Minnesteckningar och andra uppsatser by Carl Snoilsky : Difficulty Assessment for Swedish Learners

How difficult is Minnesteckningar och andra uppsatser for Swedish learners? We have performed multiple tests on its full text (freely available here) of approximately 70,511, crunched all the numbers for you and present the results below.

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Difficulty Assessment Summary

We have estimated Minnesteckningar och andra uppsatser to have a difficulty score of 82. Here're its scores:

Measure Score
easy difficult (1 - 100)
Overall Difficulty 82% 82
Vocabulary Difficulty 95% 95
Grammatical Difficulty 69% 69

Vocabulary Difficulty: Breakdown

95%

Vocabulary difficulty: 95%

This score has been calculated based on frequency vocabulary (the top most frequently used words in Swedish). It combines various measures of Minnesteckningar och andra uppsatser'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 Minnesteckningar och andra uppsatser:

Vocabulary difficulty breakdown for Minnesteckningar och andra uppsatser: a test for Swedish top frequency vocabulary

We have also calculated the following approximate data on the vocabulary in Minnesteckningar och andra uppsatser:

Measure Score
Measure Score
Number of words 70,511
Number of unique words 16,509
Number of recognized words for names/places/other entities 3,727
Number of very rare non-entity words 6,194
Number of sentences 11,774
Average number of words/sentence 6

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 16,178 words (where all the forms of the word are still counted as unique words) in Swedish to be able to read Minnesteckningar och andra uppsatser without a dictionary and fully understand it.

Grammatical Difficulty: Breakdown

69%

Grammatical difficulty: 69%

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 7
Coleman-Liau Index 11
Type/Token Ratio (TTR) 0.234134
Root type/Token Ratio (RTTR) 0.00000332053
Corrected type/Token Ratio (CTTR) 0.00000166026
MTLD Index 95
HDD Index 71
Yule's I Index 90
Lexical Diversity Index (MTLD + HD-D + Yule's I) 86

The type-token ratio (TTR) of Minnesteckningar och andra uppsatser is 0.234134. 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 16,509, while the number of words is 70,511, so the TTR is 16,509 / 70,511 = 0.234134. 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, 16,509 / (70,511 * 70,511) = 0.00000332053), while in CTTR, it is divided by a square of the number of words, multiplied twice 16,509 / 2 * (70,511 * 70,511) = 0.00000166026). 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 7, making it understandable for 7-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 86 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 69.

Other Information about Minnesteckningar och andra uppsatser by Carl Snoilsky

We provide you a sample of the text below, however, the full text of the Minnesteckningar och andra uppsatser is also available free of charge on our website.

Sample of text:

Den hufvudstad, som hans tappra klinga försvarat, skulle därtill under en följd af år i honom äga sin främste styresman, och han skulle med samma nit vaka öfver dess väl under brydsamma tider af inre tvedräkt och oro. Rutger Fuchs var årsbarn med Carl XII. Han föddes den 12 april 1682 i Malmö, där fadern, öfverstelöjtnanten Christian Fuchs, var kommendant. Modern, Susanna Leonora (d. 1743), var dotter till öfverkommissarien, sedermera assessorn i Göta hofrätt, Baltasar Struve, adlad Lejonsten. Öfverstelöjtnant Christian Fuchs, född den 3 mars 1624, ansågs själf vara af gammal adel. Ätten, af blandadt böhmiskt och lithauiskt ursprung, hade senast varit besuten i Polska Preussen, ...

Top most frequently used words in Minnesteckningar och andra uppsatser by Carl Snoilsky*

Position Word Repetitions Part of all words
Position Word Repetitions Part of all words
1 och 1,865 2.64%
2 att 1,381 1.96%
3 af 1,224 1.74%
4 den 1,043 1.48%
5 en 1,007 1.43%
6 som 901 1.28%
7 till 880 1.25%
8 han 823 1.17%
9 för 736 1.04%
10 725 1.03%
11 med 711 1.01%
12 det 684 0.97%
13 de 571 0.81%
14 sig 511 0.72%
15 var 462 0.66%
16 icke 448 0.64%
17 ett 426 0.6%
18 sin 403 0.57%
19 hans 396 0.56%
20 hade 361 0.51%
21 om 323 0.46%
22 vid 294 0.42%
23 honom 284 0.4%
24 från 272 0.39%
25 är 267 0.38%
26 263 0.37%
27 men 257 0.36%
28 under 217 0.31%
29 Warmholtz 217 0.31%
30 jag 197 0.28%
31 skulle 184 0.26%
32 man 182 0.26%
33 180 0.26%
34 såsom 176 0.25%
35 eller 165 0.23%
36 där 163 0.23%
37 efter 159 0.23%
38 detta 158 0.22%
39 sina 157 0.22%
40 genom 155 0.22%
41 denna 152 0.22%
42 kunde 151 0.21%
43 hvilken 151 0.21%
44 sitt 145 0.21%
45 nu 144 0.2%
46 än 143 0.2%
47 hvilka 138 0.2%
48 utan 134 0.19%
49 vara 128 0.18%
50 andra 126 0.18%
51 har 125 0.18%
52 hos 124 0.18%
53 Choræus 123 0.17%
54 själf 121 0.17%
55 Fuchs 120 0.17%
56 mot 120 0.17%
57 öfver 116 0.16%
58 äfven 116 0.16%
59 ej 114 0.16%
60 åt 113 0.16%
61 Gjörwell 111 0.16%
62 varit 110 0.16%
63 år 103 0.15%
64 dessa 98 0.14%
65 någon 98 0.14%
66 hafva 97 0.14%
67 mig 96 0.14%
68 allt 96 0.14%
69 dess 93 0.13%
70 hvars 92 0.13%
71 samma 86 0.12%
72 ännu 86 0.12%
73 alla 85 0.12%
74 svenska 83 0.12%
75 voro 83 0.12%
76 redan 81 0.11%
77 hvilket 81 0.11%
78 dock 81 0.11%
79 tid 77 0.11%
80 göra 76 0.11%
81 fick 75 0.11%
82 sedan 75 0.11%
83 första 75 0.11%
84 dem 73 0.1%
85 blef 73 0.1%
86 mera 72 0.1%
87 något 71 0.1%
88 gång 71 0.1%
89 bland 71 0.1%
90 mycket 70 0.1%
91 här 70 0.1%
92 samt 69 0.1%
93 Königsmark 69 0.1%
94 endast 69 0.1%
95 stora 68 0.1%
96 min 68 0.1%
97 väl 67 0.1%
98 några 66 0.09%
99 skall 65 0.09%
100 kan 63 0.09%
101 vi 63 0.09%
102 ur 60 0.09%
103 många 60 0.09%
104 hela 60 0.09%
105 bref 59 0.08%
106 hvad 59 0.08%
107 aldrig 59 0.08%
108 59 0.08%
109 Stockholm 58 0.08%
110 hon 58 0.08%
111 inom 56 0.08%
112 Choræi 56 0.08%
113 Sverige 56 0.08%
114 Vratz 55 0.08%
115 blifvit 54 0.08%
116 mindre 54 0.08%
117 Sehlstedt 53 0.08%
118 äro 52 0.07%
119 namn 52 0.07%
120 ingen 52 0.07%
121 sista 51 0.07%
122 deras 51 0.07%
123 Carl 51 0.07%
124 måste 51 0.07%
125 blott 51 0.07%
126 flera 50 0.07%
127 ville 49 0.07%
128 vore 48 0.07%
129 du 47 0.07%
130 Franzén 46 0.07%
131 GENBERG 46 0.07%
132 kom 46 0.07%
133 annan 45 0.06%
134 ofta 45 0.06%
135 oss 44 0.06%
136 nya 44 0.06%
137 Sveriges 44 0.06%
138 själfva 44 0.06%
139 Thynne 44 0.06%
140 vår 43 0.06%
141 sätt 43 0.06%
142 mer 43 0.06%
143 sålunda 43 0.06%
144 också 43 0.06%
145 in 43 0.06%
146 annat 43 0.06%
147 denne 42 0.06%
148 lika 42 0.06%
149 von 42 0.06%
150 när 40 0.06%
151 följande 40 0.06%
152 senare 40 0.06%
153 förut 40 0.06%
154 liksom 39 0.06%
155 nog 39 0.06%
156 hvarje 38 0.05%
157 snart 37 0.05%
158 ut 37 0.05%
159 vän 37 0.05%
160 fann 37 0.05%
161 likväl 36 0.05%
162 gamla 36 0.05%
163 se 36 0.05%
164 hand 35 0.05%
165 intet 35 0.05%
166 emot 35 0.05%
167 anledning 35 0.05%
168 dagar 35 0.05%
169 emellertid 35 0.05%
170 ord 34 0.05%
171 hennes 34 0.05%
172 alltid 34 0.05%
173 fått 34 0.05%
174 dig 34 0.05%
175 ehuru 33 0.05%
176 huru 33 0.05%
177 stor 33 0.05%
178 verk 33 0.05%
179 egen 33 0.05%

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 Minnesteckningar och andra uppsatser by Carl Snoilsky

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.