Resources

Validated scales, open datasets, R packages, social media simulations and learning materials, free to use with attribution.

Software, data, scales and teaching materials from my research, all free to use. Pick what you want to do or search, then open an entry for details, items, code and how to cite it.

Analyse data

R packages for transparent and robust analyses.

R packagespecrSpecification curve and multiverse analyses in R: set up, estimate, decompose and plot.CRAN · v1.1.0 · GPL-3My role: Author & maintainerR packageggmirtPublication-ready, customisable ggplot2 figures for item response theory models fitted with mirt.GitHub · v0.1.0 · GPL-3My role: Author & maintainer

Run experiments

Open-source social media simulations for experiments that are realistic and yet fully controlled.

Simulation platformTravelgramA fully functional mock-up of Instagram for studying social media behaviour.Open source · React · MITMy role: Author & maintainerSimulation platformThe Truman PlatformAn open-source social media simulation for experiments that feel real but are fully controlled.Open source · Cornell Social Media LabMy role: Contributor

Reuse data

Open data from my studies, usually with materials and analysis code. Use them for reanalyses, replications, meta-analyses or teaching. More on my OSF page. Interested in collaborating on an analysis? Get in touch.

Survey dataReplicating three key studies in privacy researchA preregistered survey that closely replicates studies on the privacy calculus, context collapse and the privacy paradox.Survey · preregistered · OSFMy role: Lead authorSurvey dataSocial norms and self-disclosure on Facebook and InstagramA preregistered survey (N = 863) on what people think others do, approve of and expect, and how much they disclose.Survey · N = 863 · OSFMy role: Lead authorExperimental dataCollective norm perceptions on social mediaAn online experiment: does the share of face-revealing posts in a feed shift perceived norms and disclosure intentions?Online experiment · MTurk · OSFMy role: Lead authorExperimental dataBehavioral adaptation to collective norms on social mediaParticipants used a realistic social media simulation for two days. We coded what they actually posted.Experiment · 2 days · TrumanMy role: Lead authorPanel dataThe Privacy Longitudinal StudyA five-wave panel survey (2014–2017), representative of the German population aged 16 and older.Panel · 5 waves · GESISMy role: Project managerSurvey dataPrivacy and self-disclosure on social network sitesA cross-sectional survey of 316 German Facebook users on privacy perceptions and disclosure management.Survey · N = 316 · OSFMy role: Lead authorDiary dataInstant messaging and mindfulnessA diary study: 211 participants answered short questionnaires on five consecutive evenings.Diary · 5 days · OSFMy role: Co-author

Measure something

Scales and knowledge tests that my colleagues and I developed and validated. You are free to use them without permission. Please cite the respective paper.

Knowledge testYouth Social Media Literacy Inventory YSMLIAn objective test of the knowledge and skills 9- to 13-year-olds need on social media.Ages 9–13 · 90, 48 or 18 items · IRTMy role: Co-developerKnowledge testOnline Privacy Literacy Scale OPLISA 20-item test of what people know about online privacy and data protection.20 items · 4 knowledge areas · DE & USMy role: Lead developerKnowledge testAlgorithm Literacy ScaleMeasures whether people notice where algorithms are used and what they know about how they work.2 × 11 items · IRTMy role: Co-developerSelf-report scaleCritical Media Literacy ScaleTwelve items on how critically and deliberately people use social media.12 items · 3 dimensions · 7-pointMy role: Lead developerSelf-report scaleOnline Privacy Concerns ScaleSeparates concerns about providers and institutions (vertical) from concerns about other people (horizontal).15 items · 5 factors · 7-pointMy role: DeveloperSelf-report scaleNeed for Privacy Questionnaire NFP-QMeasures how strongly people want informational, physical and interactional privacy.12 items · 3 dimensions · 5-pointMy role: Co-developerAssessment procedureDisclosure ManagementA within-person score that shows whether people share information they consider private less often.9 information types · within-person scoreMy role: Lead developerSelf-report scaleFacebook Addiction ScaleEighteen items on five symptoms of addictive Facebook use.18 items · 5 dimensions · 5-pointMy role: Lead developer

Learn & follow

Open course and workshop materials, and an easy way to keep up with new research in communication science.

TutorialsR course materialOpen handouts and video tutorials for learning R, from the basics to statistics and text analysis.GitHub · CCS AmsterdamMy role: Co-authorWorkshopWorkshop: Multiverse analysis in RSlides, handouts and exercises from a one-day workshop on multiverse analysis with specr.1 day · CC-BYMy role: Author & instructorWorkshopWorkshop: Introduction to Bayesian statisticsMaterials from a two-day workshop on Bayesian inference and regression models with brms.2 days · brms · CC-BYMy role: Author & instructorDashboardICA Journal TrackerThe latest, trending and most cited papers from 25 communication journals, updated automatically.25 journals · updated automaticallyMy role: Author & maintainer

R package

specr

Specification curve and multiverse analyses in R: set up, estimate, decompose and plot.

Website & docsGetting startedInference vignetteFunction referenceCRANGitHubWorkshop materials
My role
Author & maintainer
Version
1.1.0 on CRAN (October 2026)
Authors
Philipp K. Masur & Michael Scharkow
License
GPL-3

specr shows how different, equally defensible analytical choices affect a result within one dataset. You list the choices in setup() (outcomes, predictors, models, control variables, subsets), specr() fits all specifications, and summary() and plot() show the specification curve and which choices drive its variation.

Version 1.1.0 adds infer_curve() for inference on the curve as a whole, the third step proposed by Simonsohn et al. (2020). It replaces boot_null(), which produced p-values that were too small. If you used boot_null(), please recompute your results.

A specification curve does not give you a better estimate. It shows how much a result depends on analytical choices.

Install
install.packages("specr")

# Development version
# install.packages("devtools")
devtools::install_github("masurp/specr")
Example
library(specr)

specs <- setup(data = example_data,
               y = c("y1", "y2"),
               x = c("x1", "x2"),
               model = "lm",
               controls = c("c1", "c2"))

results <- specr(specs)
summary(results)
plot(results)
How to cite

R package

ggmirt

Publication-ready, customisable ggplot2 figures for item response theory models fitted with mirt.

GitHubIRT tutorial on this blogTutorial: 1PL–3PL modelsTutorial: graded response models
My role
Author & maintainer
Version
0.1.0 on GitHub (in development)
Extends
mirt (Chalmers) with ggplot2 graphics
License
GPL-3

mirt fits a wide range of IRT models, but its plots are hard to customise. ggmirt recreates the most important ones with ggplot2: item–person maps, item characteristic (trace) curves, item and test information curves, the scale characteristic curve, item and person fit, and conditional reliability. summaryPlot() combines several of them in one figure.

The package is still under development and not yet tested with every type of model. Contributions are welcome.

Install
# install.packages("devtools")
devtools::install_github("masurp/ggmirt")
Example
library(mirt)
library(ggmirt)

data <- sim_irt(500, 8, seed = 123)
mod <- mirt(data, 1, itemtype = "2PL", verbose = FALSE)

itempersonMap(mod)
tracePlot(mod, data)
summaryPlot(mod, adj_factor = 1.75)
How to cite
  • Masur, P. K. (2022). ggmirt: Plotting functions to extend the package “mirt” for IRT analyses (R-package, version 0.0.0.9000). https://github.com/masurp/ggmirt

Simulation platform

Travelgram

A fully functional mock-up of Instagram for studying social media behaviour.

DemoGitHub
My role
Author & maintainer
Version
3.1.0
Built with
React and JavaScript
License
MIT

Travelgram looks and works much like Instagram. Participants create a profile, scroll a feed and an explore page, open posts, like and comment, browse other profiles, share their own images, and save other people's posts to their profile.

This makes it possible to study behaviour on an image-based platform under controlled conditions.

How to cite

Simulation platform

The Truman Platform

An open-source social media simulation for experiments that feel real but are fully controlled.

DocumentationGitHub (Truman 2.0)GitHub (original)Demo
My role
Contributor
Developed by
Cornell Social Media Lab, led by Dominic DiFranzo; I contributed to the platform and used it in my research
Funding
National Science Foundation (IIS-1405634)
Used in
EatSnap.Love, e.g., in this experiment

Named after the film The Truman Show, the platform lets researchers build social media environments with exactly the features and affordances their study needs. Participants use a realistic, working site, while all other "users" are bots that post, comment and react according to the experimental condition.

This combines the ecological validity of real social media use with the control of an experiment. Truman 2.0 is the updated version; the documentation explains how to set up a study.

How to cite
  • DiFranzo, D., Bazarova, N. N., Yang, Q., Hui, W., Masur, P. K., Ozanne, M., Beichen, M., Sankaran, A., Zhao, P., Bae, I., & Han, E. (2024). The Truman Platform: A complete, open-source social media simulation.

Survey data

Replicating three key studies in privacy research

A preregistered survey that closely replicates studies on the privacy calculus, context collapse and the privacy paradox.

Data & code (OSF)PreregistrationPaper
My role
Lead author
Design
Preregistered survey; close replication of Krasnova et al. (2010), Vitak (2012), and Dienlin & Trepte (2015)
Measures
The constructs of the three original studies, e.g., privacy concerns, perceived benefits, self-disclosure, audience and network characteristics, and privacy behaviours
Includes
Data, analysis code, preregistration

Only 32.5% of the original effects replicated exactly, but 67.5% were significant and pointed in the original direction. The well-known negative link between privacy concerns and self-disclosure did not replicate and turned positive. Specification curve analyses showed that its strength depends on many analytical decisions.

Cite the data
  • Masur, P. K. & Ranzini, G. (2024). A survey study to replicate three key studies in communication privacy research. Open Science Framework. https://osf.io/gnb2t/
Publications
  • Masur, P. K., & Ranzini, G. (2025). Privacy Calculus, Privacy Paradox, and Context Collapse: A Replication of Three Key Studies in Communication Privacy Research. Journal of Communication, 76(2), 174–189. https://doi.org/10.1093/joc/jqaf007

Survey data

Social norms and self-disclosure on Facebook and Instagram

A preregistered survey (N = 863) on what people think others do, approve of and expect, and how much they disclose.

Data & code (OSF)PreregistrationPaper
My role
Lead author
Design
Preregistered cross-sectional survey
Sample
N = 863
Measures
Descriptive, injunctive and subjective norms; verbal and visual self-disclosure on Facebook and Instagram; perceived benefits and risks; critical media literacy; media-related self-reflection
Includes
Data, analysis code, preregistration

All three types of norms predicted self-disclosure positively and independently of each other, regardless of platform and type of disclosure, even when controlling for perceived benefits and risks. Critical media literacy and self-reflection neither predicted disclosure nor changed the norm–behaviour relationships.

Cite the data
  • Masur, P. K., Bazarova, N. N. & DiFranzo, D. J. (2023). A survey study on the relationship between social norms and self-disclosure on Facebook and Instagram. Open Science Framework. https://osf.io/ce7qb/
Publications
  • Masur, P. K., Bazarova, N. N., & DiFranzo, D. (2023). The impact of what others do, approve of, and expect you to do: An in-depth analysis of social norms and self-disclosure on social media. Social Media + Society, 9(1), 1–14. https://doi.org/10.1177/20563051231156401

Experimental data

Collective norm perceptions on social media

An online experiment: does the share of face-revealing posts in a feed shift perceived norms and disclosure intentions?

Data (OSF)Project (both studies)PreregistrationPaper (open access)
My role
Lead author
Design
Preregistered online experiment. Participants saw a snapshot (about 50 posts) of the simulated platform EatSnap.Love, in which 0%, 20% or 80% of the posts and profile pictures showed users' faces
Sample
Recruited via Amazon Mechanical Turk; N = 633 after exclusions
Measures
Perceived descriptive, injunctive and subjective norms; visual disclosure intentions; critical media literacy
Paper
Study 1 in Masur, DiFranzo & Bazarova (2021)

A critical mass of posts with visual disclosures shifted participants' norm perceptions, which in turn predicted their own intention to disclose. Critical media literacy moderated the effect of norms on disclosure intentions. Study 2 (next entry) tested whether this also holds for actual behaviour.

Cite the data
  • Masur, P. K., DiFranzo, D. J., & Bazarova, N. N. (2021). An experimental study on collective norm perceptions on social media. Open Science Framework. https://osf.io/yqhjr/
Publications
  • Masur, P. K., DiFranzo, D. J., & Bazarova, N. N. (2021). Behavioral Contagion on Social Media: Effects of Social Norms, Design Interventions, and Critical Media Literacy on Self-Disclosure. PLOS One, 16(7), e0254670. https://doi.org/10.1371/journal.pone.0254670

Experimental data

Behavioral adaptation to collective norms on social media

Participants used a realistic social media simulation for two days. We coded what they actually posted.

Data (OSF)Project (both studies)PreregistrationPaper (open access)Talk (ICA 2020)
My role
Lead author
Design
Preregistered 2 × 2 experiment on EatSnap.Love, built with the Truman Platform: 5% vs. 80% of posts with visual disclosure × privacy nudge vs. no nudge. Participants used the site for two days
Sample
Recruited via Amazon Mechanical Turk; N = 539 after exclusions
Data
Pre- and post-surveys (norm perceptions, critical media literacy, socio-demographics) plus participants' own posts, coded for visual identifiability
Paper
Study 2 in Masur, DiFranzo & Bazarova (2021)

The experiment showed behavioural contagion: a feed in which most users showed their faces shifted norm perceptions, and these in turn affected whether participants made themselves visually identifiable in their own posts. Neither critical media literacy nor the privacy nudge changed actual disclosure.

Cite the data
  • Masur, P. K., DiFranzo, D. J., & Bazarova, N. N. (2021). An experimental study on behavioral adaption to existing collective norms on social media. Open Science Framework. https://osf.io/sxp2f/
Publications
  • Masur, P. K., DiFranzo, D. J., & Bazarova, N. N. (2021). Behavioral Contagion on Social Media: Effects of Social Norms, Design Interventions, and Critical Media Literacy on Self-Disclosure. PLOS One, 16(7), e0254670. https://doi.org/10.1371/journal.pone.0254670

Panel data

The Privacy Longitudinal Study

A five-wave panel survey (2014–2017), representative of the German population aged 16 and older.

Data (GESIS)Privacy paradox analyses
My role
Project manager
Design
Longitudinal panel survey with five waves, 2014–2017
Sample
Representative of the German population aged 16+; recruited by telephone (dual-frame random digit dialling); self-administered questionnaires
Measures
Privacy concerns, online privacy literacy, self-disclosure, privacy and data protection behaviour, privacy violation experiences, social support, and more
Team
Sabine Trepte (PI), Philipp K. Masur, Tobias Dienlin; funded by the German Federal Ministry of Education and Research (Forum Privatheit)
Archive
GESIS datorium

The panel makes it possible to study within-person change in privacy-related attitudes and behaviours over three years. We used it to analyse the privacy paradox, how privacy violations shape concerns and disclosure, and the reciprocal effects between privacy literacy and data protection behaviour.

Cite the data
  • Trepte, S., Masur, P. K. & Dienlin, T. (2019). A longitudinal survey on privacy concerns, literacy, disclosure, support (5 waves, representative for the German population). GESIS Datorium. https://doi.org/10.7802/1937
Publications
  • Dienlin, T., Masur, P. K., & Trepte, S. (2021). A longitudinal analysis of the privacy paradox. New Media & Society, 25(5), 1043–1064. https://doi.org/10.1177/14614448211016316
  • Masur, P. K., & Trepte, S. (2021). Transformative or Not? How Privacy Violations Experiences Influence Online Privacy Concerns and Online Information Disclosure. Human Communication Research, 47(1), 49–74. https://doi.org/10.1093/hcr/hqaa012
  • Schäwel, J., Frener, R., Masur, P. K., & Trepte, S. (2021). Learning by doing oder doing by learning? Die Wechselwirkung zwischen Online-Privatheitskompetenz und Datenschutzverhalten [engl. Reciprocal effects between online privacy literacy and data protection behavior]. Medien & Kommunikationswissenschaft, 69(2), 221–246. https://doi.org/10.5771/1615-634X-2021-2-221
  • Trepte, S., Masur, P. K., Scharkow, M., & Dienlin, T. (2015). Privatheitsbedürfnisse verschiedener Kommunikationstypen on- und offline: Ergebnisse einer repräsentativen Studie zum Umgang mit persönlichen Inhalten [engl. Privacy needs of different communication types online and offline: Findings from a representative study on the handling of personal information]. Media Perspektiven, 250–257.

Survey data

Privacy and self-disclosure on social network sites

A cross-sectional survey of 316 German Facebook users on privacy perceptions and disclosure management.

Data & code (OSF)Paper (open access)
My role
Lead author
Design
Cross-sectional online survey
Sample
316 German users of social network sites
Measures
Privacy perception and disclosure frequency (status updates and private messages) for nine types of information, need for informational privacy, trust towards Facebook friends, use of friend lists, Facebook use
Includes
Data, R Markdown analysis, item formulations

Respondents managed their disclosures by taking both the communication channel and the type of information into account. Trust in their contacts and the use of privacy settings influenced disclosure management in status updates (one-to-many) but not in chats (one-to-one). See the Disclosure Management entry for the measure.

Cite the data
  • Masur, P. K. & Scharkow, M. (2016). A cross-sectional survey on privacy and self-disclosure on social media. Open Science Framework. https://osf.io/8bzxd/
Publications
  • Masur, P. K., & Scharkow, M. (2016). Disclosure management on social network sites: Individual privacy perceptions and user-directed privacy strategies. Social Media + Society, 2(1), 1–13. https://doi.org/10.1177/2056305116634368

Diary data

Instant messaging and mindfulness

A diary study: 211 participants answered short questionnaires on five consecutive evenings.

Data & code (OSF)Paper
My role
Co-author
Design
Diary study on five consecutive evenings
Sample
211 participants
Measures
Mindful use of instant messaging, autonomous motivation, positive affect, and stress related to instant messaging

Multilevel analyses showed that a mindful use of instant messaging went along with more positive affect and less stress, partly because it was linked to a more autonomous motivation to use instant messengers.

Cite the data
  • Bauer, A., Loy, L. S., Masur, P. K. & Schneider, F. M. (2018). A diary study on instant messaging and mindfulness. Open Science Framework. https://osf.io/nmf27/
Publications
  • Bauer, A. A., Loy, L. S., Masur, P. K., & Schneider, F. M. (2017). Mindful instant messaging: Mindfulness and autonomous motivation as predictors of well-being in smartphone communication. Journal of Media Psychology, 29(3), 159–165. https://doi.org/10.1027/1864-1105/a000225

Knowledge test

Youth Social Media Literacy Inventory YSMLI

An objective test of the knowledge and skills 9- to 13-year-olds need on social media.

ItemsPaperPreprintData & codePreregistration
My role
Co-developer
Measures
Social media literacy in six domains: advertising, cyberbullying, privacy, news, phishing, and media balance
Format
Objective knowledge and skill items (no self-reports)
Versions
90-item bank; validated short forms with 48 and 18 items
Method
Item response theory
Validation
n = 306 youth (9–13 years) in the US

Most media literacy measures ask people to rate their own skills. The YSMLI instead tests what young adolescents actually know and can do on social media. We reviewed the literature for core competencies, wrote a large item pool across six domains, refined it with expert reviews and cognitive pretests with children, and validated the final 90-item bank with item response theory.

Because all items are calibrated on the same scale, you can use the full inventory, one of the short forms, single domains, or parallel forms, for example to evaluate media literacy education with a pre-post design.

How to cite
  • Purington Drake, A., Masur, P. K., Bazarova, N. N., Zou, E. W., & Whitlock, J. (2023). The Youth Social Media Literacy Inventory: Development and Validation Using Item Response Theory. Journal of Children and Media, 17(4), 467–487. https://doi.org/10.1080/17482798.2023.2230493

Knowledge test

Online Privacy Literacy Scale OPLIS

A 20-item test of what people know about online privacy and data protection.

WebsiteScale & manual (PDF, German)PaperPreprint: revised versionsBlog post
My role
Lead developer
Measures
Online privacy literacy in four knowledge areas: institutional practices, technical aspects of data protection, data protection law, and data protection strategies
Items
20 knowledge questions, 5 per area
Model
Bifactor model; the global factor reflects online privacy literacy
Validation
Quota sample of German internet users, N = 1,945
New versions
16- and 12-item versions validated for Germany and the US (2026 preprint)

We understand online privacy literacy as a combination of factual or declarative ("knowing that") and procedural ("knowing how") knowledge about online privacy and data protection. OPLIS was developed from an initial pool of 113 knowledge questions in three consecutive studies. The global factor correlated with people's own assessment of their literacy and predicted whether they used various data protection measures.

We recently revisited the scale and validated shorter 16- and 12-item versions for Germany and the US. The original scale and its manual are available on oplis.de.

How to cite
  • Masur, P. K., Teutsch, D., & Trepte, S. (2017). Entwicklung und Validierung der Online-Privatheitskompetenzskala (OPLIS) [engl. Development and validation of the online privacy literacy scale]. Diagnostica, 63(4), 256–268. https://doi.org/10.1026/0012-1924/a000179
  • Masur, P. K., Quinn, K., & Epstein, D. (2026). Revisiting the Online Privacy Literacy Scale (OPLIS): Validating a 16- and 12-Item Version for Germany and the US. SocArXiv. Retrieved from osf.io/preprints/socarxiv/48gh7_v1

Knowledge test

Algorithm Literacy Scale

Measures whether people notice where algorithms are used and what they know about how they work.

Paper & items (supplement)
My role
Co-developer
Measures
Algorithm literacy with two related dimensions: (1) awareness of algorithm use and (2) knowledge about algorithms
Items
11 per dimension; the items are in the paper's online supplement
Method
Item response theory
Validation
Study 1: N = 331 (46 items); Study 2: N = 1,041 German internet users

As algorithmic decision-making becomes part of everyday internet use, being able to recognise and understand algorithms is a key skill. We tested 46 items in a first study, reduced them to a pool of 32, and tested these with 1,041 German internet users. The final scale has 11 items each for algorithm awareness and knowledge.

Both subscales correlated positively with participants' self-reported coding skills and predicted how they handled algorithmic curation in three test scenarios.

How to cite
  • Dogruel, L., Masur, P. K., & Jöckel, S. (2022). Development and validation of an algorithm literacy scale for Internet users. Communication Methods & Measures, 16(2), 115–133. https://doi.org/10.1080/19312458.2021.1968361

Self-report scale

Critical Media Literacy Scale

Twelve items on how critically and deliberately people use social media.

Paper (open access)Online supplement
My role
Lead developer
Dimensions
Critical information literacy, anticipatory reflection, deliberate use
Items
12 (4 per dimension); the deliberate use items are reverse-coded
Response
1 = Not at all like me to 7 = Very much like me
Validity
Good factorial validity and reliability (see the factor model below and the online supplement)

The scale captures three facets of critical media literacy on social media: judging information critically, reflecting on the possible consequences of one's own posts, and using social media deliberately instead of impulsively. We developed it to study whether critical media literacy protects people from uncritically adopting the disclosure norms they see on social media.

Items 12

Now, we want you to think about your own social media use in general. For each of the statements below, please indicate to what extent the statement is characteristic of you.

Critical information literacy
  • I can determine the credibility of information shared on social media.
  • I can easily form my own opinions on a post despite how popular it may be.
  • I manage to protect myself against the risks related to social media use.
  • I can understand and interpret social media content from political, economic, and social perspectives.
Anticipatory reflection
  • Before I post something on social media, I consider possible consequences.
  • I consider whether my comments or posts on social media could affect others’ thoughts and feelings.
  • I generally think about what people should and should not be posting on social media.
  • I always consider the consequences before I take action on social media.
Deliberate use
  • I typically share my opinions by posting comments on social media.*
  • Over the years I’ve done some pretty stupid things on social media.*
  • Occasionally I act first and think later on social media.*
  • I often do things on the spur of the moment in social media.*

Response options 1 = Not at all like me to 7 = Very much like me

* Reverse-coded.

Confirmatory factor model of the Critical Media Literacy Scale with three correlated factors
Factor model
How to cite
  • Masur, P. K., DiFranzo, D. J., & Bazarova, N. N. (2021). Behavioral Contagion on Social Media: Effects of Social Norms, Design Interventions, and Critical Media Literacy on Self-Disclosure. PLOS One, 16(7), e0254670. https://doi.org/10.1371/journal.pone.0254670

Self-report scale

Online Privacy Concerns Scale

Separates concerns about providers and institutions (vertical) from concerns about other people (horizontal).

Validation details (PDF)BookAbout the book
My role
Developer
Structure
Two second-order factors. Vertical: website providers, institutions. Horizontal: information access, information sharing, identity theft
Items
15 (3 per first-order factor)
Response
1 = not at all concerned to 7 = very concerned
Validation
German and US samples

Privacy concerns are often measured as one general worry. This scale distinguishes vertical concerns, which target website and app providers, public agencies and intelligence services, from horizontal concerns, which target other people. I developed it for my book Situational Privacy and Self-Disclosure and validated it with a German and a US sample.

Items 15

How concerned are you about…

Vertical privacy concerns: website providers
  • …website or app providers recording and using your surfing behavior?
  • …website or app providers sharing your data with unknown third parties?
  • …website or app providers tracking your online behavior and thereby getting information about you?
Vertical privacy concerns: institutions
  • …institutions, public agencies, or intelligence services monitoring your online communication?
  • …not having insight into what institutions, public agencies, or intelligence services do with your data?
  • …institutions, public agencies, or intelligence services collecting and analyzing the data that you share on the Internet?
Horizontal privacy concerns: information access
  • …other people getting information about you without your consent?
  • …other people finding information about you online?
  • …other people search information spying on you on the Internet?
Horizontal privacy concerns: information sharing
  • …other people spreading information about you without your knowledge?
  • …other people sharing information about you (e.g., photos, address, contact information…) with other people without your consent?
  • …other people publishing information about you (e.g., photos, address, contact information…) on the Internet?
Horizontal privacy concerns: identity theft
  • …people on the Internet not being who the claim to be?
  • …an unknown person claiming to be you on the Internet?
  • …someone misusing your identity on the Internet?

Response options 1 = not at all concerned to 7 = very concerned

How to cite

Self-report scale

Need for Privacy Questionnaire NFP-Q

Measures how strongly people want informational, physical and interactional privacy.

Items (PDF)Encyclopedia entry
My role
Co-developer
Dimensions
Informational, physical, and interactional need for privacy
Items
12 (4 per dimension)
Response
1 = Do not agree at all to 5 = Totally agree
Validation
Representative German sample, N = 2,073 (2015)

People differ in how much privacy they want. The questionnaire captures this need on three dimensions: informational privacy (how much others know about oneself), physical privacy (physical closeness to others), and interactional privacy (talking about oneself and private matters).

Items 12
Informational privacy
  • I prefer that not much is known about me.
  • I prefer to remain unrecognized.
  • I do not want my personal data to be publicly available.
  • Not everyone needs to know everything about me.
Physical privacy
  • I do not like to stand in a densely packed crowd.
  • I do not like to sit next to a stranger on the bus or tube.
  • I do not like strangers to come too physically close to me.
  • I do not like it when other people enter my house or my room unannounced.
Interactional privacy
  • It is hard for me to talk about myself.
  • I feel uncomfortable when others share private information about their lives.
  • There are many things about me that I do not want to discuss with other people.
  • I prefer not to talk about personal things unless my conversational partner brings them up first.

Response options 1 = Do not agree at all to 5 = Totally agree

How to cite

Assessment procedure

Disclosure Management

A within-person score that shows whether people share information they consider private less often.

Paper (open access)R codeItem formulations (PDF)Data
My role
Lead developer
Assesses
How strongly a person's disclosure frequency follows their own privacy perceptions
Items
9 types of information, each rated twice: how private it is and how often it is shared
Score
Within-person correlation between privacy perception and disclosure frequency
Context
Any channel, e.g., status updates, private messages, or WhatsApp chats
Validation
Online survey with 316 German users of social network sites

Disclosure management is a privacy strategy based on data parsimony. People do not simply stop sharing. They share information they perceive as private less often than information they perceive as harmless. Because people consider different things private, the strategy can only be measured within each person.

First, ask respondents how private they consider nine types of information and how often they share each of them in the context you study. Then compute each respondent's correlation between the two ratings. The correlation is usually negative. You can reverse it so that higher scores mean stronger disclosure management, as we did in the paper. The R code shows the full procedure.

Items 18
Privacy perception

What would you say, how private are the following things?

  • My personal feelings
  • My deepest fears and concerns
  • Things that I am proud of
  • Details from my relationships with other people
  • My hobbies or my work
  • Changes in my life (e.g., marriage, birth,…)
  • My political, ethical, or religious opinion
  • What I like or enjoy (e.g., music, books, movies)
  • Photos of myself

Response options 1 = Not at all private to 5 = Very private

Disclosure frequency

How often do you share the following things in [private messages on Facebook / WhatsApp chats / status updates on Facebook…]?

  • My personal feelings
  • My deepest fears and concerns
  • Things that I am proud of
  • Details from my relationships with other people
  • My hobbies or my work
  • Changes in my life (e.g., marriage, birth,…)
  • My political, ethical, or religious opinion
  • What I like or enjoy (e.g., music, books, movies)
  • Photos of myself

Response options 1 = Never, 2 = Very rarely, 3 = Rarely, 4 = Sometimes, 5 = Often, 6 = Very often

How to cite
  • Masur, P. K., & Scharkow, M. (2016). Disclosure management on social network sites: Individual privacy perceptions and user-directed privacy strategies. Social Media + Society, 2(1), 1–13. https://doi.org/10.1177/2056305116634368

Self-report scale

Facebook Addiction Scale

Eighteen items on five symptoms of addictive Facebook use.

PaperPDF (ResearchGate)
My role
Lead developer
Dimensions
Loss of control, tolerance, withdrawal syndrome, negative consequences for social relations, negative consequences for work and performance
Items
18
Response
5-point scale from Strongly disagree to Strongly agree
Validity
Factorial validity and reliability are reported in the paper (factor model below)

The scale transfers typical symptoms of behavioural addiction to Facebook use. We developed it to study how the satisfaction of intrinsic needs and Facebook-specific motives explain addictive behaviour on Facebook.

Items 18
Loss of control
  • While surfing on Facebook, I often think “Just a few more minutes” and then I cannot stop anyway
  • I often spent more time on Facebook then I intended
  • I often tried to cut down my time on Facebook, but without success
  • I sometimes wonder how much time passes when I am using Facebook
Tolerance
  • By now, I am spending more time on Facebook then at the beginning
  • The amount of time that I spent on Facebook has continuously increased
  • My desire to spent time on Facebook has continuously increased
Withdrawal syndrome
  • If I am off Facebook for a longer period of time, I feel nervous
  • If I am off Facebook for a longer period of time (e.g., because of vacation), I feel anxious and want to know what happens on Facebook
  • My thoughts often rotate around Facebook, even when I am not online
  • When I am not online I often ask myself what happens on Facebook
Negative consequences for social relations
  • Since I am using Facebook I do less with my friends “offline”
  • People who are important to me tell me that I changed for worse since I am using Facebook
  • People who are important to me complain that I spent too much time on Facebook
  • Since I am using Facebook some friends have withdrawn from me
Negative consequences for work and performance
  • I am regularly on Facebook while being at work/school/university
  • My concentration at work/school/university suffers because I am on Facebook
  • I often neglect duties because of Facebook

Response options 5-point Likert-type scale from Strongly disagree to Strongly agree

Confirmatory factor model of the Facebook Addiction Scale with five correlated factors
Factor model
How to cite
  • Masur, P. K., Reinecke, L., Ziegele, M., & Quiring, O. (2014). The interplay of intrinsic need satisfaction and Facebook specific motives in explaining addictive behavior on Facebook. Computers in Human Behavior, 39, 376–386. https://doi.org/10.1016/j.chb.2014.05.047

Tutorials

R course material

Open handouts and video tutorials for learning R, from the basics to statistics and text analysis.

GitHubGetting started with RR & statistics on this blog
My role
Co-author
Maintained by
The Computational Communication Science group in Amsterdam (CCS Amsterdam)
Topics
R basics; data wrangling with the tidyverse; frequentist and Bayesian statistics; power analyses and simulations; test theory and factor analysis; text analysis (tidytext, quanteda, large language models); data collection

An open collection of R tutorials that we use in our own teaching. Each tutorial is a handout with code, and many come with a video. They work well for self-study and for teaching.

Workshop

Workshop: Multiverse analysis in R

Slides, handouts and exercises from a one-day workshop on multiverse analysis with specr.

Workshop websiteGitHub
My role
Author & instructor
Format
One-day workshop: theory, discussion, and hands-on tutorials
Requirements
R and RStudio installed; some experience with R
License
CC-BY

The workshop covers (1) researcher degrees of freedom and the problem of undisclosed flexibility in data analysis, (2) how multiverse approaches can help to establish better practices and more transparency, including their pitfalls, and (3) practical sessions on running multiverse and specification curve analyses with specr.

Workshop

Workshop: Introduction to Bayesian statistics

Materials from a two-day workshop on Bayesian inference and regression models with brms.

Workshop websiteGitHub
My role
Author & instructor
Format
Two-day workshop with slides and practical exercises in R
Requirements
Basic R and tidyverse skills, descriptive statistics, frequentist inference, and regression models
License
CC-BY

An in-depth introduction to Bayesian data analysis: Bayes' theorem, Bayesian inference, and (generalised) linear models with the R package brms. You learn how to specify models and priors, interpret the results, and draw probabilistic inferences from posterior distributions instead of relying on null hypothesis tests alone.

Dashboard

ICA Journal Tracker

The latest, trending and most cited papers from 25 communication journals, updated automatically.

Open the dashboardGitHub
My role
Author & maintainer
Covers
25 communication science journals, e.g., Journal of Communication, New Media & Society, Political Communication, Media Psychology
Updates
Automatically every week (GitHub Actions)

A lightweight dashboard that collects new papers from the main journals of the field and shows the latest, trending and most cited ones. An easy way to keep up with communication science.