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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 manipulationestimated 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 described above suggested influence influences emotion. further test validity hypothesis submitted old arous repeated-measures anova. group np=".92," categor .2. ch... .......... well appeared reflect fact younger adults detected t other ts .001 differing significantly another trends high-arousa be rapidly .12 fo> 2.56, p <.017 and rts to the different emotion categories of targets did not differ significantly from one another. thus these results provided some evidence that older adults may show a broader advantage for detection any type emotional information whereas young benefit be more narrowly restricted only certain elements thepart make de discussion where you explain your draw conclusions tie it in with previous research. follow specific general organization. start by restating hypothesis. were hypotheses correct does anova support research or want subsection implications limitations this can just keep within section. also give suggestions future supported rejected are possible alternative explanations directions name independent variable our experiment: presence cell phones list levels variable: phone without cellphone second types anagrams social networking terms non-social what is dependent which they decipher descriptive statistics means standard deviation interaction between words presence: m="3.51," sd="2.29" absence: total words: error no present test within-subjects effects write main effect website following format f p=".001," np .081 df f-ratio eta squaredtests measure: measure_1 ii sum partial source squares mean square sig. squared sphericity assumed greenhouse-geisser huynh-feldt lower-bound cellphonemanipulation websites significant manipulation squaredf significant. tests between-subjects transformed average sig intercept .021 there significance manipulation.estimated marginal nonsocial estimated absence describe going on above image. while increases as well.example: race recognition scores analyzed spss two-way mixed factorial participant asian manipulated face variable. showed was .03 n .001 caucasian performing similarly overall. addition seen table .01 participants showing similar faces. contrast performed better facial stimuli compared opposite pattern faces looking at figure we see both races sized own-race findings notion an bias>