GENERAL INFORMATION

Title of Dataset: A dataset from a survey investigating disciplinary differences in data citation

Date of data collection: January to March 2022

Collection instrument: SurveyMonkey

Funding: Alfred P. Sloan Foundation

SHARING/ACCESS INFORMATION

Licenses/restrictions placed on the data: These data are available under a CC BY 4.0 license

Links to publications that cite or use the data:

Gregory, K., Ninkov, A., Ripp, C., Peters, I., & Haustein, S. (2022). Surveying practices of data citation and reuse across disciplines. Proceedings of the 26th International Conference on Science and Technology Indicators. International Conference on Science and Technology Indicators, Granada, Spain. https://doi.org/10.5281/ZENODO.6951437

Gregory, K., Ninkov, A., Ripp, C., Roblin, E., Peters, I., & Haustein, S. (2023). Tracing data:
A survey investigating disciplinary differences in data citation. Zenodo. https://doi.org/10.5281/zenodo.7555266

DATA & FILE OVERVIEW

File List

• Filename: MDCDatacitationReuse2021Codebook.pdf
  Codebook
• Filename: MDCDataCitationReuse2021surveydata.csv
  Dataset format in csv
• Filename: MDCDataCitationReuse2021surveydata.sav
  Dataset format in SPSS
• Filename: MDCDataCitationReuseSurvey2021QNR.pdf
  Questionnaire

Additional related data collected that was not included in the current data package: Open ended questions asked to respondents

METHODOLOGICAL INFORMATION

Description of methods used for collection/generation of data:

The development of the questionnaire (Gregory et al., 2022) was centered around the creation of two main branches of questions for the primary groups of interest in our study: researchers that reuse data (33 questions in total) and researchers that do not reuse data (16 questions in total). The population of interest for this survey consists of researchers from all disciplines and countries, sampled from the corresponding authors of papers indexed in the Web of Science (WoS) between 2016 and 2020.

Received 3,632 responses, 2,509 of which were completed, representing a completion rate of 68.6%. Incomplete responses were excluded from the dataset. The final total contains 2,492 complete responses and an uncorrected response rate of 1.57%. Controlling for invalid emails, bounced emails and opt-outs (n=5,201) produced a response rate of 1.62%, similar to surveys using comparable recruitment methods (Gregory et al., 2020).

Methods for processing the data:

Results were downloaded from SurveyMonkey in CSV format and were prepared for analysis using Excel and SPSS by recoding ordinal and multiple choice questions and by removing missing values.

Instrument- or software-specific information needed to interpret the data:

The dataset is provided in SPSS format, which requires IBM SPSS Statistics. The dataset is also available in a coded format in CSV. The Codebook is required to interpret to values.

DATA-SPECIFIC INFORMATION FOR: MDCDataCitationReuse2021surveydata

Number of variables: 94

Number of cases/rows: 2,492

Missing data codes: 999 Not asked

Refer to MDCDatacitationReuse2021Codebook.pdf for detailed variable information.

Context

A fictional telco company that provided home phone and Internet services to 7043 customers in California in Q3.

Data Description 7043 observations with 33 variables

CustomerID: A unique ID that identifies each customer.

Count: A value used in reporting/dashboarding to sum up the number of customers in a filtered set.

Country: The country of the customer’s primary residence.

State: The state of the customer’s primary residence.

City: The city of the customer’s primary residence.

Zip Code: The zip code of the customer’s primary residence.

Lat Long: The combined latitude and longitude of the customer’s primary residence.

Latitude: The latitude of the customer’s primary residence.

Longitude: The longitude of the customer’s primary residence.

Gender: The customer’s gender: Male, Female

Senior Citizen: Indicates if the customer is 65 or older: Yes, No

Partner: Indicate if the customer has a partner: Yes, No

Dependents: Indicates if the customer lives with any dependents: Yes, No. Dependents could be children, parents, grandparents, etc.

Tenure Months: Indicates the total amount of months that the customer has been with the company by the end of the quarter specified above.

Phone Service: Indicates if the customer subscribes to home phone service with the company: Yes, No

Multiple Lines: Indicates if the customer subscribes to multiple telephone lines with the company: Yes, No

Internet Service: Indicates if the customer subscribes to Internet service with the company: No, DSL, Fiber Optic, Cable.

Online Security: Indicates if the customer subscribes to an additional online security service provided by the company: Yes, No

Online Backup: Indicates if the customer subscribes to an additional online backup service provided by the company: Yes, No

Device Protection: Indicates if the customer subscribes to an additional device protection plan for their Internet equipment provided by the company: Yes, No

Tech Support: Indicates if the customer subscribes to an additional technical support plan from the company with reduced wait times: Yes, No

Streaming TV: Indicates if the customer uses their Internet service to stream television programing from a third party provider: Yes, No. The company does not charge an additional fee for this service.

Streaming Movies: Indicates if the customer uses their Internet service to stream movies from a third party provider: Yes, No. The company does not charge an additional fee for this service.

Contract: Indicates the customer’s current contract type: Month-to-Month, One Year, Two Year.

Paperless Billing: Indicates if the customer has chosen paperless billing: Yes, No

Payment Method: Indicates how the customer pays their bill: Bank Withdrawal, Credit Card, Mailed Check

Monthly Charge: Indicates the customer’s current total monthly charge for all their services from the company.

Total Charges: Indicates the customer’s total charges, calculated to the end of the quarter specified above.

Churn Label: Yes = the customer left the company this quarter. No = the customer remained with the company. Directly related to Churn Value.

Churn Value: 1 = the customer left the company this quarter. 0 = the customer remained with the company. Directly related to Churn Label.

Churn Score: A value from 0-100 that is calculated using the predictive tool IBM SPSS Modeler. The model incorporates multiple factors known to cause churn. The higher the score, the more likely the customer will churn.

CLTV: Customer Lifetime Value. A predicted CLTV is calculated using corporate formulas and existing data. The higher the value, the more valuable the customer. High value customers should be monitored for churn.

Churn Reason: A customer’s specific reason for leaving the company. Directly related to Churn Category.

Source This dataset is detailed in: https://community.ibm.com/community/user/businessanalytics/blogs/steven-macko/2019/07/11/telco-customer-churn-1113

Downloaded from: https://community.ibm.com/accelerators/?context=analytics&query=telco%20churn&type=Data&product=Cognos%20Analytics

There are several related datasets as documented in: https://community.ibm.com/community/user/businessanalytics/blogs/steven-macko/2018/09/12/base-samples-for-ibm-cognos-analytics

The data were preprocessed by using IBM SPSS 19.0 software to conduct descriptive statistical and correlation analyses on 540 participants. The community dataset was complete and without missing values. Network model estimation, establishment, and centrality index calculation were then performed. The network was estimated using the EBICglasso function in the qgraph software package (Version 1.9.3; Epskamp et al., 2012) in R (Version 4.1.3, RCore Team, 2022). The Glasso network was used to calculate a partial correlation network, in which the relationship between symptoms can explain all other relationships in the model; each item is represented as a node, and the association between items is referred to as the edge.

The dataset was generated within the research project Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions (CATCH-EyoU) funded by European Union, Horizon 2020 Programme - Grant Agreement No 649538 http://www.catcheyou.eu/. The data set consists of: 1 data file saved in .sav format “CATCH-EyoU Processes in Youth’s Construction of Active EU Citizenship Cross-national Wave 1 Questionnaires Italy, Sweden, Germany, Greece, Portugal, Czech Republic, UK, and Estonia - EXTRACT.sav” 1 README file The file was generated through IBM SPSS software. Discrete missing values: 88, 99. The .sav file (SPSS) can be processed using “R” (library “foreign”): https://cran.r-project.org This dataset relates to following paper: Ekaterina Enchikova, Tiago Neves, Sam Mejias, Veronika Kalmus, Elvira Cicognani, Pedro Ferreira (2019) Civic and Political Participation of European Youth: fair measurement in different cultural and social contexts. Frontiers in Education. Data Set Contact Person: Ekaterina Enchikova [UP-CIIE]; mail: enchicova@gmail.com Data Set License: this data set is distributed under a Creative Commons Attribution (CC-BY) http://creativecommons.org/licenses

The Structural Equation Modeling (SEM) software market is experiencing robust growth, driven by increasing adoption across diverse sectors like education, healthcare, and the social sciences. The market's expansion is fueled by the need for sophisticated statistical analysis to understand complex relationships between variables. Researchers and analysts increasingly rely on SEM to test theoretical models, assess causal relationships, and gain deeper insights from intricate datasets. While the specific market size for 2025 isn't provided, a reasonable estimate, considering the growth in data analytics and the increasing complexity of research questions, places the market value at approximately $500 million. A Compound Annual Growth Rate (CAGR) of 8% seems plausible, reflecting steady but not explosive growth within a niche but essential software market. This CAGR anticipates continued demand from academia, government agencies, and market research firms. The market is segmented by software type (commercial and open-source) and application (education, medical, psychological, economic, and other fields). Commercial software dominates the market currently, due to its advanced features and professional support, however the open-source segment shows strong potential for growth, particularly within academic settings and amongst researchers with limited budgets. The competitive landscape is relatively concentrated with established players like LISREL, IBM SPSS Amos, and Mplus offering comprehensive solutions. However, the emergence of Python-based packages like semopy and lavaan demonstrates an ongoing shift towards flexible and programmable SEM software, potentially increasing market competition and innovation in the years to come. Geographic distribution shows North America and Europe currently holding the largest market share, with Asia-Pacific emerging as a key growth region due to increasing research funding and investment in data science capabilities. The sustained growth of the SEM software market is expected to continue throughout the forecast period (2025-2033), largely driven by the rising adoption of advanced analytical techniques within research and businesses. Factors limiting market growth include the high cost of commercial software, the steep learning curve associated with SEM techniques, and the availability of alternative statistical methods. However, increased user-friendliness of software interfaces, alongside the growing availability of online training and resources, are expected to mitigate these restraints and expand the market's reach to a broader audience. Continued innovation in SEM software, focusing on improved usability and incorporation of advanced features such as handling of missing data and multilevel modeling, will contribute significantly to the market's future trajectory. The development of cloud-based solutions and seamless integration with other analytical tools will also drive future market growth.

Population and sampleTo find participants for the survey, this study drew from the 4,761 publicly listed members of the online group Awesome Assistants. As a result, the population was all young film/TV professionals, while the sample was the selected members of Awesome Assistants. On its Facebook page, Awesome Assistants allows film/TV professionals to post job openings and work-related questions. The author eliminated respondents younger than 18 or older than 35, and did not include moderators of Awesome Assistants. Members of the group work in the film/TV industry with either an assistant title or performing assistant duties, such as answering phones and running errands. The author randomly selected and contacted 500 individuals from this online group. The author also utilised two follow-up messages to improve the response rate.InstrumentationIn order to collect data from the sample, the author used the Career Decision Self-Efficacy Scale-Short Form (CDSES-SF).Data collection processTo distribute the survey to potential participants, the author sent a letter to the Awesome Assistants moderators to confirm their support. After, the author uploaded a message of informed consent and the survey to Qualtrics, then sent subjects a link to complete the study. Subjects received messages via Facebook Messenger, LinkedIn, or email, based on the contact information available. Once subjects completed the survey, a debriefing form invited them to enter a raffle for one of four $25 Amazon gift cards. After four weeks, the links expired. The author omitted surveys in which the subject did not answer at least one item from each subscale on the CDSES-SF. If respondents did not answer all items in a subscale, the author took the average of the completed questions. Additionally, the author eliminated subjects who fell outside of the target age range, as well as those who did not provide their age or number of contacts in the film/TV industry. Out of the 267 unique responses, the author analyzed 226 subjects as a result.Statistical Analysis ProceduresMuch like data collection, the author ensured that the statistical analysis process was legitimate and insightful. Next, the author entered data into IBM Statistical Product and Service Solutions (SPSS) Statistics 27 and determined what data should be coded as missing. Due to the assumption of linearity not being met by the data, the author used Spearman’s rho instead of a Pearson product-moment correlation. The author declared the results statistically significant if p < .05.

Na formação das sociedades, foram estabelecidos papéis femininos e masculinos. Apesar da conquista de direitos e da busca por igualdade de gênero, a mulher, ao se tornar mãe, enfrenta uma série de desafios, especialmente ao buscar a formação continuada. Este estudo teve como objetivo geral analisar o ingresso e a permanência de mães-estudantes no ensino superior e, especificamente, (1) identificar os desafios enfrentados durante a formação acadêmica, (2) explorar as perspectivas pós-formação e, (3) comparar a autoeficácia e a avaliação de vida acadêmica entre mães e não-mães. A pesquisa, de método misto, contou com 151 mulheres, divididas em mães e não-mães, e utilizou um questionário via Google Forms, além das escalas de Avaliação da Vida Acadêmica e Autoeficácia na Formação Superior. A análise foi feita com o IRAMUTEQ (CHD) e o IBM SPSS (testes de tendência central, Shapiro-Wilk e Mann-Whitney). Os resultados apontaram menor autoeficácia acadêmica (p=0,032) e gestão acadêmica (p=0,002) entre mães, sem diferença significativa na avaliação de vida acadêmica entre os grupos. As maiores dificuldades foram relacionadas à conciliação das demandas familiares e universitárias, destacando a falta de rede de apoio. Já as perspectivas pós-formação focaram na busca por especializações e pós-graduações.