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The Goal Characteristics (GC) questionnaire: A comprehensive measure for goals' content, attainability, interestingness, and usefulness

Authors:
Gabriela Iwama at University of Tübingen
Gabriela Iwama
Maria Wirzberger at University of Stuttgart
Maria Wirzberger
Falk Lieder at University of California, Los Angeles
Falk Lieder
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Abstract

Many studies have investigated how goal characteristics affect goal achievement. However, most of them considered only a small number of characteristics and the psychometric properties of their measures remains unclear. To overcome these limitations, we developed and validated a comprehensive questionnaire of goal characteristics with four subscales - measuring the goal’s content, attainability, interestingness, and usefulness respectively. 590 participants completed the questionnaire online. A confirmatory factor analysis supported the four subscales and their structure. The GC questionnaire (https://osf.io/qfhup) can be easily applied to investigate goal setting, pursuit and adjustment in a wide range of contexts.
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Exploratory Graph Analysis
Method
Prompt Validation
Final Structure Evaluation
References
1. Milyavskaya & Werner (2018) 2. Locke & Latham (2002). 3. Golino & Epskamp (2017)
Conclusion and Future Directions
•The final structure showed a good fit and reliability indices. Few
characteristics showed an unexpected structured (e.g., Self-
Congruence and Hierarchy).
•The GC questionnaire can be used to assess characteristics of
any goal, such as educational, organizational or personal goals.
•Future studies can investigate interaction effects between
characteristics on goal achievement and measurement invariance.
http://re.is.mpg.de
The Goal Characteristics (GC) questionnaire:
A comprehensive measure for goals content, attainability, interestingness, and usefulness
Gabriela Iwama, Maria Wirzberger, Falk Lieder
Max Planck Institute for Intelligent Systems, Tübingen
Figure 2. Fit indices for alternative models using WLSM estimator. ꭓ² = Chi-Square with Satorra-Bentler
correction; df = degrees of freedom; CFI = Comparative Fit Index; TLI = Tucker Lewis index;
RMSEA = Root Mean Square Error of Approximation; SRMSR = Standardized Root Mean Square Residual.
Interfactor Correlation
Characteristics from
Literature
Item Pool
Generation
Item-Sort Task with
Psychometricians
Item Refinement Prompt Validation
(N = 85)
Online
Data Collection
(N = 590)
Exploratory Graph
Analysis
Questionnaire
Refinement
Confirmatory Factor
Analysis
(WLSMV estimator)
Content
CFI = .96;
TLI = .96;
RMSEA = .038;
SRMR = .052
Interest
CFI = .97;
TLI = .97;
RMSEA = .038;
SRMR = .052
Utility
CFI = .99;
TLI = .98;
RMSEA = .038;
SRMR = .046
Overall Fit
27 factors measured by 105 items
CFI = .93; TLI = .93; RMSEA = .046; SRMR = .059
EGA is a dimensional analysis based on
Graphical LASSO regression and a
walktrap algorithm to identify the factors.
It was showed that EGA has abetter
accuracy compared to Parallel Analysis³.
See the full questionnaire:
osf.io/qfhup/
In an online survey, 590 participants (52% female; average age = 40 years, SD =
12 years) rate their agreement with 171 statements using a 5-point scale
regarding a self-generated goal. To avoid memory bias and ensure variability in
the goals, participants were prompted to report goals with different time
horizons, difficulties, and progress in a between-subjects design. Example:
Data Collection
Introduction
•Some characteristics of goals help people achieve them¹.
•Previous studies had focused on the influence of specific,
challenging and approach goals.
•Existing measures are usually context specific².
•The aim of this study is to develop and evaluate the psychometric
properties for a comprehensive and general measure for goal‘s
characteristics.
Attainability
CFI = .97;
TLI = .96;
RMSEA = .038;
SRMR = .052
Factor Item with highest factor loading
Content
Specificity
This goal has a clear defined outcome or final state.
Time
Specificity
I have a clear deadline by which I want to attain this goal.
Measurability
My progress in this goal can be tracked with objective measures.
Controllability
As long as I do what it takes, I will achieve this goal.
Plannability
It's hard to foresee what will be my next steps. (Reversed)
Social
Support
People encourage me to keep going.
Actionable
I know how to start working on this goal.
Importance
This goal doesn't drive much of my attention. (Reversed)
Awareness
I didn't know I had this goal until you asked me. (Reversed)
Long
-Term Utility
This goal won't make a huge impact in the future.
(Reversed)
Self
-Improvement
This goal will help me grow as a person.
Sample
Items
gabriela.yukari.iwama@gmail.com
Figure 1. Example of Exploratory Graph Analysis with
Interest subscale items.
Progress Difficulty Time Perspective
Figure 3. Interfactor
correlation matrix.
Composite reliability
indices are showed
on matrix diagonal.
.83.82.70.86.83.91.78.93.83.83.89.91.80.89.87.93.93.84.81.76.93.91.90.89.89.88.82
... Goal characteristics which cannot be assessed from an external perspective merely from an articulated goal itself can better be captured by psychometric self-report measures used by the person with the goal in mind. The ongoing development of a goal characteristics questionnaire (Iwama et al., 2019), which constitutes a psychometric measure covering a broad variety of goal-related variables, bears a huge potential for future research on goals. ...
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Chapter
  • Jan 2022
The main topic of this chapter is individual educational goals, which are central to self-regulated learning and an observable manifestation of students’ motivation. As introduction we review research literature on goal setting, motivation, and self-regulation from the domains of organizational psychology, developmental psychology, educational psychology, and positive psychology, which elucidates the importance of personally relevant goals, especially for higher education. Based on this theoretical foundation, we introduce our work on the development of a digital data-driven study assistant for goal pursuit, which is integrated into the learning management system Stud.IP at three German universities. In the empirical part, we report the development of a tagset for university students’ educational goals, intended to be used for automatic tagging of students’ goals in the digital assistance software. We collected a sample of 2.262 educational goals in natural language, originating from 732 students from 3 German universities and developed a tagset for 28 tags, grouped into 7 sets, each constituting a meta-tag. Six independent raters assigned tags to goals, resulting in 376.458 tag assignments. Based on this data, we calculated Krippendorff’s α as measure for inter-rater agreement and relative frequencies for tags and meta-tags. The results for inter-rater agreement scores are quite heterogeneous, which can be explained by the inherently subjective nature of goals. We conclude with an outlook on practical implications of our research for the assessment of goal characteristics and prospective future research topics.KeywordsGoal settingGoal characteristicsDigital assistantsAI in higher education
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Chapter
  • Dec 2022
This paper summarizes three studies with a digital goal-setting intervention for higher education based on hierarchical goal systems (HGS). As a theoretical background, we cover organizational, motivational, and educational psychology findings related to goals, self-regulated learning, and goal systems. Subsequently, we introduce hierarchical goal systems conceptually and present the concrete implementation of the digital HGS intervention and its essential functions. Next, three formative studies with their methods, results, and implications are summarized. We designed the studies to answer the following research questions: which visualization type is most suitable to represent HGS and to function as a visual metaphor in a graphical user interface? Do visualization preferences and performance indicators depend on personality traits? How can priming be used to lead students to personally meaningful educational goals? The results show overall high preference and performance scores for the dendrogram visualization, low scores for the treemap visualization, and heterogeneous scores for the circle packing and the sunburst. These findings are found to be robust over the “big five” or “OCEAN” (openness, conscientiousness, extraversion, agreeableness, and neuroticism) personality traits (Goldberg, J Pers Soc Psychol 59(6):1216–1229, 1990). The effects of priming tasks on the characteristics of subsequently formulated goals are relatively low, as we measured small effects with rather low significance in 3 out of 32 factors, explainable by alpha error inflation. The chapter concludes with an outlook on the benefits between students and researchers in the upcoming field study with the digital goal-setting intervention.
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