Multilevel analysis quantifies variation in the experimental effect while optimizing power and preventing false positives

BMC Neuroscience

Volumn 16, Issue 1, 2015, Pages

  Aarts, Emmeke ^a,b   Dolan, Conor V ^a   Verhage, Matthijs ^a,c   Van der Sluis, Sophie ^c  

^a VU UNIVERSITY AMSTERDAM   (Netherlands)

^b MAX PLANCK INSTITUTE FOR MOLECULAR GENETICS   (Germany)

^c VU UNIVERSITY MEDICAL CENTER   (Netherlands)

Author keywords

Clustered data; Experimental effect; False positive rate; Hierarchical data; Multilevel analysis; Neuroscience; Optimal research design; Pseudo replication; Statistical power

Indexed keywords

EFFECT SIZE; ERROR; EXPERIMENTAL MODEL; MULTILEVEL ANALYSIS; SAMPLE SIZE; ANIMAL; COMPUTER SIMULATION; FALSE POSITIVE RESULT; GENE SILENCING; METHODOLOGY; MOUSE; NERVE CELL; NEUROSCIENCE; PHYSIOLOGY; PROCEDURES; STATISTICAL ANALYSIS;

SMALL INTERFERING RNA;

ANIMALS; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; FALSE POSITIVE REACTIONS; GENE KNOCKDOWN TECHNIQUES; MICE; MULTILEVEL ANALYSIS; NEURONS; NEUROSCIENCES; RESEARCH DESIGN; RNA, SMALL INTERFERING;

EID: 84949955876     PISSN: None     EISSN: 14712202     Source Type: Journal    
DOI: 10.1186/s12868-015-0228-5     Document Type: Article

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