This Study Examines The (A) Lateralization Pattern Of Handedness Groups On Central And Peripheral Measures And The (B) Association Between Different Laterality Measures. The Sample Comprised 142 Right-Handers, 61 Left-Handers And 21 Mixed-Handers. The Hand, Foot, Eye, And Ear Preferences Were Assessed Using A Lateral Preference Inventory. Hand And Foot Performances Were Measured Using A Hand And Foot Tapping Task. Conjugate Lateral Eye Movements Were Elicited In Response To Verbal And Spatial Questions. Split Visual Field, Dichotic Listening, And Dichaptic Methods Were Used To Tap The Visual-, Auditory-, And Tactual-Bias Respectively. Results Reveal That On Hand And Foot Tapping Task, The Left-Handers Tapped Faster On The Left Side And The Right-Handers Tapped Faster On The Right Side. Conjugate Lateral Eye Movements For Verbal Questions Are Rightward For Righthanders And Leftward For Left- And Mixed-Handers But On Spatial Questions They Are Leftward For All The Three Groups. On Central Measures Of Split-Visual Field And Dichotic Listening, The Right-Handers Show Left-Hemispheric Advantage For Verbal Stimuli And Righthemispheric Advantage For Non-Verbal Stimuli With The Slower Reaction Time. On Dichaptic Task, Right-Handers Have Shown A Left-Hemispheric Advantage For Both Linguistic And Figurative Stimuli. The Left-Handers Have Shown A Right-Hemispheric Advantage On All The Three Tasks. The Performance Of Mixed-Handers Has Remained Invariant Across The Experimental Conditions. Of The Central Measures, The Split Visual Field Is Found To Be A Better Measure Than The Dichotic And Dichaptic Tasks In Detecting Hemispheric Dominance. In Testing The Association Between 14 Tasks Of Laterality Assessment, The ‘Global And Local’ Model, The ‘Global’ Model, And The ‘Local’ Model Are Tested. Results Reveal That The Local Model Is Superior Over The Other Two Models. This Suggests The Localization Of Specific Functions In Particular Areas Of Brain. Key Words: Hemispheric Advantage, Laterality, Local Model, Mixed-Handers, Split Visual