title: Detecting false positives in A-B designs: potential implications for practitioners.
abstract: This study evaluated the probability of generating false positives with A-B graphs. We generated 1,000 graphs consisting of three stable A-phase data points at 25% and three random B-phase data points; 1,000 graphs consisting of three stable A-phase data points at 50% and three random B-phase data points; and 1,000 graphs consisting of three random A-phase data points and three random B-phase data points. Results indicate that false positives were produced for (a) a relatively high percentage of graphs containing nonrandom data points in the A phase and (b) less than 2% of graphs containing random data points in both the A and B phases. These findings suggest that A-B designs may be a stronger clinical tool for evaluating the effects of interventions than previously recognized.
authors.affiliation: St. Cloud State University, MN, USA.
authors.forename: Tyler K
authors.initials: TK
authors.lastname: Krueger
authors.forename: John T
authors.initials: JT
authors.lastname: Rapp
authors.forename: Lisa M
authors.initials: LM
authors.lastname: Ott
authors.forename: Elizabeth A
authors.initials: EA
authors.lastname: Lood
authors.forename: Marissa A
authors.initials: MA
authors.lastname: Novotny
mesh_terms: D003198:Computer Simulation; D005189:False Positive Reactions; D006801:Humans; D015588:Observer Variation; D011336:Probability; D012107:Research Design / Q000706:statistics & numerical data*