Tärnö Maria by Daniel Fallström : Difficulty Assessment for Swedish Learners

How difficult is Tärnö Maria for Swedish learners? We have performed multiple tests on its full text (freely available here) of approximately 34,468, crunched all the numbers for you and present the results below.

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Difficulty Assessment Summary

We have estimated Tärnö Maria to have a difficulty score of 60. Here're its scores:

Measure Score
easy difficult (1 - 100)
Overall Difficulty 60% 60
Vocabulary Difficulty 73% 73
Grammatical Difficulty 48% 48

Vocabulary Difficulty: Breakdown

73%

Vocabulary difficulty: 73%

This score has been calculated based on frequency vocabulary (the top most frequently used words in Swedish). It combines various measures of Tärnö Maria's text analyzed in terms of frequency vocabulary: a plain vocabulary score, frequency-weighted vocabulary score, banded frequency vocabulary scores based on vocabulary of the text falling in the top 1,000 or 2,000 most frequent words, etc. Here's a further breakdown of how often the top most frequently used words in Swedish appear in the full text of Tärnö Maria:

Vocabulary difficulty breakdown for Tärnö Maria: a test for Swedish top frequency vocabulary

We have also calculated the following approximate data on the vocabulary in Tärnö Maria:

Measure Score
Measure Score
Number of words 34,468
Number of unique words 7,062
Number of recognized words for names/places/other entities 1,499
Number of very rare non-entity words 1,524
Number of sentences 4,915
Average number of words/sentence 7

There is some research suggesting that that you need to know about 98% of a text's vocabulary in order to be able to infer the meaning of unknown words when reading. If true, this means that you would need to know around 6,920 words (where all the forms of the word are still counted as unique words) in Swedish to be able to read Tärnö Maria without a dictionary and fully understand it.

Grammatical Difficulty: Breakdown

48%

Grammatical difficulty: 48%

Here is the further grammatical comparison on this text. You can find an explanation of all these scores below.

Measure Score
Measure Score
Automated Readability Index 3
Coleman-Liau Index 7
Type/Token Ratio (TTR) 0.204886
Root type/Token Ratio (RTTR) 0.00000594423
Corrected type/Token Ratio (CTTR) 0.00000297211
MTLD Index 62
HDD Index 63
Yule's I Index 66
Lexical Diversity Index (MTLD + HD-D + Yule's I) 63

The type-token ratio (TTR) of Tärnö Maria is 0.204886. The TTR is the most basic measure of lexical diversity. To calculate it, we divide the number of unique words by the number of words in the text. For example, for this text, the number of unique words is 7,062, while the number of words is 34,468, so the TTR is 7,062 / 34,468 = 0.204886. However, the TTR is a very crude measure, as it is extremely dependent on text length. The longer the text, the lower the TTR is usually going to be, since common words tend to often repeat. Especially since the number of words in this text is more than 1,000, the TTR is not likely to give an accurate measure.

The root type-token ratio (RTTR) and corrected type-token ratio (CTTR) are measures which were suggested by researchers to partially address the problem of TTR's variance on text length. In the RTTR, the number of unique words is divided by a square of the number of words (therefore, 7,062 / (34,468 * 34,468) = 0.00000594423), while in CTTR, it is divided by a square of the number of words, multiplied twice 7,062 / 2 * (34,468 * 34,468) = 0.00000297211). However, these measures are not as easily readable, and also there is a growing body of research asserting that CTTR and RTTR do not effectively address the problems of text length. Therefore, while we do provide the full text's TTR, RTTR and CTTR on this page, these fiqures do not form part of our final calculations.

The Automated Readability Index (ARI) is one readability measure that has been developed by researchers over the years. The formula for calculating the ARI is as follows:
Formula for calculating the Automated Readability Index

The ARI should compute a reading level approximately corresponding to the reader's grade level (assuming the reader undertakes formal education). Thus, for example, a value of 1 is kindergarten level, while a value of 12 or 13 is the last year of school, and 14 is a sophomore at college. The current ARI of this text is 3, making it understandable for 3-grade students at their expected level of education.

The Coleman Liau Index (CLI) is a similar index designed by Meri Coleman and T. L. Liau, and it is supposed to compute the grade level of the reader (thus, for example, sophomore level material would be around grade 14, or year 14 of formal education, while kindergarten / primary school level material would be close to grade 1 in the CLI). The CLI is usually slightly higher than the ARI. The CLI is computed with this formula:
Formula for calculating the Coleman-Liau Readability Index

It is notable that other indexes exist, such as the Flesch-Kincaid Reading Ease, Gunning-Fog Score, and others, but we have chosen not to include them, since, contrary to the ARI and CLI, such other indexes are based on a syllable count and therefore arguably only work for English and not Swedish.

We compute a further compound lexical diversity index, which should range from 1 to a 100 (with the standard deviation being around 10, and its average value being around 50) - it is 63 in the present case. The compound lexical diversity index consists of the following indexes, averaged out (and also provided in the table above):

  • the Measure of Textual Lexical Diversity (MTLD) index - a measure which is based on computing the TTR for increasingly larger parts of the text until the TTR drops below a certain threshold point (around 0.7 in our case) - in which case, the TTR is reset, and the overall counter is increased; the counter is at the end divided by the number of words in text; as a result, the MTLD does not significantly vary by text length;
  • the Yule's I index (based on Yule's K characteristic inverted) - an index based on the work of the statistician G.U. Yule, who published his index of Frequency Vocabulary in his paper "The statistical study of literary vocabulary"; Yule's I takes into account the number of words in the text, and a compound summed measure of word frequency;
  • the Hypergeometric Distribution D (HD-D) index (based on vocd) - an index which assesses the contribution of each word to the diversity of the text; to calculate such contributions, a hypergeometric distribution is used to compute probabilities of each word appearing in word samples extracted from the text; then such distributions are divided by sample sizes and added up;

Our overall measure of grammatical diversity is based on a combination of the compound lexical diversity index (which includes the MTLD, Yule's I and HD-D indexes), the ARI and CLI, all normalized and given certain weight. The score should normally range from 1 to 100. In this case, the score is 48.

Other Information about Tärnö Maria by Daniel Fallström

We provide you a sample of the text below, however, the full text of the Tärnö Maria is also available free of charge on our website.

Sample of text:

. . madame!» Tonfallet i detta sista ord borde hafva öfvertygat henne om, att Felix Pyat var mer än nöjd. »Och nu, då jag uppfyllt er begäran, min herre, kommer jag i minordning att begära någonting af er.» »Befall!» »Nej, jag ber!» Journalisten bugade sig. »Man har sagt mig, att ni är eller, rättare sagdt, var den skickligaste fäktaren i Paris, att ni sköter värjan lika bra som pennan, och det vill säga mycket. Vill ni ge mig en lektion?» »Ni utmanar mig således? Ni vann nyss en fullständig seger genom er konst, vill ni nu också. . . * »Ah, ni smickrar! — det är således af gj or dt. — Och nu — till vapen!» ...

Top most frequently used words in Tärnö Maria by Daniel Fallström*

Position Word Repetitions Part of all words
Position Word Repetitions Part of all words
1 och 1,355 3.93%
2 en 647 1.88%
3 att 629 1.82%
4 han 559 1.62%
5 548 1.59%
6 det 526 1.53%
7 som 480 1.39%
8 den 421 1.22%
9 sig 390 1.13%
10 hon 363 1.05%
11 med 359 1.04%
12 till 348 1.01%
13 var 335 0.97%
14 af 311 0.9%
15 för 308 0.89%
16 hade 287 0.83%
17 ett 248 0.72%
18 236 0.68%
19 jag 221 0.64%
20 de 217 0.63%
21 inte 173 0.5%
22 henne 167 0.48%
23 honom 161 0.47%
24 om 155 0.45%
25 där 150 0.44%
26 men 147 0.43%
27 är 144 0.42%
28 man 143 0.41%
29 öfver 130 0.38%
30 sin 127 0.37%
31 skulle 126 0.37%
32 mig 122 0.35%
33 119 0.35%
34 hennes 114 0.33%
35 du 111 0.32%
36 nu 102 0.3%
37 från 97 0.28%
38 har 96 0.28%
39 icke 94 0.27%
40 hans 94 0.27%
41 vid 94 0.27%
42 när 92 0.27%
43 fram 92 0.27%
44 ut 86 0.25%
45 upp 73 0.21%
46 kunde 71 0.21%
47 mot 69 0.2%
48 såg 68 0.2%
49 sina 64 0.19%
50 ned 62 0.18%
51 min 61 0.18%
52 dem 61 0.18%
53 gick 60 0.17%
54 efter 60 0.17%
55 kom 59 0.17%
56 in 59 0.17%
57 allt 57 0.17%
58 under 57 0.17%
59 56 0.16%
60 sitt 55 0.16%
61 ni 55 0.16%
62 eller 54 0.16%
63 tog 53 0.15%
64 gång 53 0.15%
65 lilla 52 0.15%
66 detta 52 0.15%
67 åt 52 0.15%
68 än 51 0.15%
69 sade 50 0.15%
70 vara 49 0.14%
71 hvad 48 0.14%
72 själf 47 0.14%
73 Maria 47 0.14%
74 blef 47 0.14%
75 endast 46 0.13%
76 aldrig 46 0.13%
77 ha 45 0.13%
78 Hök 45 0.13%
79 någon 44 0.13%
80 varit 44 0.13%
81 ännu 43 0.12%
82 också 43 0.12%
83 ville 43 0.12%
84 ur 42 0.12%
85 Bob 42 0.12%
86 stod 42 0.12%
87 er 41 0.12%
88 sedan 40 0.12%
89 skall 40 0.12%
90 denna 39 0.11%
91 började 39 0.11%
92 se 38 0.11%
93 Jonas 38 0.11%
94 mycket 37 0.11%
95 genom 37 0.11%
96 komma 37 0.11%
97 gamla 37 0.11%
98 här 37 0.11%
99 utan 36 0.1%
100 hvilken 36 0.1%
101 alla 36 0.1%
102 göra 35 0.1%
103 Henri 35 0.1%
104 låg 35 0.1%
105 tillbaka 35 0.1%
106 framför 35 0.1%
107 bli 35 0.1%
108 ju 35 0.1%
109 helt 35 0.1%
110 måste 34 0.1%
111 något 34 0.1%
112 väl 33 0.1%
113 gjorde 33 0.1%
114 ögonblick 33 0.1%
115 steg 32 0.09%
116 ty 32 0.09%
117 unga 32 0.09%
118 bara 32 0.09%
119 sist 32 0.09%
120 kommer 31 0.09%
121 liten 31 0.09%
122 vi 31 0.09%
123 kände 30 0.09%
124 satt 30 0.09%
125 sista 30 0.09%
126 hela 29 0.08%
127 kommit 29 0.08%
128 29 0.08%
129 ingen 28 0.08%
130 hand 28 0.08%
131 mellan 28 0.08%
132 kan 27 0.08%
133 inne 27 0.08%
134 säga 27 0.08%
135 vill 27 0.08%
136 dig 27 0.08%
137 mer 27 0.08%
138 ska 27 0.08%
139 någonting 26 0.08%
140 lätt 26 0.08%
141 lika 26 0.08%
142 emot 26 0.08%
143 medan 26 0.08%
144 första 26 0.08%
145 frågade 26 0.08%
146 därför 26 0.08%
147 vet 26 0.08%
148 Strykern 26 0.08%
149 drog 25 0.07%
150 slog 25 0.07%
151 hvita 25 0.07%
152 Olle 25 0.07%
153 rätt 24 0.07%
154 Ja 24 0.07%
155 länge 24 0.07%
156 hur 24 0.07%
157 par 24 0.07%
158 gaf 24 0.07%
159 lät 24 0.07%
160 år 23 0.07%
161 midt 23 0.07%
162 höll 23 0.07%
163 blifvit 23 0.07%
164 andra 23 0.07%
165 lade 23 0.07%
166 voro 23 0.07%
167 hufvudet 23 0.07%
168 igen 22 0.06%
169 långt 22 0.06%
170 ögonen 22 0.06%
171 några 22 0.06%
172 litet 22 0.06%
173 alltid 22 0.06%
174 likväl 22 0.06%
175 bland 21 0.06%
176 fått 21 0.06%
177 bort 21 0.06%
178 mera 21 0.06%
179 sett 21 0.06%
180 tänkte 21 0.06%
181 nog 21 0.06%

This list excludes punctuation or single-letter words, also some different-case repeats of the same words.

If you think the text would be accessible to you, you can read it on our site (click on the cover to access):

Cover of Tärnö Maria by Daniel Fallström

Other resources and languages

If you like this analysis, you should have a look at out our lists of Swedish short stories and Swedish books.

If you like literature as a means to learn languages - please take a look at our project Interlinear Books. We even have a Swedish Interlinear book available for purchase.