, o12s, o21s, o22s):
     settings = main.settings_custom['measures']['statistical_significance']['fishers_exact_test']
@@ -85,7 +85,7 @@ def fishers_exact_test(main, o11s, o12s, o21s, o22s):
 
     return [None] * len(p_vals), p_vals
 
-# Log-likelihood Ratio
+# Log-likelihood ratio test
 # References: Dunning, T. E. (1993). Accurate methods for the statistics of surprise and coincidence. Computational Linguistics, 19(1), 61–74.
 def log_likelihood_ratio_test(main, o11s, o12s, o21s, o22s):
     settings = main.settings_custom['measures']['statistical_significance']['log_likelihood_ratio_test']
@@ -108,7 +108,7 @@ def log_likelihood_ratio_test(main, o11s, o12s, o21s, o22s):
 
     return gs, p_vals
 
-# Mann-Whitney U Test
+# Mann-Whitney U test
 # References: Kilgarriff, A. (2001). Comparing corpora. International Journal of Corpus Linguistics, 6(1), 232–263. https://doi.org/10.1075/ijcl.6.1.05kil
 def mann_whitney_u_test(main, freqs_x1s, freqs_x2s):
     settings = main.settings_custom['measures']['statistical_significance']['mann_whitney_u_test']
@@ -129,10 +129,10 @@ def mann_whitney_u_test(main, freqs_x1s, freqs_x2s):
 
     return u1s, p_vals
 
-# Pearson's Chi-squared Test
+# Pearson's chi-squared test
 # References:
 #     Hofland, K., & Johanson, S. (1982). Word frequencies in British and American English. Norwegian Computing Centre for the Humanities.
-#     Oakes, M. P. (1998). Statistics for Corpus Linguistics. Edinburgh University Press.
+#     Oakes, M. P. (1998). Statistics for corpus linguistics. Edinburgh University Press.
 def pearsons_chi_squared_test(main, o11s, o12s, o21s, o22s):
     settings = main.settings_custom['measures']['statistical_significance']['pearsons_chi_squared_test']
 
@@ -202,7 +202,7 @@ def students_t_test_2_sample(main, freqs_x1s, freqs_x2s):
 
     return t_stats, p_vals
 
-def _z_score_p_val(z_scores, direction):
+def _z_test_p_val(z_scores, direction):
     p_vals = numpy.empty_like(z_scores)
 
     if direction == _tr('wl_measures_statistical_significance', 'Two-tailed'):
@@ -217,23 +217,23 @@ def _z_score_p_val(z_scores, direction):
 
     return p_vals
 
-# z-score
+# Z-test
 # References: Dennis, S. F. (1964). The construction of a thesaurus automatically from a sample of text. In M. E. Stevens, V. E. Giuliano, & L. B. Heilprin (Eds.), Proceedings of the symposium on statistical association methods for mechanized documentation (pp. 61–148). National Bureau of Standards.
-def z_score(main, o11s, o12s, o21s, o22s):
-    settings = main.settings_custom['measures']['statistical_significance']['z_score']
+def z_test(main, o11s, o12s, o21s, o22s):
+    settings = main.settings_custom['measures']['statistical_significance']['z_test']
 
     oxxs = o11s + o12s + o21s + o22s
     e11s, _, _, _ = get_freqs_expected(o11s, o12s, o21s, o22s)
 
     z_scores = wl_measure_utils.numpy_divide(o11s - e11s, numpy.sqrt(e11s * (1 - wl_measure_utils.numpy_divide(e11s, oxxs))))
-    p_vals = _z_score_p_val(z_scores, settings['direction'])
+    p_vals = _z_test_p_val(z_scores, settings['direction'])
 
     return z_scores, p_vals
 
-# z-score (Berry-Rogghe)
+# Z-test (Berry-Rogghe)
 # References: Berry-Rogghe, G. L. M. (1973). The computation of collocations and their relevance in lexical studies. In A. J. Aiken, R. W. Bailey, & N. Hamilton-Smith (Eds.), The computer and literary studies (pp. 103–112). Edinburgh University Press.
-def z_score_berry_rogghe(main, o11s, o12s, o21s, o22s, span):
-    settings = main.settings_custom['measures']['statistical_significance']['z_score_berry_rogghe']
+def z_test_berry_rogghe(main, o11s, o12s, o21s, o22s, span):
+    settings = main.settings_custom['measures']['statistical_significance']['z_test_berry_rogghe']
 
     o1xs, o2xs, ox1s, _ = get_freqs_marginal(o11s, o12s, o21s, o22s)
 
@@ -242,6 +242,6 @@ def z_score_berry_rogghe(main, o11s, o12s, o21s, o22s, span):
     es = ps * o1xs * span
 
     z_scores = wl_measure_utils.numpy_divide(o11s - es, numpy.sqrt(es * (1 - ps)))
-    p_vals = _z_score_p_val(z_scores, settings['direction'])
+    p_vals = _z_test_p_val(z_scores, settings['direction'])
 
     return z_scores, p_vals
diff --git a/wordless/wl_profiler.py b/wordless/wl_profiler.py
index dbc9de06b..a332c1eec 100644
--- a/wordless/wl_profiler.py
+++ b/wordless/wl_profiler.py
@@ -366,13 +366,14 @@ def update_gui_table(self, err_msg, text_stats_files):
 class Wl_Table_Profiler_Readability(Wl_Table_Profiler):
     def __init__(self, parent):
         HEADERS_READABILITY = [
-            _tr('Wl_Table_Profiler_Readability', "Al-Heeti's Readability Prediction Formula"),
+            _tr('Wl_Table_Profiler_Readability', "Al-Heeti's Readability Formula"),
             _tr('Wl_Table_Profiler_Readability', 'Automated Arabic Readability Index'),
             _tr('Wl_Table_Profiler_Readability', 'Automated Readability Index'),
             _tr('Wl_Table_Profiler_Readability', "Bormuth's Cloze Mean"),
             _tr('Wl_Table_Profiler_Readability', "Bormuth's Grade Placement"),
             _tr('Wl_Table_Profiler_Readability', 'Coleman-Liau Index'),
             _tr('Wl_Table_Profiler_Readability', "Coleman's Readability Formula"),
+            _tr('Wl_Table_Profiler_Readability', "Crawford's Readability Formula"),
             _tr('Wl_Table_Profiler_Readability', 'Dale-Chall Readability Formula'),
             _tr('Wl_Table_Profiler_Readability', "Danielson-Bryan's Readability Formula"),
             _tr('Wl_Table_Profiler_Readability', "Dawood's Readability Formula"),
@@ -383,14 +384,13 @@ def __init__(self, parent):
             _tr('Wl_Table_Profiler_Readability', 'Flesch-Kincaid Grade Level'),
             _tr('Wl_Table_Profiler_Readability', 'Flesch Reading Ease'),
             _tr('Wl_Table_Profiler_Readability', 'Flesch Reading Ease (Farr-Jenkins-Paterson)'),
-            _tr('Wl_Table_Profiler_Readability', 'FORCAST Grade Level'),
-            _tr('Wl_Table_Profiler_Readability', 'Fórmula de Comprensibilidad de Gutiérrez de Polini'),
-            _tr('Wl_Table_Profiler_Readability', 'Fórmula de Crawford'),
+            _tr('Wl_Table_Profiler_Readability', 'FORCAST'),
             _tr('Wl_Table_Profiler_Readability', "Fucks's Stilcharakteristik"),
-            _tr('Wl_Table_Profiler_Readability', 'Gulpease Index'),
+            _tr('Wl_Table_Profiler_Readability', 'GULPEASE'),
             _tr('Wl_Table_Profiler_Readability', 'Gunning Fog Index'),
+            _tr('Wl_Table_Profiler_Readability', "Gutiérrez de Polini's Readability Formula"),
             _tr('Wl_Table_Profiler_Readability', 'Legibilidad μ'),
-            _tr('Wl_Table_Profiler_Readability', 'Lensear Write'),
+            _tr('Wl_Table_Profiler_Readability', 'Lensear Write Formula'),
             _tr('Wl_Table_Profiler_Readability', 'Lix'),
             _tr('Wl_Table_Profiler_Readability', 'Lorge Readability Index'),
             _tr('Wl_Table_Profiler_Readability', "Luong-Nguyen-Dinh's Readability Formula"),
@@ -399,12 +399,12 @@ def __init__(self, parent):
             _tr('Wl_Table_Profiler_Readability', 'neue Wiener Sachtextformel'),
             _tr('Wl_Table_Profiler_Readability', 'OSMAN'),
             _tr('Wl_Table_Profiler_Readability', 'Rix'),
-            _tr('Wl_Table_Profiler_Readability', 'SMOG Grade'),
-            _tr('Wl_Table_Profiler_Readability', 'Spache Grade Level'),
+            _tr('Wl_Table_Profiler_Readability', 'SMOG Grading'),
+            _tr('Wl_Table_Profiler_Readability', 'Spache Readability Formula'),
             _tr('Wl_Table_Profiler_Readability', 'Strain Index'),
-            _tr('Wl_Table_Profiler_Readability', "Tränkle & Bailer's Readability Formula"),
-            _tr('Wl_Table_Profiler_Readability', "Tuldava's Text Difficulty"),
-            _tr('Wl_Table_Profiler_Readability', "Wheeler & Smith's Readability Formula")
+            _tr('Wl_Table_Profiler_Readability', "Tränkle-Bailer's Readability Formula"),
+            _tr('Wl_Table_Profiler_Readability', "Tuldava's Readability Formula"),
+            _tr('Wl_Table_Profiler_Readability', "Wheeler-Smith's Readability Formula")
         ]
 
         super().__init__(
@@ -589,7 +589,7 @@ def __init__(self, parent):
             _tr('Wl_Table_Profiler_Lexical_Density_Diversity', "Brunét's Index"),
             _tr('Wl_Table_Profiler_Lexical_Density_Diversity', 'Corrected TTR'),
             _tr('Wl_Table_Profiler_Lexical_Density_Diversity', "Fisher's Index of Diversity"),
-            _tr('Wl_Table_Profiler_Lexical_Density_Diversity', "Herdan's Vₘ"),
+            _tr('Wl_Table_Profiler_Lexical_Density_Diversity', "Herdan's vₘ"),
             'HD-D',
             _tr('Wl_Table_Profiler_Lexical_Density_Diversity', "Honoré's Statistic"),
             _tr('Wl_Table_Profiler_Lexical_Density_Diversity', 'Lexical Density'),
@@ -1185,6 +1185,7 @@ def run(self):
                         wl_measures_readability.bormuths_gp(self.main, text),
                         wl_measures_readability.coleman_liau_index(self.main, text),
                         wl_measures_readability.colemans_readability_formula(self.main, text),
+                        wl_measures_readability.crawfords_readability_formula(self.main, text),
                         wl_measures_readability.x_c50(self.main, text),
                         wl_measures_readability.danielson_bryans_readability_formula(self.main, text),
                         wl_measures_readability.dawoods_readability_formula(self.main, text),
@@ -1196,13 +1197,12 @@ def run(self):
                         wl_measures_readability.re_flesch(self.main, text),
                         wl_measures_readability.re_farr_jenkins_paterson(self.main, text),
                         wl_measures_readability.rgl(self.main, text),
-                        wl_measures_readability.cp(self.main, text),
-                        wl_measures_readability.formula_de_crawford(self.main, text),
                         wl_measures_readability.fuckss_stilcharakteristik(self.main, text),
-                        wl_measures_readability.gulpease_index(self.main, text),
+                        wl_measures_readability.gulpease(self.main, text),
                         wl_measures_readability.fog_index(self.main, text),
+                        wl_measures_readability.cp(self.main, text),
                         wl_measures_readability.mu(self.main, text),
-                        wl_measures_readability.lensear_write(self.main, text),
+                        wl_measures_readability.lensear_write_formula(self.main, text),
                         wl_measures_readability.lix(self.main, text),
                         wl_measures_readability.lorge_readability_index(self.main, text),
                         wl_measures_readability.luong_nguyen_dinhs_readability_formula(self.main, text),
@@ -1211,8 +1211,8 @@ def run(self):
                         wl_measures_readability.nws(self.main, text),
                         wl_measures_readability.osman(self.main, text),
                         wl_measures_readability.rix(self.main, text),
-                        wl_measures_readability.smog_grade(self.main, text),
-                        wl_measures_readability.spache_grade_lvl(self.main, text),
+                        wl_measures_readability.smog_grading(self.main, text),
+                        wl_measures_readability.spache_readability_formula(self.main, text),
                         wl_measures_readability.strain_index(self.main, text),
                         wl_measures_readability.trankle_bailers_readability_formula(self.main, text),
                         wl_measures_readability.td(self.main, text),
diff --git a/wordless/wl_settings/wl_settings_default.py b/wordless/wl_settings/wl_settings_default.py
index 247a4a4cb..4b93e6352 100644
--- a/wordless/wl_settings/wl_settings_default.py
+++ b/wordless/wl_settings/wl_settings_default.py
@@ -2318,7 +2318,7 @@ def init_settings_default(main):
                     'variant': '1'
                 },
 
-                'spache_grade_lvl': {
+                'spache_readability_formula': {
                     'use_rev_formula': True
                 },
 
@@ -2407,11 +2407,11 @@ def init_settings_default(main):
                     'direction': _tr('wl_settings_default', 'Two-tailed')
                 },
 
-                'z_score': {
+                'z_test': {
                     'direction': _tr('wl_settings_default', 'Two-tailed')
                 },
 
-                'z_score_berry_rogghe': {
+                'z_test_berry_rogghe': {
                     'direction': _tr('wl_settings_default', 'Two-tailed')
                 }
             },
diff --git a/wordless/wl_settings/wl_settings_global.py b/wordless/wl_settings/wl_settings_global.py
index 5af8b99cd..89c570475 100644
--- a/wordless/wl_settings/wl_settings_global.py
+++ b/wordless/wl_settings/wl_settings_global.py
@@ -3546,8 +3546,7 @@ def init_settings_global():
             'zul': ['vader_zul']
         },
 
-        # Only people's names are capitalized
-        # Case of measure names are preserved
+        # Only people's names are capitalized and case of measure names are preserved as in original papers
         'mapping_measures': {
             'dispersion': {
                 _tr('wl_settings_global', 'None'): 'none',
@@ -3578,12 +3577,12 @@ def init_settings_global():
                 _tr('wl_settings_global', 'None'): 'none',
                 _tr('wl_settings_global', "Fisher's exact test"): 'fishers_exact_test',
                 _tr('wl_settings_global', 'Log-likelihood ratio test'): 'log_likelihood_ratio_test',
-                _tr('wl_settings_global', 'Mann-Whitney U Test'): 'mann_whitney_u_test',
+                _tr('wl_settings_global', 'Mann-Whitney U test'): 'mann_whitney_u_test',
                 _tr('wl_settings_global', "Pearson's chi-squared test"): 'pearsons_chi_squared_test',
                 _tr('wl_settings_global', "Student's t-test (1-sample)"): 'students_t_test_1_sample',
                 _tr('wl_settings_global', "Student's t-test (2-sample)"): 'students_t_test_2_sample',
-                _tr('wl_settings_global', 'z-score'): 'z_score',
-                _tr('wl_settings_global', 'z-score (Berry-Rogghe)'): 'z_score_berry_rogghe'
+                _tr('wl_settings_global', 'Z-test'): 'z_test',
+                _tr('wl_settings_global', 'Z-test (Berry-Rogghe)'): 'z_test_berry_rogghe'
             },
 
             'bayes_factor': {
@@ -3599,9 +3598,9 @@ def init_settings_global():
                 _tr('wl_settings_global', "Dice's coefficient"): 'dices_coeff',
                 _tr('wl_settings_global', 'Difference coefficient'): 'diff_coeff',
                 _tr('wl_settings_global', 'Jaccard index'): 'jaccard_index',
-                _tr('wl_settings_global', 'Log-frequency biased MD'): 'lfmd',
                 _tr('wl_settings_global', "Kilgarriff's ratio"): 'kilgarriffs_ratio',
                 'logDice': 'log_dice',
+                _tr('wl_settings_global', 'Log-frequency biased MD'): 'lfmd',
                 _tr('wl_settings_global', 'Log ratio'): 'log_ratio',
                 'MI.log-f': 'mi_log_f',
                 _tr('wl_settings_global', 'Minimum sensitivity'): 'min_sensitivity',
@@ -3611,6 +3610,7 @@ def init_settings_global():
                 _tr('wl_settings_global', 'Odds ratio'): 'or',
                 _tr('wl_settings_global', 'Pointwise mutual information'): 'pmi',
                 _tr('wl_settings_global', 'Poisson collocation measure'): 'poisson_collocation_measure',
+                _tr('wl_settings_global', 'Squared association ratio'): 'im2',
                 _tr('wl_settings_global', 'Squared phi coefficient'): 'squared_phi_coeff'
             }
         },
@@ -3791,17 +3791,17 @@ def init_settings_global():
                 'keyword_extractor': True
             },
 
-            'z_score': {
+            'z_test': {
                 'col_text': _tr('wl_settings_global', 'z-score'),
-                'func': wl_measures_statistical_significance.z_score,
+                'func': wl_measures_statistical_significance.z_test,
                 'to_sections': False,
                 'collocation_extractor': True,
                 'keyword_extractor': True
             },
 
-            'z_score_berry_rogghe': {
+            'z_test_berry_rogghe': {
                 'col_text': _tr('wl_settings_global', 'z-score'),
-                'func': wl_measures_statistical_significance.z_score_berry_rogghe,
+                'func': wl_measures_statistical_significance.z_test_berry_rogghe,
                 'to_sections': False,
                 'collocation_extractor': True,
                 'keyword_extractor': False
@@ -3847,9 +3847,9 @@ def init_settings_global():
                 'func': wl_measures_effect_size.im3
             },
 
-            'dices_coeff': {
-                'col_text': _tr('wl_settings_global', "Dice's Coefficient"),
-                'func': wl_measures_effect_size.dices_coeff
+            'dice_sorensen_coeff': {
+                'col_text': _tr('wl_settings_global', 'Dice-Sørensen coefficient'),
+                'func': wl_measures_effect_size.dice_sorensen_coeff
             },
 
             'diff_coeff': {
@@ -3922,6 +3922,11 @@ def init_settings_global():
                 'func': wl_measures_effect_size.poisson_collocation_measure
             },
 
+            'im2': {
+                'col_text': 'IM²',
+                'func': wl_measures_effect_size.im2
+            },
+
             'squared_phi_coeff': {
                 'col_text': 'φ2',
                 'func': wl_measures_effect_size.squared_phi_coeff
diff --git a/wordless/wl_settings/wl_settings_measures.py b/wordless/wl_settings/wl_settings_measures.py
index 5a0773446..91f00ef1f 100644
--- a/wordless/wl_settings/wl_settings_measures.py
+++ b/wordless/wl_settings/wl_settings_measures.py
@@ -31,8 +31,8 @@ def __init__(self, main):
         self.settings_default = self.main.settings_default['measures']['readability']
         self.settings_custom = self.main.settings_custom['measures']['readability']
 
-        # Al-Heeti's Readability Prediction Formula
-        self.group_box_rd = QGroupBox(self.tr("Al-Heeti's Readability Prediction Formula"), self)
+        # Al-Heeti's Readability Formula
+        self.group_box_rd = QGroupBox(self.tr("Al-Heeti's Readability Formula"), self)
 
         self.label_rd_variant = QLabel(self.tr('Variant:'), self)
         self.combo_box_rd_variant = wl_boxes.Wl_Combo_Box(self)
@@ -207,16 +207,16 @@ def __init__(self, main):
 
         self.group_box_nws.layout().setColumnStretch(2, 1)
 
-        # Spache Grade Level
-        self.group_box_spache_grade_lvl = QGroupBox(self.tr('Spache Grade Level'), self)
+        # Spache Readability Formula
+        self.group_box_spache_readability_formula = QGroupBox(self.tr('Spache Readability Formula'), self)
 
         self.checkbox_use_rev_formula = QCheckBox(self.tr('Use revised formula'), self)
 
-        self.group_box_spache_grade_lvl.setLayout(wl_layouts.Wl_Layout())
-        self.group_box_spache_grade_lvl.layout().addWidget(self.checkbox_use_rev_formula, 0, 0)
+        self.group_box_spache_readability_formula.setLayout(wl_layouts.Wl_Layout())
+        self.group_box_spache_readability_formula.layout().addWidget(self.checkbox_use_rev_formula, 0, 0)
 
-        # Tränkle & Bailer's Readability Formula
-        self.group_box_trankle_bailers_readability_formula = QGroupBox(self.tr("Tränkle & Bailer's Readability Formula"), self)
+        # Tränkle-Bailer's Readability Formula
+        self.group_box_trankle_bailers_readability_formula = QGroupBox(self.tr("Tränkle-Bailer's Readability Formula"), self)
 
         self.label_trankle_bailers_readability_formula_variant = QLabel(self.tr('Variant:'), self)
         self.combo_box_trankle_bailers_readability_formula_variant = wl_boxes.Wl_Combo_Box(self)
@@ -242,7 +242,7 @@ def __init__(self, main):
         self.layout().addWidget(self.group_box_lorge_readability_index, 9, 0)
         self.layout().addWidget(self.group_box_nwl, 10, 0)
         self.layout().addWidget(self.group_box_nws, 11, 0)
-        self.layout().addWidget(self.group_box_spache_grade_lvl, 12, 0)
+        self.layout().addWidget(self.group_box_spache_readability_formula, 12, 0)
         self.layout().addWidget(self.group_box_trankle_bailers_readability_formula, 13, 0)
 
         self.layout().setContentsMargins(6, 4, 6, 4)
@@ -262,7 +262,7 @@ def load_settings(self, defaults = False):
         else:
             settings = copy.deepcopy(self.settings_custom)
 
-        # Al-Heeti's Readability Prediction Formula
+        # Al-Heeti's Readability Formula
         self.combo_box_rd_variant.setCurrentText(settings['rd']['variant'])
 
         # Automated Readability Index
@@ -300,14 +300,14 @@ def load_settings(self, defaults = False):
         # neue Wiener Sachtextformel
         self.combo_box_nws_variant.setCurrentText(settings['nws']['variant'])
 
-        # Spache Grade Level
-        self.checkbox_use_rev_formula.setChecked(settings['spache_grade_lvl']['use_rev_formula'])
+        # Spache Readability Formula
+        self.checkbox_use_rev_formula.setChecked(settings['spache_readability_formula']['use_rev_formula'])
 
-        # Tränkle & Bailer's Readability Formula
+        # Tränkle-Bailer's Readability Formula
         self.combo_box_trankle_bailers_readability_formula_variant.setCurrentText(settings['trankle_bailers_readability_formula']['variant'])
 
     def apply_settings(self):
-        # Al-Heeti's Readability Prediction Formula
+        # Al-Heeti's Readability Formula
         self.settings_custom['rd']['variant'] = self.combo_box_rd_variant.currentText()
 
         # Automated Readability Index
@@ -345,10 +345,10 @@ def apply_settings(self):
         # neue Wiener Sachtextformel
         self.settings_custom['nws']['variant'] = self.combo_box_nws_variant.currentText()
 
-        # Spache Grade Level
-        self.settings_custom['spache_grade_lvl']['use_rev_formula'] = self.checkbox_use_rev_formula.isChecked()
+        # Spache Readability Formula
+        self.settings_custom['spache_readability_formula']['use_rev_formula'] = self.checkbox_use_rev_formula.isChecked()
 
-        # Tränkle & Bailer's Readability Formula
+        # Tränkle-Bailer's Readability Formula
         self.settings_custom['trankle_bailers_readability_formula']['variant'] = self.combo_box_trankle_bailers_readability_formula_variant.currentText()
 
         return True
@@ -760,33 +760,33 @@ def __init__(self, main):
 
         self.group_box_students_t_test_2_sample.layout().setColumnStretch(2, 1)
 
-        # z-score
-        self.group_box_z_score = QGroupBox(self.tr('z-score'), self)
+        # Z-test
+        self.group_box_z_test = QGroupBox(self.tr('Z-test'), self)
 
         (
-            self.label_z_score_direction,
-            self.combo_box_z_score_direction
+            self.label_z_test_direction,
+            self.combo_box_z_test_direction
         ) = wl_widgets.wl_widgets_direction(self)
 
-        self.group_box_z_score.setLayout(wl_layouts.Wl_Layout())
-        self.group_box_z_score.layout().addWidget(self.label_z_score_direction, 0, 0)
-        self.group_box_z_score.layout().addWidget(self.combo_box_z_score_direction, 0, 1)
+        self.group_box_z_test.setLayout(wl_layouts.Wl_Layout())
+        self.group_box_z_test.layout().addWidget(self.label_z_test_direction, 0, 0)
+        self.group_box_z_test.layout().addWidget(self.combo_box_z_test_direction, 0, 1)
 
-        self.group_box_z_score.layout().setColumnStretch(2, 1)
+        self.group_box_z_test.layout().setColumnStretch(2, 1)
 
-        # z-score (Berry-Rogghe)
-        self.group_box_z_score_berry_rogghe = QGroupBox(self.tr('z-score (Berry-Rogghe)'), self)
+        # Z-test (Berry-Rogghe)
+        self.group_box_z_test_berry_rogghe = QGroupBox(self.tr('Z-test (Berry-Rogghe)'), self)
 
         (
-            self.label_z_score_berry_rogghe_direction,
-            self.combo_box_z_score_berry_rogghe_direction
+            self.label_z_test_berry_rogghe_direction,
+            self.combo_box_z_test_berry_rogghe_direction
         ) = wl_widgets.wl_widgets_direction(self)
 
-        self.group_box_z_score_berry_rogghe.setLayout(wl_layouts.Wl_Layout())
-        self.group_box_z_score_berry_rogghe.layout().addWidget(self.label_z_score_berry_rogghe_direction, 0, 0)
-        self.group_box_z_score_berry_rogghe.layout().addWidget(self.combo_box_z_score_berry_rogghe_direction, 0, 1)
+        self.group_box_z_test_berry_rogghe.setLayout(wl_layouts.Wl_Layout())
+        self.group_box_z_test_berry_rogghe.layout().addWidget(self.label_z_test_berry_rogghe_direction, 0, 0)
+        self.group_box_z_test_berry_rogghe.layout().addWidget(self.combo_box_z_test_berry_rogghe_direction, 0, 1)
 
-        self.group_box_z_score_berry_rogghe.layout().setColumnStretch(2, 1)
+        self.group_box_z_test_berry_rogghe.layout().setColumnStretch(2, 1)
 
         self.setLayout(wl_layouts.Wl_Layout())
         self.layout().addWidget(self.group_box_fishers_exact_test, 0, 0)
@@ -795,8 +795,8 @@ def __init__(self, main):
         self.layout().addWidget(self.group_box_pearsons_chi_squared_test, 3, 0)
         self.layout().addWidget(self.group_box_students_t_test_1_sample, 4, 0)
         self.layout().addWidget(self.group_box_students_t_test_2_sample, 5, 0)
-        self.layout().addWidget(self.group_box_z_score, 6, 0)
-        self.layout().addWidget(self.group_box_z_score_berry_rogghe, 7, 0)
+        self.layout().addWidget(self.group_box_z_test, 6, 0)
+        self.layout().addWidget(self.group_box_z_test_berry_rogghe, 7, 0)
 
         self.layout().setContentsMargins(6, 4, 6, 4)
         self.layout().setRowStretch(8, 1)
@@ -830,11 +830,11 @@ def load_settings(self, defaults = False):
         self.combo_box_students_t_test_2_sample_use_data.setCurrentText(settings['students_t_test_2_sample']['use_data'])
         self.combo_box_students_t_test_2_sample_direction.setCurrentText(settings['students_t_test_2_sample']['direction'])
 
-        # z-score
-        self.combo_box_z_score_direction.setCurrentText(settings['z_score']['direction'])
+        # Z-test
+        self.combo_box_z_test_direction.setCurrentText(settings['z_test']['direction'])
 
-        # z-score (Berry-Rogghe)
-        self.combo_box_z_score_berry_rogghe_direction.setCurrentText(settings['z_score_berry_rogghe']['direction'])
+        # Z-test (Berry-Rogghe)
+        self.combo_box_z_test_berry_rogghe_direction.setCurrentText(settings['z_test_berry_rogghe']['direction'])
 
     def apply_settings(self):
         # Fisher's Exact Test
@@ -860,11 +860,11 @@ def apply_settings(self):
         self.settings_custom['students_t_test_2_sample']['use_data'] = self.combo_box_students_t_test_2_sample_use_data.currentText()
         self.settings_custom['students_t_test_2_sample']['direction'] = self.combo_box_students_t_test_2_sample_direction.currentText()
 
-        # z-score
-        self.settings_custom['z_score']['direction'] = self.combo_box_z_score_direction.currentText()
+        # Z-test
+        self.settings_custom['z_test']['direction'] = self.combo_box_z_test_direction.currentText()
 
-        # z-score (Berry-Rogghe)
-        self.settings_custom['z_score_berry_rogghe']['direction'] = self.combo_box_z_score_berry_rogghe_direction.currentText()
+        # Z-test (Berry-Rogghe)
+        self.settings_custom['z_test_berry_rogghe']['direction'] = self.combo_box_z_test_berry_rogghe_direction.currentText()
 
         return True