Insufficient Effort Research Articles

The Impact of Attentiveness Interventions on Survey Data.

Social and behavioral science researchers who use survey data are vigilant about data quality, with an increasing emphasis on avoiding common method variance (CMV) and insufficient effort responding (IER). Each of these errors can inflate and deflate substantive relationships, and there are both a priori and post hoc means to address them. Yet, little research has investigated how both IER and CMV are affected with the use of these different procedural or statistical techniques used to address them. More specifically, if interventions to reduce IER are used, does this affect CMV in data? In an experiment conducted both in and out of the laboratory, we investigate the impact of attentiveness interventions, such as a Factual Manipulation Check (FMC) on both IER and CMV in same-source survey data. In addition to typical IER measures, we also track whether respondents play the instructional video and their mouse movement. The results show that while interventions have some impact on the level of participant attentiveness, these interventions do not appear to lead to differing levels of CMV.

Developing a Machine-learning Algorithm for Classifying Parents’ Responses to Web-based Early Language Screening

Objectives: This study aims to address the issue of decreased reliability caused by inaccurate responses in web-based early language screening tests for infants and toddlers by proposing a method for detecting the sincerity of parental responses using a machinelearning algorithm. Methods: The language development screening test used in this study was developed so that parents could respond directly online, and a total of 453 parents participated in the screening test. Additionally, 226 randomly generated responses were created using a random number generator. According to the criteria set by the researchers, the response types were categorized into sufficient effort responses, insufficient effort responses, and randomly generated responses. Various machine learning algorithms, including XGBoost, LightGBM, and MLP, were used to train the response data, and the accuracy of the model was assessed using K-fold cross-validation. Results: Based on the established criteria, the responses were classified as 220 sufficient effort responses, 233 insufficient effort responses, and 226 randomly generated responses. The ensemble voting demonstrated a high accuracy of approximately 89.7% in detecting insufficient effort responses and randomly generated responses, effectively classifying parents’ insufficient effort responses. Conclusion: The machine-learning-based approach proposed in this study can be effectively utilized not only in web-based language screening for infants and toddlers but also in detecting insufficient effort responses in self-reported surveys. This approach is expected to enhance the reliability of research data and facilitate more accurate assessments.

Identification of supplier partnership critical success factors in the construction industry

In the construction industry, supplier partnerships and collaboration are crucial for overcoming competing objectives and fostering growth. Challenges such as interpersonal conflicts, lack of trust, unequal risk sharing, overdependence, cultural barriers, inefficient problem-solving, communication breakdowns, insufficient effort to sustain long-term cooperation, inadequate training, and exclusion can disrupt these partnership and hinder project progress. Thus, this paper aims to investigate the critical success factor (CSF) of supplier partnerships in the construction industry. The survey-based study was conducted using purposive sampling among top management in construction companies, resulting a final sample of 190 respondents. The study highlights the importance of trust, mutual commitment, and effective dispute resolution in shaping key relationship traits linked to successful supplier partnerships in the construction industry. The findings on CSFs in supplier partnerships provide valuable insights for construction practitioners, helping them prevent disputes, improve partnership efficiency, and eliminate adversarial interactions.

Investigating dynamics in attentive and inattentive responding together with their contextual correlates using a novel mixture IRT model for intensive longitudinal data.

In ecological momentary assessment (EMA), respondents answer brief questionnaires about their current behaviours or experiences several times per day across multiple days. The frequent measurement enables a thorough grasp of the dynamics inherent in psychological constructs, but it also increases respondent burden. To lower this burden, respondents may engage in careless and insufficient effort responding (C/IER), leaving data contaminated with responses that do not reflect what researchers want to measure. We introduce a novel approach to investigating C/IER in EMA data. Our approach combines a confirmatory mixture item response theory model separating C/IER from attentive behaviour with latent Markov factor analysis. This enables gauging the occurrence of C/IER and studying transitions among states of different response behaviours including their contextual correlates. The approach can be implemented using R packages. An empirical application showcases the approach's efficacy in pinpointing C/IER instances and gaining insights into their underlying causes. We showcase that the approach identifies various C/IER response patterns but requires heterogeneous and negatively worded items to detect straightlining. In a simulation investigating robustness against unaccounted for changes in measurement models underlying attentive responses, the approach proved robust against heterogeneity in loading patterns but not against heterogeneity in factor structures. Extensions to accommodate the latter are discussed.

The United Nations Sustainable Development Goal 13 Climate Action and Efforts of UK Banks Toward a Low Carbon Economy?

Banks are increasingly motivated to align their business operations with the United Nations Sustainable Development Goals (SDGs) as this can help them attract investors and build a more loyal customer base in the long run. This paper explores whether major UK banks (HSBC Holdings Plc, Lloyds Banking Group Plc, Barclays Plc, Standard Chartered Plc, and NatWest Group Plc) are increasing their involvement in green banking by transitioning away from fossil fuels. The research gathered expert and policymaker views on decarbonization (reducing carbon footprint) at the Scotland Policy Conference UK in 2022 to support its findings. The results reveal slow progress toward a green banking regime, as the banks continued underwriting fossil fuel IPOs and bond issuances, and funding carbon-intensive industries. Their portfolios primarily consist of fossil fuel projects, with implications for insufficient effort toward meeting UN SDGs related to global climate commitments. The study proposes a policy recommendation: governments should introduce regulations requiring banks to develop a minimum percentage of financial products and services designed to support SDG-related activities, fostering a low-carbon economy.

The distribution of insufficient effort responses according to the methods of classifying and interpreting students’ evaluation of teaching effectiveness

PurposeThis study explores the distribution of insufficient effort responders according to methods of classifying students’ evaluation of teaching effectiveness in higher education. Five different methods were found in the literature to classify students’ evaluation of teaching effectiveness in higher education.Design/methodology/approachQuantitative research methodology was used to achieve the goals of this study. Data from a major public university was used through 20 five-point items that are designed to measure students’ evaluation of teaching effectiveness. The dataset that consisted of 26,679 surveys was analyzed. Detecting insufficient efforts responding was based on item response theory procedures.FindingsThe results show that insufficient effort responders are distributed differently to students’ evaluation of teaching effectiveness in higher education levels according to different methods of classifying these levels. The results of this study suggest using a percentage of students’ agreement of 4 or 5 for each item to classify SET levels and deleting IERs before interpreting SET results.Research limitations/implicationsAccording to the results of this study, it is recommended to research the relationships between IER and SET scores and students’ motivation to participate in evaluating teaching effectiveness.Practical implicationsAccording to the results of this study, it is recommended to:1– Exclude the IERs from the dataset before generating SET reports. 2– Use the percentage of 4 (agree) and 5 (strongly agree) satisfactions of SET items to classify and interpret SET results.Originality/valueReviewing the literature shows the absence of studies that explore the distribution of insufficient effort responders according to methods of classifying students’ evaluation of teaching effectiveness in higher education. The results suggest using a percentage of students’ agreement of 4 or 5 for each item to classify SET levels and deleting IERs before interpreting SET results.

Refuting the hypothesis of Centinelan extinction at its place of origin.

Scientists' limited understanding of tropical plant communities obscures the true extent of species loss caused by habitat destruction1. The Centinelan extinction hypothesis2,3 posits an extreme but widely referenced scenario wherein forest clearing causes the immediate extinction of species known only from a single geographic location. It remains unclear, however, whether the disappearance of such microendemics reflects their global extinction or insufficient collection effort at larger scales. Here we test these hypotheses by synthesizing decades of floristic data from the heavily deforested tropical cloud forest (TCF) at Centinela, Ecuador. We find that 99% of its putative microendemics have been collected elsewhere and are not extinct. Our field work also revealed new species, highlighting the enduring conservation value of TCFs and the intense efforts required to illuminate such plant diversity 'darkspots'4. Field and herbarium research remain essential to the conservation action needed to forestall large-scale plant extinctions in Earth's beleaguered cloud forests.

In Likert Scale, Is Ticking Options Consecutively at Two Ends Equally Problematic as Ticking in the Middle?

The popularity of self-report surveys brings to light the issue of insufficient effort responding (IER), where participants lack motivation to respond thoughtfully, thereby compromising data quality. A prevalent form of IER, known as straightlining, occurs when participants consistently select the same response for multiple items, leading to potentially biased outcomes. Typically, researchers apply a uniform threshold criterion across all response options (e.g., nine consecutive identical answers in any option are considered problematic). Considering the diverse or near-normal distribution patterns in questionnaire responses, employing varied thresholds for different options may be more appropriate. This study utilized both simulated data and real, large-scale international survey data to examine the likelihood of straightlining as IER across various options. The findings revealed that legitimate straightlining (non-IER) was more common at the extremes than in the middle options. Moreover, the study highlights the significant risks of incorrectly categorizing non-IER straightlining as IER without corroborating evidence. In conclusion, our results question the conventional approaches to eliminating straightlining, including (a) the application of a uniform cutoff value across different options or adopting stricter criteria for middle options and (b) the direct removal of straightlining as IER without additional verification.

Validating the IDRIS and IDRIA: Two infrequency/frequency scales for detecting careless and insufficient effort survey responders.

To detect careless and insufficient effort (C/IE) survey responders, researchers can use infrequency items-items that almost no one agrees with (e.g., "When a friend greets me, I generally try to say nothing back")-and frequency items-items that almost everyone agrees with (e.g., "I try to listen when someone I care about is telling me something"). Here, we provide initial validation for two sets of these items: the 14-item Invalid Responding Inventory for Statements (IDRIS) and the 6-item Invalid Responding Inventory for Adjectives (IDRIA). Across six studies (N1 = 536; N2 = 701; N3 = 500; N4 = 499; N5 = 629, N6 = 562), we found consistent evidence that the IDRIS is capable of detecting C/IE responding among statement-based scales (e.g., the HEXACO-PI-R) and the IDRIA is capable of detecting C/IE responding among both adjective-based scales (e.g., the Lex-20) and adjective-derived scales (e.g., the BFI-2). These findings were robust across different analytic approaches (e.g., Pearson correlations; Spearman rank-order correlations), different indices of C/IE responding (e.g., person-total correlations; semantic synonyms; horizontalcursor variability), and different sample types (e.g., US undergraduate students; Nigerian survey panel participants). Taken together, these results provide promising evidence for the utility of the IDRIS and IDRIA in detecting C/IE responding.

The meaning of seeking help: The effects of ascribed and achieved status of the help‐seeker on observer attributions, expectations, and willingness to offer help

AbstractResearch indicates that sometimes people rely on limited sources of information when judging a person or group. Unable to see the “whole picture,” they, usually unconsciously, often fill in missing pieces of information themselves. Ascribed and achieved status dimensions assist in the process of social perception. Drawing on recent research on intergroup helping and focusing on evaluations of potential helpers, this research investigates inferences and attributions made by observers and their willingness to offer help while considering the effects of ascribed and achieved status dimensions of the help‐seeker. We conducted a pilot study and two additional studies exploring the implications of this link in real‐life settings. Our findings indicate that seeking help may be viewed as a sign of insufficient effort, particularly for individuals characterized by low ascribed and achieved status dimensions, and, thus, as stigma‐consistent behavior. Theoretical and applied implications are discussed.

Detection of insufficient effort and simulation of cognitive impairment during neuropsychological examination using RBANS and SIMS methods

Detection of insufficient effort and simulation of cognitive impairment during neuropsychological examination using RBANS and SIMS methods

Using a novel multiple-source indicator to investigate the effect of scale format on careless and insufficient effort responding in a large-scale survey experiment

Common indicator-based approaches to identifying careless and insufficient effort responding (C/IER) in survey data scan response vectors or timing data for aberrances, such as patterns signaling straight lining, multivariate outliers, or signals that respondents rushed through the administered items. Each of these approaches is susceptible to unique types of misidentifications. We developed a C/IER indicator that requires agreement on C/IER identification from multiple behavioral sources, thereby alleviating the effect of each source’s standalone C/IER misidentifications and increasing the robustness of C/IER identification. To this end, we combined a response-pattern-based multiple-hurdle approach with a recently developed screen-time-based mixture decomposition approach. In an application of the proposed multiple-source indicator to PISA 2022 field trial data we (a) showcase how the indicator hedges against (presumed) C/IER overidentification of its constituting components, (b) replicate associations with commonly reported external correlates of C/IER, namely agreement with self-reported effort and C/IER position effects, and (c) employ the indicator to study the effects of changes of scale characteristics on C/IER occurrence. To this end, we leverage a large-scale survey experiment implemented in the PISA 2022 field trial and investigate the effects of using frequency instead of agreement scales as well as approximate instead of abstract frequency scale labels. We conclude that neither scale format manipulation has the potential to curb C/IER occurrence.

The Impact of Insufficient Effort Responses on the Order of Category Thresholds in the Polytomous Rasch Model.

Insufficient effort responding (IER) refers to a lack of effort when answering survey or questionnaire items. Such items typically offer more than two ordered response categories, with Likert-type scales as the most prominent example. The underlying assumption is that the successive categories reflect increasing levels of the latent variable assessed. This research investigates how IER affects the intended category order of Likert-type scales, focusing on the category thresholds in the polytomous Rasch model. In a simulation study, we examined several IER patterns in datasets generated from the mixture model for IER (MMIER). The key findings were (a) random responding and overusing the non-extreme categories of a five-category scale were each associated with high frequencies of disordered category thresholds; (b) raising the IER rate from 5% to 10% led to a substantial increase in threshold disordering, particularly among easy and difficult items; (c) narrow distances between adjacent categories (0.5 logits) were associated with more frequent disordering, compared with wide distances (1.0 logits). Two real-data examples highlighted the efficiency and utility of the MMIER for detecting latent classes of respondents exhibiting different forms of IER. Under the MMIER, the frequency of disordered thresholds was reduced substantially in both examples. The discussion focuses on the practical implications of using the MMIER in survey research and points to directions for future research.

The Impact of Zoning Designations on Bird Communities: A Case Study of National Nature Reserves in Guangdong Province

One of the main management strategies of China’s nature reserve is “zonation”, dividing reserves into three zones (core, buffer, and experimental zones) to which different levels of restrictions on anthropogenic activities are applied. While zonation has a positive effect on the conservation of target species, it is unclear whether management zones can be effective in conserving biodiversity. Using bird data collected from five national nature reserves of Guangdong province in China during 2018–2019, we investigated how management zones and reserves are associated with bird diversity and community composition. We considered species richness, the community-weighted mean (CWM) values of 10 single traits, and beta diversity based on 2 dissimilarity indices (Jaccard and Bray–Curtis). The overall effects of zone management were weak or insignificant. Species richness and the CWMs of most traits were not associated with zone (p > 0.08). Reserve itself was a strong factor influencing bird traits. Dissimilarity indices also showed variations in bird community composition between reserves in the north region and those in the south region, which were largely contributed by the turnover component of beta diversity (p ≥ 0.06 for nestedness and p ≤ 0.03 for turnover in both dissimilarity indices). The dominance of turnover indicates that these reserves could be equally important to maintain regional bird diversity, requiring a multiple-site management plan. Our study also suggests that current zoning designations of the five reserves may not be as effective in conserving bird species diversity and traits; however, caution is needed due to the limitations of our study design, such as potential observer effect and insufficient sampling effort.

Measuring Attentiveness in Self-Administered Surveys

Abstract The surge in online self-administered surveys has given rise to an extensive body of literature on respondent inattention, also known as careless or insufficient effort responding. This burgeoning literature has outlined the consequences of inattention and made important strides in developing effective methods to identify inattentive respondents. However, differences in terminology, as well as a multiplicity of different methods for measuring and correcting for inattention, have made this literature unwieldy. We present an overview of the current state of this literature, highlighting commonalities, emphasizing key debates, and outlining open questions deserving of future research. Additionally, we emphasize the key considerations that survey researchers should take into account when measuring attention.

Identifying Dynamic Shifts to Careless and Insufficient Effort Behavior in Questionnaire Responses; a Novel Approach and Experimental Validation

Careless and insufficient effort responding (C/IER) is a situation where participants respond to survey instruments without considering the item content. This phenomena adds noise to data leading to erroneous inference. There are multiple approaches to identifying and accounting for C/IER in survey settings, of these approaches the best performing are model based classification techniques. Classic approaches to accounting for C/IER treat it as a person level phenomena. They first use some method to identify participants who exhibit C/IER, then use list-wise deletion to remove them prior to analysis. We argue that C/IER is actually a state that participants may exhibit for a portion of a survey instrument. In other words, participants start a survey with the intention to follow instructions, but at some point transition to a C/IER state and no longer respond in line with item contents. Accounting for C/IER at the item level, as opposed to the person level preserves data resulting in increased power. In this article we present a Bayesian Dynamic Latent Class Structural Equation Modeling (DLCSEM) approach for simultaneously accounting for C/IER at the item level and estimating a model of interest. We use a simulation study to establish the approaches performance under empirically relevant conditions and to compare it to other methods. We then conducted an experimental validation in which we induce C/IER like responses from human subjects and investigate the approaches ability to identify the point in which participants transition to a C/IER state. We also compare the model to existing approaches. In both the simulation and experimental validation the DLCSEM outperforms the alternative approaches. We conclude that the approach should be used by applied researchers for the pragmatic benefits of the method. Conclusions, limitations, and future directions are discussed.

A screen-time-based mixture model for identifying and monitoring careless and insufficient effort responding in ecological momentary assessment data.

Ecological momentary assessment (EMA) involves repeated real-time sampling of respondents' current behaviors and experiences. The intensive repeated assessment imposes an increased burden on respondents, rendering EMAs vulnerable to respondent noncompliance and/or careless and insufficient effort responding (C/IER). We developed a mixture modeling approach that equips researchers with a tool for (a) gauging the degree of C/IER contamination of their EMA data and (b) studying the trajectory of C/IER across the study. For separating attentive from C/IER behavior, the approach leverages collateral information from screen times, which are routinely recorded in electronically administered EMAs, and translates theoretical considerations on respondents' behavior into component models for attentive and careless screen times as well as for the functional form of C/IER trajectories. We show how a sensible choice of component models (a) allows disentangling short screen times due to C/IER from familiarity effects due to repeated exposure to the same measures, (b) aids in gaining a fine-grained understanding of C/IER trajectories by distinguishing within-day from between-day effects, and (c) allows investigating interindividual differences in attentiveness. The approach shows good parameter recovery when attentive and C/IER screen time distributions exhibit sufficient separation and yields valid conclusions even in scenarios of uncontaminated data. The approach is illustrated on EMA data from the German Socio-Economic Panel innovation sample. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Confounding Effects of Insufficient Effort Responding Across Survey Sources: The Case of Personality Predicting Performance

Insufficient effort responding (IER) to surveys, which occurs when participants provide responses in a haphazard, careless, or random fashion, has been identified as a threat to data quality in survey research because it can inflate observed relationships between self-reported measures. Building on this discovery, we propose two mechanisms that lead to IER exerting an unexpected confounding effect between self-reported and informant-rated measures. First, IER can contaminate self-report measures when the means of attentive and inattentive responses differ. Second, IER may share variance with some informant-rated measures, particularly supervisor ratings of participants’ job performance. These two mechanisms operating in tandem would suggest that IER can act as a “third variable” that inflates observed relationships between self-reported predictor scores and informant-rated criteria. We tested this possibility using a multisource dataset ( N = 398) that included incumbent self-reports of five-factor model personality traits and supervisor-ratings of three job performance dimensions—task performance, organizational citizenship behavior (OCB), and counterproductive work behavior (CWB). We observed that the strength of the relationships between self-reported personality traits and supervisor-rated performance significantly decreased after IER was controlled: Across the five personality traits, the average reduction of magnitude from the zero-order to partial correlations was |.08| for task performance, |.07| for OCB, and |.14| for CWB. Because organizational practices are often driven by research linking incumbent-reported predictors to supervisor-rated criteria (e.g., validation of predictors used in various organizational contexts), our findings have important implications for research and practice.

Exploring Factors Contributing to Indifference Towards Learning Mathematics Among Secondary School Students in Nepal

Abstract 
 
 Mathematics is a compulsory subject at the school level in Nepal, deemed essential for everyday life and higher studies, particularly in the fields of science and technology. However, there is a noticeable apathy among students when it comes to learning mathematics. This qualitative research aims to identify the factors that contribute to this indifference towards learning mathematics. Data was collected through in-depth interviews with four participants from both public and private schools, all enrolled in the tenth grade. Analysis and interpretation of the data revealed several factors that lead to this indifference. These factors can be classified as student-related, school-related, and home and society-related. Student-related factors include mathematics anxiety, negative perceptions, insufficient effort, poor academic achievements, limited real-world applications, low self-efficacy, and perpetuation of misconceptions about mathematics. School-related factors encompass teaching practices, teacher qualifications, traditional methods focused on rote learning, impractical curriculum and courses, inadequate school administration, and subpar physical facilities. Home and society-related factors have a negative effect on mathematics engagement, such as unfavorable home environments, low socioeconomic status, and parental education. Together, these factors contribute to the observed indifference towards learning mathematics.
 Keywords: Indifference, Qualitative, Mathematics, Factors, Home, Students, School

The effectiveness of warning statements in reducing careless responding in crowdsourced online surveys

Carelessness or insufficient effort responding is a widespread problem in online research, with estimates ranging from 3% to almost 50% of participants in online surveys being inattentive. While detecting carelessness has been subject to multiple studies, the factors that reduce or prevent carelessness are not as well understood. Initial evidence suggests that warning statements prior to study participation may reduce carelessness, but there is a lack of conclusive high-powered studies. This preregistered randomized controlled experiment aimed to test the effectiveness of a warning statement and an improved implementation of a warning statement in reducing participant inattention. A study with 812 participants recruited on Amazon Mechanical Turk was conducted. Results suggest that presenting a warning statement is not effective in reducing carelessness. However, requiring participants to actively type the warning statement statistically significantly reduced carelessness as measured with self-reported diligence, even-odd consistency, psychometric synonyms and antonyms, and individual response variability. The active warning statements also led to statistically significantly more attrition and potentially deterred those who were likely to be careless from even participating in this study. We show that the current standard practice of implementing warning statements is ineffective and novel methods to prevent and deter carelessness are needed.