alermann-henestrosa/Data_Descriptor_HMC
alermann-henestrosa/Data_Descriptor_HMC
0
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Added some random stuff from the LEK application

Browse Source
This commit is contained in:
Nora Wickelmaier 2025-09-26 15:21:39 +02:00
parent e929e16b98
commit 81a7f066fe
3 changed files with 112 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

54
manuscript.Rnw
View File

@ -19,8 +19,8 @@
\begin{abstract}
Since the emergence of large language models (LLMs) in 2022, generative AI has rapidly expanded into mainstream applications, leading to the integration of Apple Intelligence into customer devices in 2024. This integration into personal technology marks a significant shift, bringing advanced AI capabilities into everyday devices and making them accessible to private individuals.
Thus, the use of generative AI--consciously or unconsciously--along with interaction through LLM-powered (voice) assistants and engagement with AI-generated content is expected to increase significantly.
However, data that link this usage to psychological variables and track it over time remain scarce.
Thus, the use of generative AI--consciously or unconsciously--along with interaction through LLM-powered (voice) assistants and engagement with AI-generated content is expected to increase significantly.
However, data that link this usage to psychological variables and track it over time remain scarce.
This longitudinal study comprises the data from an American sample across six waves at two-month intervals between September 2024 and July 2025. It examines user behavior, attitudes, knowledge, and perceptions related to generative AI.
...
This dataset allows for future research on psychological and behavioral dynamics of AI use over time, offering insights into user engagement and the individual factors connected to it.
@ -30,6 +30,31 @@ This dataset allows for future research on psychological and behavioral dynamics
\section{Background and Summary}
% Taken from LEK application
This project is a joint project from the human-computer interaction group at
IWM. There will be several preregistrations from group members focusing on their
subquestions. However, the overall aim is to examine how people change their
use, perception, and attitudes towards AI-tools like ChatGPT, Siri, or Alexa.
We target an US-American sample and plan a longitudinal study starting at the
end of August 2024 (six waves, roughly one year). We use a longitudinal design
to track changes over time and to get some hints on causality. We target an
American sample because Apple announced to release its new AI platform Apple
Intelligence in autumn 24 (in the US due to the stricter regulations in the EU)
and we expect that many people will be exposed to this AI on their Apple
devices. Longitudinal studies are more likely to find changes if there is a
potential change trigger (Zhao et al., 2024)
Central questions are whether predictors of technology acceptance as well as
technology use change over time, whether the perception of AI-Tools as tools vs.
agents (if so: what type of role/relationship) changes over time, whether this
perception is related to concepts like credibility, trustworthiness, or task
delegation, and whether factors such as social presence of perceive
anthropomorphism mediate such processes. We’ll also explore the long-term
effects of delegating tasks to AI Tools on outcomes like perceived
self-efficacy (writing skills), loneliness, or cognitive self-esteem and explore
the moderating role of personality.
...
Longitudinal studies like this are needed to capture the evolving perceptions of opportunities and risks associated with AI, perceived capabilities of AI systems, attitudes toward AI, trust in AI, willingness to delegate tasks to AI, areas of application, and the interrelationships among these constructs over time (to be continued). To examine those changes and relationships, an American sample mainly consisting of AI users (specify) was invited to participate in this survey at two-month intervals between September 2024 and July 2025.
% Overview of Dataset
@ -41,9 +66,30 @@ Longitudinal studies like this are needed to capture the evolving perceptions of
* dataset brings together various seperate WPs -> possibility to make across-WP analyses
* potential to look on clusters/subgroups/individual trajectories ignored in the WPs
* snapshots of important points in time (LLMs on the rise)
* outlook on potential developments in other countries
* outlook on potential developments in other countries
* connection of actual use and stable psychological variables
% WP1 Teresa/Nico/Vanessa/Angelica
%
% WP2 Teresa
%
% WP3 Sonja https://aspredicted.org/4g3d-rqkt.pdf
%
% WP4 Büsra https://aspredicted.org/m6zv9.pdf
%
% WP5 Büsra/Teresa https://aspredicted.org/kx5r-4pxq.pdf
%
% WP6 Angelica/Gerrit
%
% WP7 Mike ???
%
% WP8 Steffi/Sonja https://osf.io/f3jyc?view_only=d8d009e575c64dc2bd453f969c3cb7b1
%
% WP9 Steffi https://osf.io/h5fwe?view_only=8c5bc9e62074469ebdb3d72b38f4716d
%
% --> Are these all WPs? Are there any missing?
% Previous Publications
%
% * Cite any previous publications that utilized these data, in whole or in part
@ -119,7 +165,7 @@ We collected sociodemographic information, including, age, gender, educational l
\section{Data Records}
Data records for each of the six waves are available in csv format at (tbd) together with the R/python scripts for data anonymization, data cleaning, and data preprocessing.
Data records for each of the six waves are available in csv format at (tbd) together with the R/python scripts for data anonymization, data cleaning, and data preprocessing.
That is, firstly the data was anonymized by removing participants' Prolific IDs and unused variables, empty variables resulting from faulty questionnaire programming, and xy were removed. Thus (filename) represents the cleaned and anonymized raw data, including the single items of each measurement. Second, variable names were harmonized and scales were calculated, resulting an the preprocessed data set xy, ready for analyses across scales.
Moreover, a codebook explaining variable abbreviations and containing information about the waves in which the variable was collected (what else?) is available at (tbd).

BIN
manuscript.pdf
View File

Binary file not shown.

66
manuscript.tex
View File

@ -19,8 +19,8 @@
\begin{abstract}
Since the emergence of large language models (LLMs) in 2022, generative AI has rapidly expanded into mainstream applications, leading to the integration of Apple Intelligence into customer devices in 2024. This integration into personal technology marks a significant shift, bringing advanced AI capabilities into everyday devices and making them accessible to private individuals.
Thus, the use of generative AI--consciously or unconsciously--along with interaction through LLM-powered (voice) assistants and engagement with AI-generated content is expected to increase significantly.
However, data that link this usage to psychological variables and track it over time remain scarce.
Thus, the use of generative AI--consciously or unconsciously--along with interaction through LLM-powered (voice) assistants and engagement with AI-generated content is expected to increase significantly.
However, data that link this usage to psychological variables and track it over time remain scarce.
This longitudinal study comprises the data from an American sample across six waves at two-month intervals between September 2024 and July 2025. It examines user behavior, attitudes, knowledge, and perceptions related to generative AI.
...
This dataset allows for future research on psychological and behavioral dynamics of AI use over time, offering insights into user engagement and the individual factors connected to it.
@ -30,6 +30,31 @@ This dataset allows for future research on psychological and behavioral dynamics
\section{Background and Summary}
% Taken from LEK application
This project is a joint project from the human-computer interaction group at
IWM. There will be several preregistrations from group members focusing on their
subquestions. However, the overall aim is to examine how people change their
use, perception, and attitudes towards AI-tools like ChatGPT, Siri, or Alexa.
We target an US-American sample and plan a longitudinal study starting at the
end of August 2024 (six waves, roughly one year). We use a longitudinal design
to track changes over time and to get some hints on causality. We target an
American sample because Apple announced to release its new AI platform Apple
Intelligence in autumn 24 (in the US due to the stricter regulations in the EU)
and we expect that many people will be exposed to this AI on their Apple
devices. Longitudinal studies are more likely to find changes if there is a
potential change trigger (Zhao et al., 2024)
Central questions are whether predictors of technology acceptance as well as
technology use change over time, whether the perception of AI-Tools as tools vs.
agents (if so: what type of role/relationship) changes over time, whether this
perception is related to concepts like credibility, trustworthiness, or task
delegation, and whether factors such as social presence of perceive
anthropomorphism mediate such processes. We’ll also explore the long-term
effects of delegating tasks to AI Tools on outcomes like perceived
self-efficacy (writing skills), loneliness, or cognitive self-esteem and explore
the moderating role of personality.
...
Longitudinal studies like this are needed to capture the evolving perceptions of opportunities and risks associated with AI, perceived capabilities of AI systems, attitudes toward AI, trust in AI, willingness to delegate tasks to AI, areas of application, and the interrelationships among these constructs over time (to be continued). To examine those changes and relationships, an American sample mainly consisting of AI users (specify) was invited to participate in this survey at two-month intervals between September 2024 and July 2025.
% Overview of Dataset
@ -41,9 +66,30 @@ Longitudinal studies like this are needed to capture the evolving perceptions of
* dataset brings together various seperate WPs -> possibility to make across-WP analyses
* potential to look on clusters/subgroups/individual trajectories ignored in the WPs
* snapshots of important points in time (LLMs on the rise)
* outlook on potential developments in other countries
* outlook on potential developments in other countries
* connection of actual use and stable psychological variables
% WP1 Teresa/Nico/Vanessa/Angelica
%
% WP2 Teresa
%
% WP3 Sonja https://aspredicted.org/4g3d-rqkt.pdf
%
% WP4 Büsra https://aspredicted.org/m6zv9.pdf
%
% WP5 Büsra/Teresa https://aspredicted.org/kx5r-4pxq.pdf
%
% WP6 Angelica/Gerrit
%
% WP7 Mike ???
%
% WP8 Steffi/Sonja https://osf.io/f3jyc?view_only=d8d009e575c64dc2bd453f969c3cb7b1
%
% WP9 Steffi https://osf.io/h5fwe?view_only=8c5bc9e62074469ebdb3d72b38f4716d
%
% --> Are these all WPs? Are there any missing?
% Previous Publications
%
% * Cite any previous publications that utilized these data, in whole or in part
@ -70,12 +116,20 @@ Longitudinal studies like this are needed to capture the evolving perceptions of
% * Detail the data acquisition methods.
% * Explain any computational processing involved.
%
\subsection{e.g.: Participants and Data Collection}
* Prolific
* Invitation
* time and intervals
* retention rate
* second sample -> invitation of wave1 participants
* focus on users -> exclusion of nousers without intention
* ethics approval
\subsection{e.g.: Measurements}
* List of all measures by wave
We collected sociodemographic information, including, age, gender, educational level, and household income from all participants at wave 1.
% Input Data for Secondary Datasets
%
% * Provide detailed descriptions of all input data.
@ -111,6 +165,10 @@ Longitudinal studies like this are needed to capture the evolving perceptions of
\section{Data Records}
Data records for each of the six waves are available in csv format at (tbd) together with the R/python scripts for data anonymization, data cleaning, and data preprocessing.
That is, firstly the data was anonymized by removing participants' Prolific IDs and unused variables, empty variables resulting from faulty questionnaire programming, and xy were removed. Thus (filename) represents the cleaned and anonymized raw data, including the single items of each measurement. Second, variable names were harmonized and scales were calculated, resulting an the preprocessed data set xy, ready for analyses across scales.
Moreover, a codebook explaining variable abbreviations and containing information about the waves in which the variable was collected (what else?) is available at (tbd).
% * Explain what the dataset contains.
% * Specify the repository where the dataset is stored.
% * Provide an overview of the data files and their formats.
@ -171,7 +229,7 @@ Hier ist ein R-Chunk:
\end{Sinput}
\begin{Soutput}
Min. 1st Qu. Median Mean 3rd Qu. Max.
-3.31070 -0.70017 0.02192 -0.05356 0.66420 2.06854
-2.79361 -0.91601 0.03741 -0.13932 0.64710 2.73127
\end{Soutput}
\end{Schunk}