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Descriptive Statistics Cell Phone Manipulation M8311 Std Deviation N Social networking words Cell phone absence 3. 63 3.043 94 Cell phone presence 3. 51 3. 385 94 Total 3.56 3.163 188 Non-social Networking Cell phone absence 4. 67 3. 1 13 94 Cell phone presence 3.77 1.811 94 Total 4.33 3.013 188 Partial Eta Source df F Sig Squared Words 16.330 <.001 .081 words cell phone .015 manipulation error marginal means of measure_1 social nonsocial estimated absence presence manipulationelb ui lllb results sectnon analyses focus on participants1 rts to the trials in which a target was present and from different emotional category distractor g. were not included for arrays containing eight images cat one image buttery because cats butteries are both positive low-arousal items analyzed each emotion l iiicccooddoiqqccooiiioooiiiiiioooo category. excluded than all responses as that sd participant mean median then calculated five categories collapsing across array type table raw rt values two age groups this allowed us examine .4 example whether participants faster detect snakes mushrooms regardless they presented. our main interest examining effects valence arousal detection times we created scores controlled neutral targets subtracting high these difference examined with x _- older negative low analysis variance anova revealed only significant effect f p=".006," .16 v larger differences between high-arousal . i.e. processed more quickly compared see figure there no signicant nor an interaction arousal. it is critical name independent variable experiment: phones list levels variable: without cellphone second types anagrams networking terms non-social what dependent can decipher descriptive statistics give standard deviation presence: m="3.51," absence: total words: test within-subjects write website following format np df f-ratio eta squaredtests measure: ii sum partial source squares square sig. squared sphericity assumed greenhouse-geisser huynh-feldt lower-bound cellphonemanipulation websites squaredf significant. tests between-subjects transformed average sig intercept .021 significance manipulation.estimated describe going above image. while increases well.example: race recognition spss two-way mixed factorial asian manipulated face variable. showed .03 n .001 caucasian performing similarly overall. addition seen also .01 showing similar faces. contrast performed better facial stimuli opposite pattern faces looking at races sized advantage own-race findings support notion bias>