S1: S1 dataset

In FuzzyStatTra: Statistical Methods for Trapezoidal Fuzzy Numbers

Description

S1 is a matrix of dimension 69 x 4 containing 69 trapezoidal fuzzy rating responses, each of which is characterized by its four values inf0,inf1,sup1,sup0. The data correspond to the well-known questionnaire TIMSS-PIRLS2011. This questionnaire was adapted to allow a double-type response, namely, the original Likert and a fuzzy rating scale-based (to simplify, trapezoidal). The questionnaire was conducted on 69 fourth grade students from Colegio San Ignacio (Oviedo-Asturias, Spain). Trapezoidal fuzzy rating responses to the Question S1 "My teacher taught me to discover science in daily life" are collected in this dataset.

Usage

data("S1")

Format

A matrix of dimension 69 x 4 containing 69 trapezoidal fuzzy rating responses, each of which is characterized by its four values inf0,inf1,sup1,sup0.

Details

See examples

Source

The complete dataset can be found in http://bellman.ciencias.uniovi.es/SMIRE/FuzzyRatingScaleQuestionnaire-SanIgnacio.html

References

[1] Gil, M.A.; Lubiano, M.A.; De la Rosa de Saa, S.; Sinova, B.: Analyzing data from a fuzzy rating scale-based questionnaire. A case study, Psicothema 27(2), pp. 182-191 (2015)

[2] Lubiano, M.A.; De la Rosa de Saa, S.; Montenegro, M.; Sinova, B.; Gil, M.A.: Descriptive analysis of responses to items in questionnaires. Why not a fuzzy rating scale?, Information Sciences 360, pp. 131-148 (2016)

[3] Lubiano, M.A.; Montenegro, M.; Sinova, B.; De la Rosa de Saa, S.; Gil, M.A.: Hypothesis testing for means in connection with fuzzy rating scale-based data: algorithms and applications, European Journal of Operational Research 251, pp. 918-929 (2016)

Examples

data(S1)
filterNA(S1)
F=filterNA(S1)[[1]]
Var(F)

Example output

[[1]]
     inf0   inf1   sup1   sup0
1   6.350  6.950  8.000  8.000
2   0.450  6.225  7.400  7.500
3   2.500  3.750  6.250  7.500
4   5.975  7.900  9.450 10.000
5   9.175  9.200  9.950  9.950
6   6.500  7.000  7.500  8.000
7   4.000  4.800  7.275  7.500
8   9.000  9.000  9.450 10.000
9   9.000 10.000 10.000 10.000
10  0.800  0.800  1.300  1.850
11  6.925  6.925  7.500  7.500
12  5.000  5.000  7.000  7.000
13  2.000  2.800  4.000  4.000
14  4.000  5.650  6.725  7.375
15  4.625  4.650  7.475  7.500
16  3.550  3.750  6.950  6.950
17  3.625  4.600  6.950  7.500
18  6.175  6.475  8.250  9.525
19  6.700  6.700  9.975 10.000
20  5.000  6.050  6.875  7.350
21 10.000 10.000 10.000 10.000
22  5.000  5.500  6.500  7.100
24  4.725  5.375  7.275  7.600
25  3.000  3.500  3.500  4.000
26  5.300  6.000  6.500  6.500
27  9.000  9.150  9.550 10.000
28  1.400  3.750  9.900  9.925
29  9.950  9.975  9.975  9.975
30  2.950  3.025  7.000  7.025
31  3.350  3.750  7.000  7.500
32  6.900  7.600  8.500  9.200
33  4.600  4.700  5.150  5.450
34  8.250  8.350  8.850  9.100
35  5.150  6.050  7.925  9.000
36  8.000  9.000 10.000 10.000
38  8.850  9.975  9.975  9.975
39  1.950  3.750  6.025  8.050
40  4.875  4.875  8.025  8.050
41  4.975  4.975  6.575  7.925
42  2.500  2.525  3.350  3.775
43  3.550  3.550  3.575  3.600
44  5.075  7.050  9.975  9.975
45  7.000  7.000  8.000  8.450
46  0.000  0.025  0.550  0.550
47  7.000  7.700  8.400  9.000
48  1.500  2.500  4.525  5.475
49  0.050  0.075  3.025  3.025
50  2.500  3.000  5.000  6.000
51  7.975  8.975  9.950 10.000
52  2.900  3.125  6.250  6.275
53  3.900  3.900  6.800  6.875
54  0.025  3.750  6.250  9.950
55  6.000  6.000  7.000  8.000
56  3.000  3.000  3.000  3.600
57  0.125  0.125  3.000  3.050
58  2.900  4.000  5.000  6.100
59  2.200  3.000  3.700  4.600
60  0.050  0.050  1.100  1.100
61  0.000  0.000  0.000  0.000
62  4.925  6.100  8.425  8.425
63  5.400  5.425  7.825  8.425
64  2.275  3.750  6.250  8.075
65  5.125  6.775  7.450  7.500
66  6.025  6.050  7.500  9.050
67  6.100  6.700  7.300  7.650
68  8.500  9.200  9.700 10.000
69  2.500  2.675  2.675  2.725

[[2]]
[1] 67

[1] 6.346375

FuzzyStatTra documentation built on May 2, 2019, 10:59 a.m.