On the use of receiver operating characteristic area under the curve in eyewitness memory research

被引:0
|
作者
Riesthuis, Paul [1 ,2 ]
Otgaar, Henry [1 ,2 ]
机构
[1] Katholieke Univ Leuven, Leuven Inst Criminol, Fac Law & Criminol, Oude Markt 13, B-3000 Leuven, Belgium
[2] Maastricht Univ, Fac Psychol & Neurosci, Forens Psychol Sect, Maastricht, Limburg, Netherlands
关键词
area under the curve; power analysis; receiver operating characteristic curve; smallest effect size of interest; PSYCHOLOGICAL-RESEARCH; CONFIDENCE-INTERVALS; RECOGNITION MEMORY; EFFECT SIZE; IDENTIFICATION; RELIABILITY; HYPOTHESIS; LINEUPS; TESTS; ROCS;
D O I
10.1111/lcrp.12300
中图分类号
DF [法律]; D9 [法律];
学科分类号
0301 ;
摘要
PurposeEyewitness memory research has reformed police practices and policy and is sometimes relied upon in legal proceedings. Due to the practical implications derived from this research, it is imperative to evaluate how practical recommendations are postulated. To assess the practical relevance of research, effect sizes and their interpretation play a pivotal role.MethodsWe examined how the frequently used effect size Area Under the Curve (AUC) obtained via Receiver Operating Characteristic (ROC) curves are used and interpreted in eyewitness memory research. We identified 157 eyewitness memory related articles that conducted ROC curve analyses resulting in 1580 AUCs.ResultsApproximately 90% of the AUCs were only interpreted via statistical significance. The majority of studies did not report 95%CIs for their AUCs. Finally, power analyses were frequently not conducted or not reproducible.ConclusionsTo improve the practical inferences of eyewitness memory research, we recommend establishing a smallest effect size of interest, focusing on 95%CIs, and conducting reproducible power analyses.
引用
收藏
页数:19
相关论文
共 50 条
  • [21] Neural network design for optimization of the partial area under the receiver operating characteristic curve
    Sahiner, B
    Chan, HP
    Petrick, N
    Gopal, SS
    Goodsitt, MM
    1997 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, 1997, : 2468 - 2471
  • [22] Direct estimation of the area under the receiver operating characteristic curve in the presence of verification bias
    He, Hua
    Lyness, Jeffrey M.
    McDermott, Michael P.
    STATISTICS IN MEDICINE, 2009, 28 (03) : 361 - 376
  • [23] The Area Under a Receiver Operating Characteristic Curve Over Enriched Multipath Fading Conditions
    Sofotasios, Paschalis C.
    Fikadu, Mulugeta K.
    Khuong Ho-Van
    Valkama, Mikko
    Karagiannidis, George K.
    2014 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2014), 2014, : 3490 - 3495
  • [24] Combining binary and continuous biomarkers by maximizing the area under the receiver operating characteristic curve
    Ahmadian, Robab
    Ercan, Ilker
    Sigirli, Deniz
    Yildiz, Abdulmecit
    COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2022, 51 (08) : 4396 - 4409
  • [25] Estimating the area under a receiver operating characteristic (ROC) curve: Parametric and nonparametric ways
    Xu, Weichao
    Dai, Jisheng
    Hung, Y. S.
    Wang, Qinruo
    SIGNAL PROCESSING, 2013, 93 (11) : 3111 - 3123
  • [26] Direct estimation of the area under the receiver operating characteristic curve with verification biased data
    Hai, Yan
    Qin, Gengsheng
    STATISTICS IN MEDICINE, 2020, 39 (30) : 4789 - 4820
  • [27] Evidence Based Emergency Medicine; Part 5 Receiver Operating Characteristic Curve and Area under the Curve
    Safari, Saeed
    Baratloo, Alireza
    Elfil, Mohamed
    Negida, Ahmed
    EMERGENCY, 2016, 4 (02): : 111 - 113
  • [28] Multi-instance learning by maximizing the area under receiver operating characteristic curve
    Sakarya, I. Edhem
    Kundakcioglu, O. Erhun
    JOURNAL OF GLOBAL OPTIMIZATION, 2023, 85 (02) : 351 - 375
  • [29] Estimating the area under a receiver operating characteristic curve using partially ordered sets
    Zamanzade, Ehsan
    Wang, Xinlei
    INTERNATIONAL JOURNAL OF BIOSTATISTICS, 2021, 17 (01): : 139 - 152
  • [30] On the optimism correction of the area under the receiver operating characteristic curve in logistic prediction models
    Iparragirre, Amaia
    Barrio, Irantzu
    Xose Rodriguez-Alvarez, Maria
    SORT-STATISTICS AND OPERATIONS RESEARCH TRANSACTIONS, 2019, 43 (01) : 145 - 162