R code for analysing ordinal dataset of 234 Hong Kong preservice teachers' beliefs about proof and proving, using confirmatory factor analysis

These four labeled data sets are targeted at ordinal quantification. The goal of quantification is not to predict the label of each individual instance, but the distribution of labels in unlabeled sets of data.

With the scripts provided, you can extract CSV files from the UCI machine learning repository and from OpenML. The ordinal class labels stem from a binning of a continuous regression label.

We complement this data set with the indices of data items that appear in each sample of our evaluation. Hence, you can precisely replicate our samples by drawing the specified data items. The indices stem from two evaluation protocols that are well suited for ordinal quantification. To this end, each row in the files app_val_indices.csv, app_tst_indices.csv, app-oq_val_indices.csv, and app-oq_tst_indices.csv represents one sample.

Our first protocol is the artificial prevalence protocol (APP), where all possible distributions of labels are drawn with an equal probability. The second protocol, APP-OQ, is a variant thereof, where only the smoothest 20% of all APP samples are considered. This variant is targeted at ordinal quantification tasks, where classes are ordered and a similarity of neighboring classes can be assumed.

Usage

You can extract four CSV files through the provided script extract-oq.jl, which is conveniently wrapped in a Makefile. The Project.toml and Manifest.toml specify the Julia package dependencies, similar to a requirements file in Python.

Preliminaries: You have to have a working Julia installation. We have used Julia v1.6.5 in our experiments.

Data Extraction: In your terminal, you can call either

make

(recommended), or

julia --project="." --eval "using Pkg; Pkg.instantiate()"
julia --project="." extract-oq.jl

Outcome: The first row in each CSV file is the header. The first column, named "class_label", is the ordinal class.

Further Reading

Implementation of our experiments: https://github.com/mirkobunse/regularized-oq

Data for the numerical example.

We present a 47-year-long record of sea surface temperature (SST) derived from Sr/Ca and U/Ca analysis of a massive Porites coral which grew at ~4150 calendar years before present (B.P.) in Vanuatu (southwest tropical Pacific Ocean). Mean SST is similar in both the modern instrumental record and paleorecord, and both exhibit El Niño-Southern Oscillation (ENSO) frequency SST oscillations. However, several strong decadal-frequency cooling events and a marked modulation of the seasonal SST cycle, with power at both ENSO and decadal frequencies, are observed in the paleorecord, which are unprecedented in the modern record.

We propose a method for variable selection and basis learning for high-dimensional classification with ordinal responses. The proposed method extends sparse multiclass linear discriminant analysis, with the aim of identifying not only the variables relevant to discrimination but also the variables that are order-concordant with the responses. For this purpose, we compute for each variable an ordinal weight, where larger weights are given to variables with ordered group-means, and penalize the variables with smaller weights more severely. A two-step construction for ordinal weights is developed, and we show that the ordinal weights correctly separate ordinal variables from non-ordinal variables with high probability. The resulting sparse ordinal basis learning method is shown to consistently select either the discriminant variables or the ordinal and discriminant variables, depending on the choice of a tunable parameter. Such asymptotic guarantees are given under a high-dimensional asymptotic regime where the dimension grows much faster than the sample size. We also discuss a two-step procedure of post-screening ordinal variables among the selected discriminant variables. Simulated and real data analyses confirm that the proposed basis learning provides sparse and interpretable basis, as it mostly consists of ordinal variables. Supplementary materials for this article are available online.

Scenarios for the evaluation of the trial design.

In 1998, we were able to collect at the Bykovsky Peninsula more than 600 bones. Typically for permafrost regions, where fossil bones in summer rapidly emerge from frozen sediment and are delivered to the cliff foot by mud flows, most bones come from the shore and shallow coastal bars, mainly in the area of the Mamontovy Khayata cliff, and another site NW of it, provisionally called ”the Holocene Shore”. However, rather large amount of bones (145) was collected at the Mamontovy Khayata exposure itself. Among them, about 20 bones (location group ”a”) were discovered strictly in situ in the frozen silts and sands, and about 80 bones were found in the mud flows; the initial stratigraphic posi-tion of the latter could be reconstructed more or less precisely (location group ”b”). About 30 bones, listed under the last group, were found on the surface of fresh mud flows evidently related to certain baidzherakhs, or their closely ar-ranged groups, so the original position of these bones can be estimated within 2-4 meters of the baidzherakh height. The other bones, listed under the group ”b”, were found (sometimes in rather high concentrations) on the mud flows related to large exposed parts of the cliff, usually at the base of its upper steep part. The areas of these concentrations are labelled here as ”bone fields”; the possible altitude range of the original position of bones was usually estimated between the height of their occurrence (minimum) and the height of the cliff in this particular area (maximum). The bones referred to the location type ”c” have been found within the Mamontovy Khayata cliff, but their exact position remains unknown. Nearly 300 bones (group ”d”) were collected at the Mamontovy Khayata shore and on the more or less adjacent shallow bars emerging during the lower sea level conditions. About 150 bones were picked up at the ”Holocene Shore” (group ”e”). Finally, a few dozens of bones come from the other sites on the Bykovsky Peninsula (mostly NW from the Mamontovy Khayata), and a few specimens have been delivered from other locations in the Lena Delta (group ”f”).

Morphology often reflects ecology, enabling the prediction of ecological roles for taxa that lack direct observations such as fossils. In comparative analyses, ecological traits, like diet, are often treated as categorical, which may aid prediction and simplify analyses but ignores the multivariate nature of ecological niches. Futhermore, methods for quantifying and predicting multivariate ecology remain rare. Here, we ranked the relative importance of 13 food items for a sample of 88 extant carnivoran mammals, and then used Bayesian multilevel modeling to assess whether those rankings could be predicted from dental morphology and body size. Traditional diet categories fail to capture the true multivariate nature of carnivoran diets, but Bayesian regression models derived from living taxa have good predictive accuracy for importance ranks. Using our models to predict the importance of individual food items, the multivariate dietary niche, and the nearest extant analogs for a set of data-deficient extant and extinct carnivoran species confirms long-standing ideas for some taxa, but yields new insights about the fundamental dietary niches of others. Our approach provides a promising alternative to traditional dietary classifications. Importantly, this approach need not be limited to diet, but serves as a general framework for predicting multivariate ecology from phenotypic traits.

Replication Package for A Study on the Pythonic Functional Constructs' Understandability

This package contains several folders and files with code and data used in the study.

examples/
Contains the code snippets used as objects of the study, named as reported in Table 1, summarizing the experiment design.

RQ1-RQ2-files-for-statistical-analysis/
Contains three .csv files used as input for conducting the statistical analysis and drawing the graphs for addressing the first two research questions of the study. Specifically:

- ConstructUsage.csv contains the declared frequency usage of the three functional constructs object of the study. This file is used to draw Figure 4.
- RQ1.csv contains the collected data used for the mixed-effect logistic regression relating the use of functional constructs with the correctness of the change task, and the logistic regression relating the use of map/reduce/filter functions with the correctness of the change task.
- RQ1Paired-RQ2.csv contains the collected data used for the ordinal logistic regression of the relationship between the perceived ease of understanding of the functional constructs and (i) participants' usage frequency, and (ii) constructs' complexity (except for map/reduce/filter).

inter-rater-RQ3-files/
Contains four .csv files used as input for computing the inter-rater agreement for the manual labeling used for addressing RQ3. Specifically, you will find one file for each functional construct, i.e., comprehension.csv, lambda.csv, and mrf.csv, and a different file used for highlighting the reasons why participants prefer to use the procedural paradigm, i.e., procedural.csv.

Questionnaire-Example.pdf
This file contains the questionnaire submitted to one of the ten experimental groups within our controlled experiment. Other questionnaires are similar, except for the code snippets used for the first section, i.e., change tasks, and the second section, i.e., comparison tasks.

RQ2ManualValidation.csv
This file contains the results of the manual validation being done to sanitize the answers provided by our participants used for addressing RQ2. Specifically, we coded the behavior description using four different levels: (i) correct, (ii) somewhat correct, (iii) wrong, and (iv) automatically generated.

RQ3ManualValidation.xlsx
This file contains the results of the open coding applied to address our third research question. Specifically, you will find four sheets, one for each functional construct and one for the procedural paradigm. For each sheet, you will find the provided answers together with the categories assigned to them.

Appendix.pdf
This file contains the results of the logistic regression relating the use of map, filter, and reduce functions with the correctness of the change task, not shown in the paper.

FuncConstructs-Statistics.r
This file contains an R script that you can reuse to re-run all the analyses conducted and discussed in the paper.

FuncConstructs-Statistics.ipynb
This file contains the code to re-execute all the analysis conducted in the paper as a notebook.

Species: Quercus species; No of rings: 41; Age not determined; Pith: not applicable; Heartwood: complete; Sapwood: incomplete; Number of sapwood rings: 7; Last ring under bark: absent

This is a small dataset of various global indicators developed for use in a course teaching research methods at the Croft Institute for International Studies at the University of Mississippi. The data is ready to be directly imported into SPSS, Stata, or other statistical packages. A brief codebook includes descriptions of each variable, the indicator's reference year(s), and links to the original sources. The data is cross-sectional, country-level data centered on 2015 as the primary reference year. Some data come from the most recent election or averages from a handful of years. The dataset includes socioeconomic and political data drawn from sources and indicators from the World Bank, the UNDP, and International IDEA. It also includes popular indexes (and some key components) from Freedom House, Polity IV, the Economist's Democracy Index, the Heritage Foundation's Index of Economic Freedom, and the Fund for Peace's Fragile States Index. The dataset also includes various types of data (nominal, ordinal, interval, and ratio), useful for pedagogical examples of how to handle statistical data.

Motivated by a longitudinal oral health study, the Signal-Tandmobiel® study, a Bayesian approach has been developed to model misclassified ordinal response data. Two regression models have been considered to incorporate misclassification in the categorical response. Specifically, probit and logit models have been developed. The computational difficulties have been avoided by using data augmentation. This idea is exploited to derive efficient Markov chain Monte Carlo methods. Although the method is proposed for ordered categories, it can also be implemented for unordered ones in a simple way. The model performance is shown through a simulation-based example and the analysis of the motivating study.

Using livestock grazing as a tool to manage biomass and reduce fuel hazard has gained widespread popularity, but examples from across the globe demonstrate that it often yields mixed, context-dependent results. Grazing has potential to deliver practical solutions in systems where grazing reduces not only biomass but also reduces fuel hazard by altering vegetation connectivity or composition. We assessed the extent to which recent rainfall, rabbit and kangaroo grazing and recent and historic livestock grazing alters and accounts for variation in above-ground biomass, biomass composition and fuel hazard ratings across three broad communities in eastern Australia. We used nested linear models to assess biomass in three vertical vegetation strata, that matched the strata assessed in the Overall Fuel Hazard Assessment guide (i.e. litter/surface fuel; groundstorey vegetation/near surface fuel; and midstorey vegetation/elevated fuel) and Ordinal Logistic Regression to assess categorical f...

A model of apoptosis (aEARM) was calibrated to a dataset containing synthetic ordinal measurements and nominal (survival vs apoptosis) outcomes using PyDREAM. The posterior sample for the model parameters and log-posterior traces are included as .npy files. The .txt files show the Gelman Rubin metric for each of the model parameters. The data was generated using https://github.com/LoLab-VU/Opt2Q

We include the course syllabus used to teach quantitative research design and analysis methods to graduate Linguistics students using a blended teaching and learning approach. The blended course took place over two weeks and builds on a face to face course presented over two days in 2019. Students worked through the topics in preparation for a live interactive video session each Friday to go through the activities. Additional communication took place on Slack for two hours each week. A survey was conducted at the start and end of the course to ascertain participants' perceptions of the usefulness of the course. The links to online elements and the evaluations have been removed from the uploaded course guide.Participants who complete this workshop will be able to:- outline the steps and decisions involved in quantitative data analysis of linguistic data- explain common statistical terminology (sample, mean, standard deviation, correlation, nominal, ordinal and scale data)- perform common statistical tests using jamovi (e.g. t-test, correlation, anova, regression)- interpret and report common statistical tests- describe and choose from the various graphing options used to display data- use jamovi to perform common statistical tests and graph resultsEvaluationParticipants who complete the course will use these skills and knowledge to complete the following activities for evaluation:- analyse the data for a project and/or assignment (in part or in whole)- plan the results section of an Honours research project (where applicable)Feedback and suggestions can be directed to M Schaefer schaemn@unisa.ac.za

Description: The Core Insect Pitfall/Malaise Trapping program has been running since 2012. The novel method was first developed by Robert M Ewers. This provides not only insect specimens, but other invertebrates such as myriapods, collembola, arachnids, and occasionally provides vertebrates such as small rodents, snakes, and amphibians. All specimens and data is kept as part of the Core of the SAFE project. The majority of the data which has been exploited so far remains with the Coleoptera and Staphylinidae groups, while much of the preliminary field iding groupings are currently not used, the data remains to have serious as of yet untapped potential for future workers.Trap construction:Traps were based on a design combining pitfall, flight-interception, and malaise traps. Flying insects were directed either upwards into a "top" trap or downwards into a "bottom" trap. Components used in the construction of each trap are as follows:• 1 25cm diameter, 20cm depth, 4.5cm spout aperture Blue Plastic "top" funnel.• 1 20cm diameter, 20cm depth, 2cm spout aperture Blue Plastic "bottom" funnel.• 1 Xcm four pointed star cloth, acting as a malaise tent "director", held at approximately 90 degrees to the trap with clear fishing line.• 1 Xcm diameter, 90 degree grey plastic elbow pipe, one end modified to include appropriate teeth to screw collection bottle onto which.• 1 Xml "bottom" collection bottle, modified with mesh lined holes to allow water to escape to prevent overspilling.• 1 Xml "top" collection bottle, unmodified.• 1 77cm length cross-intersecting clear plastic (PVC) flight interception vane, supported by 4 (metal) 68.5cm aluminum poles.• 8 white plastic Zip-Ties.• 70% ethanol solution to fill the collection bottles.Trapping strategy:The pitfall style bottom trap are dug flush into the ground where possible and the hole preserved between trapping periods to limit catch bias associated with soil and leaf litter disturbance (Digweed et al. 1995). Once constructed, and occasion start time recorded, the traps are left for three days before collection with collection time also recorded.Samples are then stored in a chest freezer at -10 degC, before being taken to Maliau Basin research station and sorted by order and 70% ethanol before again being stored in freezers.Justifications:This combination of traps is implemented to target invertebrates of various morphology and behaviour to take full advantage of the sampling opportunity. Project: This dataset was collected as part of the following SAFE research project: Spatial scaling of beetle community diversity Funding: These data were collected as part of research funded by:

Sime Darby (Standard grant, SAFE - core data, na) This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.

Permits: These data were collected under permit from the following authorities:

Sabah Biodiversity Council (Research licence na)

XML metadata: GEMINI compliant metadata for this dataset is available here Files: This consists of 1 file: Core_insect_2011_2012_2017.xlsx Core_insect_2011_2012_2017.xlsx This file contains dataset metadata and 2 data tables:

Insect sorting (described in worksheet Insect_sorting) Description: Insect sorting 2011, 2012 and 2017 Number of fields: 28 Number of data rows: 2502 Fields:

TrapNo: Trap number (Field type: id) Fragment: Fragment number in the SAFE landscape (Field type: id) Plot: Insect rapping point (Field type: location) Top_Bottom: Position in the trap (Field type: categorical) DateColl: Date the trap was collected (Field type: date) TimeColl: Time the trap was collected (Field type: time) Coleoptera: Coleoptera abundance (Field type: abundance) Staphylinids: Staphylinids abundance (Field type: abundance) Formicidae: Formicidae abundance (Field type: abundance) Isoptera: Isoptera abundance (Field type: abundance) Others_Insects_Invertebrate: Other Inverts abundance (Field type: abundance) Spider: Spider abundance (Field type: abundance) Woodlice: Woodlice abundace (Field type: abundance) Centipide_Milipide: Centipide and milipide abundance (Field type: abundance) Lizard: Lizard abundance (Field type: abundance) Snake: Snake abundance (Field type: abundance) Mouse: Mouse abundance (Field type: abundance) Frog: Frog abundance (Field type: abundance) Worm: Worm abundance (Field type: abundance) Snail: snail abundance (Field type: abundance) Other_animal: Other animal abundance (Field type: abundance) WetWeight: Wet weight of sample (Field type: numeric) DateSort: Date of sample sorting (Field type: date) Sorter: Name of the sorter (Field type: comments) DateEnter1: Date data entered 1 (Field type: date) EnteredBy1: Name of data enterer (Field type: comments) DateEnter2: Date data entered (Field type: date) EnteredBy2: Name of data enterer (Field type: comments)

Order counts (described in worksheet Order_counts) Description: Order counts 2011 and 2012 Number of fields: 10 Number of data rows: 1977 Fields:

Site: Trap collection (Field type: location) Position: Litter sample collected from (Field type: categorical) DateColl: Date trap collected (Field type: date) N_days: Number of days (Field type: numeric) missing_data: Is there any missing data? (Field type: categorical) Coloptera: Coloptera (Field type: abundance) Staphylinid: Staphylinid (Field type: abundance) Formicidae: Formicidae (Field type: abundance) Isoptera: Isoptera (Field type: abundance) Other: Invertebrates (Field type: abundance) Date range: 2011-01-11 to 2018-11-07 Latitudinal extent: 4.5000 to 5.0700 Longitudinal extent: 116.7500 to 117.8200 Taxonomic coverage: All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.  -  Animalia  -  -  Annelida  -  -  Arthropoda  -  -  -  Centipide_Milipide  -  -  -  Insecta  -  -  -  -  Coleoptera  -  -  -  -  -  Staphylinidae  -  -  -  -  Blattodea  -  -  -  -  -  Isoptera  -  -  -  -  Hymenoptera  -  -  -  -  -  Formicidae  -  -  -  Arachnida  -  -  -  -  Araneae  -  -  -  Malacostraca  -  -  -  -  Isopoda  -  -  -  -  -  Woodlice  -  -  Chordata  -  -  -  Reptilia  -  -  -  -  Squamata  -  -  -  -  -  Snake  -  -  -  Mammalia  -  -  -  -  Rodentia  -  -  -  Amphibia  -  -  -  -  Anura  -  -  Mollusca  -  -  -  Gastropoda  -  Animalia  -  -  Annelida  -  -  Arthropoda  -  -  -  Centipide_Milipide  -  -  -  Insecta  -  -  -  -  Coleoptera  -  -  -  -  -  Staphylinidae  -  -  -  -  Blattodea  -  -  -  -  -  Isoptera  -  -  -  -  Hymenoptera  -  -  -  -  -  Formicidae  -  -  -  Arachnida  -  -  -  -  Araneae  -  -  -  Malacostraca  -  -  -  -  Isopoda  -  -  -  -  -  Woodlice  -  -  Chordata  -  -  -  Reptilia  -  -  -  -  Squamata  -  -  -  -  -  Snake  -  -  -  Mammalia  -  -  -  -  Rodentia  -  -  -  Amphibia  -  -  -  -  Anura  -  -  Mollusca  -  -  -  Gastropoda

Species: Quercus species; No of rings: 29; Age not determined; Pith: not applicable; Heartwood: absent; Sapwood: absent; Number of sapwood rings: 0; Last ring under bark: absent

Species: Quercus species; No of rings: 57; Age not determined; Pith: not applicable; Heartwood: absent; Sapwood: absent; Number of sapwood rings: 0; Last ring under bark: absent

Author: Marko Bohanec, Blaz Zupan
Source: UCI - 1997
Please cite: UCI

Car Evaluation Database
This database was derived from a simple hierarchical decision model originally developed for the demonstration of DEX (M. Bohanec, V. Rajkovic: Expert system for decision making. Sistemica 1(1), pp. 145-157, 1990.).

The model evaluates cars according to the following concept structure:

CAR           car acceptability
. PRICE         overall price
. . buying        buying price
. . maint        price of the maintenance
. TECH          technical characteristics
. . COMFORT       comfort
. . . doors       number of doors
. . . persons      capacity in terms of persons to carry
. . . lug_boot      the size of luggage boot
. . safety        estimated safety of the car

Input attributes are printed in lowercase. Besides the target concept (CAR), the model includes three intermediate concepts: PRICE, TECH, COMFORT. Every concept is in the original model related to its lower level descendants by a set of examples (for these examples sets see http://www-ai.ijs.si/BlazZupan/car.html).

The Car Evaluation Database contains examples with the structural information removed, i.e., directly relates CAR to the six input attributes: buying, maint, doors, persons, lug_boot, safety. Because of known underlying concept structure, this database may be particularly useful for testing constructive induction and structure discovery methods.

Changes with respect to car (1)

The ordinal variables are stored as ordered factors in this version.

Relevant papers:

M. Bohanec and V. Rajkovic: Knowledge acquisition and explanation for multi-attribute decision making. In 8th Intl Workshop on Expert Systems and their Applications, Avignon, France. pages 59-78, 1988.

M. Bohanec, V. Rajkovic: Expert system for decision making. Sistemica 1(1), pp. 145-157, 1990.

The data contains inequality measures at the municipality-level for 1892 and 1871, as estimated in the PhD thesis "Institutions, Inequality and Societal Transformations" by Sara Moricz. The data also contains the source publications: 1) tabel 1 from “Bidrag till Sverige official statistik R) Valstatistik. XI. Statistiska Centralbyråns underdåniga berättelse rörande kommunala rösträtten år 1892” (biSOS R 1892) 2) tabel 1 from “Bidrag till Sverige official statistik R) Valstatistik. II. Statistiska Centralbyråns underdåniga berättelse rörande kommunala rösträtten år 1871” (biSOS R 1871)

moricz_inequality_agriculture.csv

A UTF-8 encoded .csv-file. Each row is a municipality of the agricultural sample (2222 in total). Each column is a variable.

R71muncipality_id: a unique identifier for the municipalities in the R1871 publication (the municipality name can be obtained from the source data) R92muncipality_id: a unique identifier for the municipalities in the R1892 publication (the municipality name can be obtained from the source data) agriTop1_1871: an ordinal measure (ranking) of the top 1 income share in the agricultural sector for 1871 agriTop1_1892: an ordinal measure (ranking) of the top 1 income share in the agricultural sector for 1892 highestFarm_1871: a cardinal measure of the top 1 person share in the agricultural sector for 1871 highestFarm_1871: a cardinal measure of the top 1 person share in the agricultural sector for 1892

moricz_inequality_industry.csv

A UTF-8 encoded .csv-file. Each row is a municipality of the industrial sample (1328 in total). Each column is a variable.

R71muncipality_id: see above description R92muncipality_id: see above description indTop1_1871: an ordinal measure (ranking) of the top 1 income share in the industrial sector for 1871 indTop1_1892: an ordinal measure (ranking) of the top 1 income share in the industrial sector for 1892

moricz_R1892_source_data.csv

A UTF-8 encoded .csv-file with the source data. The variables are described in the adherent codebook moricz_R1892_source_data_codebook.csv.

Contains table 1 from “Bidrag till Sverige official statistik R) Valstatistik. XI. Statistiska Centralbyråns underdåniga berättelse rörande kommunala rösträtten år 1892” (biSOS R 1892). SCB provides the scanned publication on their website. Dollar Typing Service typed and delivered the data in 2015. All numerical variables but two have been checked. This is easy to do since nearly all columns should sum up to another column. For “Folkmangd” (population) the numbers have been corrected against U1892. The highest estimate of errors in the variables is 0.005 percent (0.5 promille), calculated at cell level. The two numerical variables which have not been checked is “hogsta_fyrk_jo“ and “hogsta_fyrk_ov“, as this cannot much be compared internally in the data. According to my calculations as the worst case scenario, I have measurement errors of 0.0043 percent (0.43 promille) in those variables.

moricz_R1871_source_data.csv

A UTF-8 encoded .csv-file with the source data. The variables are described in the adherent codebook moricz_R1871_source_data_codebook.csv.

Contains table 1 from “Bidrag till Sverige official statistik R) Valstatistik. II. Statistiska Centralbyråns underdåniga berättelse rörande kommunala rösträtten år 1871” (biSOS R 1871). SCB provides the scanned publication on their website. Dollar Typing Service typed and delivered the data in 2015. The variables have been checked for accuracy, which is feasible since columns and rows should sum. The variables that most likely carry mistakes are “hogsta_fyrk_al” and “hogsta_fyrk_jo”.