Results and Discussion SectionsYou have worked hard on this research project, and you are over halfway done!In the last project assignment, based on the data collected from your participants, you completed the Participants subsection for your research paper.Now, let’s work on what we have found and see whether your hypotheses (listed in your Introduction section) are supported by the data you collected. This assignment is about writing the Results and Discussion sections for your research paper.  One of the prerequisites for this course is Introductory Statistics (PSYC 3301). The Results section is where you would utilize what you learned in that class.If you need to refresh your memory on this subject, no worries; I have included a review and guide in the instructions below to help you interpret the statistical results and write up the findings using APA style. I recommend going over the instructions below as soon as possible, especially if you are not too familiar with statistical tests and results.  Download this PDF that shows the results and data you collected: Findings.pdf download  Download and carefully go over the instructions and examples in this PDF: Results & Discussion.pdf download  Review the sample papers, particularly the Results and Discussion sections. Note that these sample papers are NOT the sample proposals you have already gone over. Check out the Common Writing Issues and Questions page before you finalize and submit your assignment.
Results and Discussion Sections You have worked hard on this research project, and you are over halfway done! In the last project assignment, based on the data collected from your participants, you co
ANXIETY, STRESS, AND SMOKING 1 The Relationships among Anxiety, Stress, and Smoking Status by XXXXXXXXXXXXXXX Research Paper Submitted in Partial Fulfillment of the Requirement for PSYC 3304 and 3104 at The University of Texas Permian Basin August 5, 2021 ANXIETY, STRESS, AND SMOKING 2 Abstract Stress and anxiety are both conditions experienced by many human beings. Depending on the degree to which these variables are prevalent, each, either separately or together, has the potential to greatly hinder an individual and limit the ability to function efficiently. Therefore, it is important for health research to determine potential causes or exacerbating variables that may contribute to an individual’s health. S moking status, for example, i s a variable that has been shown to be related to both stress and anxiety . The purpose of the present study was to analyze the relationship between smoking status, anxiety , and stress. Data was collect ed from 492 participants via online questionnaire s avai lable through Qualtrics. The Depression Anxiety Stress Scale -2 (DASS -21) was used to measure participants stress and anxiety , and demographic information was gathered using the demographic questionnaire . Results displayed that smokers scored significantly higher on the anxiety scale than non -smokers. Smokers also scored significantly higher on the anxiety scale than non -smokers. Additionally, there was a positive correlation identified between anxiety score and stress score. These findings may be relevant t o and provide insight for medical as well as mental health professionals. Keywords: health, stress, anxiety, smoking status , correlation ANXIETY, STRESS, AND SMOKING 3 The Relationship among Anxiety, Stress, and Smoking Status Anxiety and stre ss are both h indering phenomena that occur frequently for many people in their day to day lives. Both anxiety and stress have been known to be associated w ith various ailments as well as general ability to function successfully . In order to fully understand the effects of stress and anxiety on performance and health, it is important to be aware of the factors that contribute to their occurrence. The present study attempted to analyze the factor, smoking status, as it is related to both anxiety and stress in order to det ermine if any correlations exist among the three factors. Research has shown that smoking s tatus may be an indicator of stress levels . In a study con ducted by Cao et al. (2012), researchers analyzed the smoking status and perceived stress levels of a grou p of migrant workers in rural China. Their results concluded that the manifestation of perceived stress among the workers exhibited an excess likelihood to be smokers , relative to low stress counterparts . Additionally, p ast studies have also indicated that stress can occur in the absence or reduction of smoking, if already a smoker . In a study conducted by Azagba and Sharaf (2012), findings concluded in their study on perceived stress associated with smoking bans at work that although th e bans may not be the main determinant of perceived stress in the work environment, there is a positive correlation. Additionally , a study from Allen et al. (2015) analyzed the effects of nicotine withdrawal on stress; a strong positive correlation was obs erved. Previous research has indicated that anxiety is also significantly associated with smoki ng. In a study conducted by Farris et al. (2014) , the researchers concluded that anxiety sensitivity correlated positively with nicotine dependence as well as o ther health hindering factors such as alcohol use, perceived barriers to smoking cessation, and severity of problems faced while attempting to stop. Although there is an underlying directionality problem ANXIETY, STRESS, AND SMOKING 4 in many of these findings, a study by Asbridge et al . (2013) pro vides some insight for the current rationale regarding smoking status as it relates to the variables . This study focused on non – smokers and the effects of second hand smoke. Analyses revealed that smoke exposure to non – smokers was associated wi th increased anxiety disorders as well as poor mental health and high stress. Therefore, although stress and anxiety hav e been shown to exist with the reduction of smoking, or in the absence of it, there is also indication of a causal nature in smoking as it is related to stress and anxiety, which is important for health research. Furthermore, i n the previously mentioned study con ducted by Cao et al. (2012) regarding stress and smoking, it should be noted that the researchers used two model s; life stress and work stress. Interestingly, findings displayed that the likelihood of smoking was more significant with the life stress model as opposed to the wo rk stress model. A different study conducted by Carpenter et al. (2011 ) examined this phenomenon of life st ress as it relates to anxiety. The res earch focused on gene environment interaction s which contributed to stress which , in turn, is correlated with anxiety outcomes, further evidence of the ongoing relationship among the three factors. The present study at tempted to demonstrate a correlation between anxiety and stress as well as a correlation among smoking status and each of these factors. Utilizing an online questionnaire, the researcher relied on self -reported measures . The researcher hypothesized that smokers are more likely to report being stressed compared to nonsmokers. The researcher also hypothesized that smokers are more likely to report having anxiety compared to non -smokers. Additionally, it was hypothesized that stress and anxiety correlate positively; as stress increases, anxiety tends to increase, as well. Method ANXIETY, STRESS, AND SMOKING 5 Participants The survey was conducted through Qualtrics. There were 492 participants total in this study. The age mean of these participants (except the 16 participants who did not report their age) was 31.27 with a standard deviation of 11.94. Of the 492 participants 168 (34.2%) were male whereas 320 (65.0%) were female and 4 (0.8%) reported other. Of all participants, 358 (72.8%) were non -Hispanic W hite/Caucasians, 38 (7.7%) were Black/African American, 28 (5.7%) were Hispanic/Latino(a), 24 (4.9%) were Asians/ Pacific Islanders, 14 (2.8%) were Native American, 21 (4.3%) were biracial/multiracial, and 9 (1.8%) reported other. In regards to smoking sta tus, 78 (15.9%) were smokers whereas 414 (84.1%) were non -smokers. Table 1 displays the demographic information of the participants. Measures Depression A nxiety Stress Scale. Anxiety and stress were assessed using the Depression Anxiety Stress Scale (DAS S-21; Lovibond & Lovibond, 1995; Appendix A). The questionnaire consisted of 21 items that included factors related to stress and anxiety such as overreaction and nervousness (e.g.; “I found it difficult to relax). Each item was rated on a 0 -3-point Likert -type scale that ranged from “did not apply” to “apply very much”. Cronbach’s alpha coefficient reported that DASS -21 used for this study has high reliability and validity: .81 for anxiety and .89 for stress (Lovibond & Lovibond, 1995). Demographic questionnaire. For each participant, a demographic questionnaire (Appendix B) was administered in order to collect basic demographic information such as smoking status, age, student status and year, gender, and ethnicity. Procedure ANXIETY, STRESS, AND SMOKING 6 The questio nnaire utilized for this study was the DASS -21, one of the five online questionnaires in a larger research study. The researcher collected the data via Qualtrics. Participants completed the survey in about 10 and 15 min, on average. Results The researcher hypothesized that smokers are more likely to report being stressed compared to nonsmokers. A one -way analysis of variance ( ANOVA ) was conducted in order to observe potential correlations between smokers and non -smokers. This hypothesis is supported as the results displayed a significant difference between stress scores in smokers ( M = 16.05, SD = 9.7 0), and non -smokers ( M = 12.45, SD = 8.26), F(1, 490) =11.77 , p = .001. Smokers scored signi ficantly higher on th e stress scale than non -smokers. The researcher also hypothesized that smokers are more likely to report having anxiety compared to non -smokers . A one -way ANOVA was conducted for this hypothesis, as well. The results supported the hypothesis as the results displayed a significant difference b etween anxiety scores for smokers ( M = 12.85, SD = 10.45) and anxiety scores for non -smokers ( M = 8.04, SD = 7.79), F(1, 490) = 22.16, p < .001. In other words, smokers scored significantly higher on the anxiety scale than non -smokers. Additionally, it was hypothesized that stress and anxiety correlate positively; as stress increases, anxiety tends to increase, as well. To test this hypothesis, a Pearson correlation was conducted. Results show there was a significant correlation between anxiety ( M = 8.80, SD = 8.44) and stress ( M = 13.02, SD = 8.60), r(490) = .78 , p < .001. In other words, there was a positive correlation identified between anxiety score and stress score. Discussion ANXIETY, STRESS, AND SMOKING 7 The present study attempted to analyze whether the factor, smoking status, is related to both anxiety and stress. The researcher hypothesized that smokers are more likely to report being stressed compared to nonsmokers. The results supported this hypothesis; smokers scored significantly higher on the stres s scale than non -smokers. The researcher also hypothesized that smokers are more likely to report having anxiety compared to non -smokers. Results supported this hypothesis, as well, as smokers scored significantly higher on the anxiety scale than non - smoke rs. Finally, it was hypothesized that stress and anxiety correlate positively; as stress increases, anxiety tends to increase, as well. Once again, results supported this hypothesis as there was a significant and positive correlation between anxiety and st ress. In most studies, including the study conducted by Cao, Cui, Rockett, and Yang (2012), results demonstrated a positive correlation among stress and anxiety. The results of the present study are consistent with thi s knowledge structure. This study is a lso consistent with the present hypothesis, regarding smoking and stress, as results concluded that the perception of stress more likely manifested in smokers compared to lower stress counterparts. However, there is also an inconsistency due to additional factors assessed in other studies but not examined in the present study such as entire smoke history and level of nicotine dependency. For example, the study conducted by Az agba and Sharaf (2012), studied a positive correlation between stress and workplace smoking bans. Additionally, a study from Allen et al. (2015) analyzed the effects of nicotine withdrawal on stress and a strong positive correlation was observed. These results are the opposite of the present study in terms of correlation. As for anxiety and smoking, the present study is consistent with the current structure of knowledge in that the variable does correlate positively with smoking. This is the result s of most other studies analyzing these variables, such ANXIETY, STRESS, AND SMOKING 8 as the study conducted by Farris et al. (2014) , concluding that there is , indeed , a positive correlation The researcher observed limitations to this study. Although the researcher did observe a statistical significance among the variable s, the participants were random and not controlled, it is difficult to determine whether individual differences contributed to the results of this study, s uch as gender or age; there could be an additional, confounding variable, damaging the internal validity of this study. Anoth er limitation could be the tru thfulness of the participants. It is possible that participants gave untrue answers in an attempt to manipulate results. The most important limitation would be the directionality problem. Although the variables correlate, this study does not determine the cause of these correlations. There is still further research needed in order to determine the directionality of these correlations. For example, these results display a correlation but it is unclear if stress causes smoking or sm oking causes stress . The re searcher recommends utilizing an experimental design in order to control confounding variables and improve internal validity. Utilizing twins, for example, would be a sufficient method of minimizing individual differences that result from factors such as g ender, personality, and age. Using another method, such as within -subjects designs, can also serve to minimize individual differences. Temporal factors should be considered, as well, for future studies, especially clinical observations, as temporal factors relate to level of dependency for smokers, which could directly affect the variables. In certain cases, that is, for heavy smokers, not smoking when having previously been a smoker could, could potentially lead to an increase in anxiety and stress, creati ng a negative correlation ANXIETY, STRESS, AND SMOKING 9 References Allen, S. S, Eberly, L. E., Grandits, G. A., Harrison, K., & Lawless, M. H. (2015). Perceived stress and smoking -related behaviors and symptomatology in male and female smokers . Addictive Behaviors , 51 , 80 -83. https://doi.org/ 10.1016/j.addbeh.2015.07.011 Asbridge, M., Ralph, K.., & Stewart, S. (2013). Private space second -hand smoke exposure and the mental health of non -smokers: a cross -sectional analysis of Canadian adults. Addictive Behaviors , 38 (3), 1679 -1686. http://dx.doi.org/10.1016/j.addbeh.2012.10.008 Azagba, S. , & Sharaf, M. (2012). The association between workplace smoking bans and self - perceived, work -related stress among smoking workers. BMC Public Health , 12 , 123. https://doi.org/ 10.1186/1471 -2458 -12 -123 Cao, R., Cui, X., Rockett, I., & Yang, T. (2012). Work stress, life stress, and smoking among rural -urban migrant workers in China . BMC public health , 12 , 979 . https://doi.org/ 10.1186/1471 - 2458 -12 -979 Carpenter, L. L., Nugent, N. R., Price, L., & Tyrka, A.R. (2011) . Gene -environment interactions: early life stress and risk for depressive and anxiety disorders . Psychopharmacology , 214 , 175. https://doi.org/ 10.1007/s00213 -010 -2151 -x Farris, S., Leventhal, A., Schmidt, N., & Zvolensky, M. (2014). Anxiety sensitivity mediates relations between emotional disorders and smoking. Psychology of addicted behaviors , 28 (3), 912 -920. https://doi.org/ 10.1037/a0037450 Lovibond, P. F., & Lovibond, S. H. (1995). The structure of negative emotional states: comparison of the depression a nxiety stress scales (DAS -21) with the Beck Depression and Anxiety Inventories. Behavior Research and Therapy, 33 (3), 335 -343. https://doi.org/ 10.1016/0005 -7967(94)00075 -U ANXIETY, STRESS, AND SMOKING 10 Table 1 Participant Demographic Information Variable Age M = 31.27 SD = 11.94 Gender Female n = 320 65.0% Male n = 168 34.2% Other n =4 0.8% Ethnicity White/Caucasian (Non -Hispanic) n = 358 72.8% Black/ African American n = 38 7.7% Hispanic/Latino n = 28 5.7% Asian/Pacific Islander n = 24 4.9% Biracial/Multiracial n = 21 4.3% Native American n = 14 2.8% Other n = 9 1.8% Smoking Non -smokers n =414 84.1% Smokers n = 78 15.9% Note . N = 492. Sixteen participants did not report their age. ANXIETY, STRESS, AND SMOKING 11 Appendix A Depression Anxiety Stress Scale -21 (DASS -21) Please read each statement and circle a number 0, 1, 2 or 3 that indicates how much the statement apply to you in general. There are no right or wrong answers. Do not spend too much time on any statement. The rating scale is as follows: 0 Did not apply to me at all 1 Applied to me to some degree, or some of the time 2 Applied to me to a considerable degree, or a good part of time 3 Applied to me very much, or most of the time 1. I found it hard to wind down 0 1 2 3 2. I was aware of dryness of my mouth 0 1 2 3 3. I couldn't seem to experience any positive feeling at all 0 1 2 3 4. I experienced breathing difficulty (e.g., excessively rapid breathing, breath lessness in the absence of physical exertion) 0 1 2 3 5. I found it difficult to work up the initiative to do things 0 1 2 3 6. I tended to over -react to situations 0 1 2 3 7. I experienced trembling (e.g., in the hands) 0 1 2 3 8. I felt that I was using a lot of nervous energy 0 1 2 3 9. I was worried about situations in which I might panic and make a fool of myself 0 1 2 3 10 . I felt that I had nothing to look forward to 0 1 2 3 ANXIETY, STRESS, AND SMOKING 12 11 . I found myself getting agitated 0 1 2 3 12 . I found it difficult to relax 0 1 2 3 13 . I felt down -hearted and blue 0 1 2 3 14 . I was intolerant of anything that kept me from getting on with what I was doing 0 1 2 3 15 . I felt I was close to panic 0 1 2 3 16 . I was unable to become enthusiastic about anything 0 1 2 3 17 . I felt I wasn't worth much as a person 0 1 2 3 18 . I felt that I was rather touchy 0 1 2 3 19 . I was aware of the action of my heart in the absence of physical exertion (e.g., sense of heart rate increase, heart missing a beat) 0 1 2 3 20 . I felt scared without any good reason 0 1 2 3 21 . I felt that life was meaningless 0 1 2 3 Source: Lovibond & Lovibond (1995) ANXIETY, STRESS, AND SMOKING 13 Appendix B Demographic Questionnaire Below are a series of demographic questions. Please answer them as accurately as you can. Be assured that information provided is confidential. 1. Do you smoke? (If you smoke, please indicate how many cigarettes you smoke per day ) □Yes: _____________ □No 2. What is your age (years old)? __________ 3. Gender (please select one): □ Male □ Female □ Other: __________ 4. Are you currently a student? □ Yes □ No 5. Year in college (please select one): □ Freshman □ Sophomore □ J unior □ Senior □ Graduate Student □ Other: __________ □ Not Applicable 6. What is your ethnicity? □ White/Caucasian (non -Hispanic) □ Black/African American □Hispanic/Latino(a) □ Asian/Pacific Islanders □ Native American □ Biracial /Multiracial: ____________________________ □ Other: ____________________________ Results and Discussion Sections You have worked hard on this research project, and you are over halfway done! In the last project assignment, based on the data collected from your participants, you co PSYC 3304 & 3104 , Research Project Findings Page 1 of 6 See the copyright statement in the course syllabus. Research Project Statistical Findings Data were analyzed using the Statistical Package for the Social Sciences (SPSS ®). Table of Content with Page Numbers:  Descriptive statistics (m eans, standard deviations , and frequency distributions ) of the variables – page 1 of this file  Correlations among the variables – page 2 of this file  Gender differences examined using analys es of variance (ANOVA s) – page s 3 and 4 of this file  D ifferences between smoker and non -smokers examined using ANOVA s – pages 5 and 6 of this file Descriptive statistics of the variables : Descriptive statistics ( means, standard deviations, and frequency distributions ) of the variables are listed in the table below. Descriptive Statistics Mean Std. Deviation N Self_Esteem 19.4857 5.40383 945 SleepDisturbance 22.3016 5.85263 945 Optimism 10.2688 4.62534 945 Resilience 3.4439 .78051 945 Anxiety 9.0944 8.58222 943 Depression 8.0996 8.75413 944 Stress 13.3192 9.01844 943 Age 34.0647 12.68467 927 (Gender) (Smokers and Non -Smokers ) PSYC 3304 & 3104 , Research Project Findings Page 2 of 6 See the copyright statement in the course syllabus. Correlations among the variables : Pearson correlations were conducted. See the results below. Correlations Self_Esteem SleepDisturbance Optimism Resilience Anxiety Depression Stress Age Self_Esteem Pearson Correlation 1 .314 ** -.645 ** -.602 ** .480 ** .630 ** .482 ** -.144 ** Sig. (2 -tailed) .000 .000 .000 .000 .000 .000 .000 N 945 945 945 945 943 944 943 927 SleepDisturbance Pearson Correlation .314 ** 1 -.334 ** -.304 ** .315 ** .341 ** .385 ** -.034 Sig. (2 -tailed) .000 .000 .000 .000 .000 .000 .297 N 945 945 945 945 943 944 943 927 Optimism Pearson Correlation -.645 ** -.334 ** 1 .620 ** -.462 ** -.550 ** -.487 ** .177 ** Sig. (2 -tailed) .000 .000 .000 .000 .000 .000 .000 N 945 945 945 945 943 944 943 927 Resilience Pearson Correlation -.602 ** -.304 ** .620 ** 1 -.426 ** -.502 ** -.471 ** .153 ** Sig. (2 -tailed) .000 .000 .000 .000 .000 .000 .000 N 945 945 945 945 943 944 943 927 Anxiety Pearson Correlation .480 ** .315 ** -.462 ** -.426 ** 1 .723 ** .738 ** -.155 ** Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 N 943 943 943 943 943 943 943 925 Depression Pearson Correlation .630 ** .341 ** -.550 ** -.502 ** .723 ** 1 .681 ** -.082 * Sig. (2 -tailed) .000 .000 .000 .000 .000 .000 .013 N 944 944 944 944 943 944 943 926 Stress Pearson Correlation .482 ** .385 ** -.487 ** -.471 ** .738 ** .681 ** 1 -.186 ** Sig. (2 -tailed) .000 .000 .000 .000 .000 .000 .000 N 943 943 943 943 943 943 943 925 Age Pearson Correlation -.144 ** -.034 .177 ** .153 ** -.155 ** -.082 * -.186 ** 1 Sig. (2 -tailed) .000 .297 .000 .000 .000 .013 .000 N 927 927 927 927 925 926 925 927 **. Correlation is significant at the 0.01 level (2 -tailed). *. Correlation is significant at the 0.05 level (2 -tailed). PSYC 3304 & 3104 , Research Project Findings Page 3 of 6 See the copyright statement in the course syllabus. Gender differences : One -way analys es of variance (ANOVA s) were conducted. There are only four in the other category, so these four participants were excluded from the “gender differences” analyses below. Note: With 2 groups (male and female), an independent -samples t test is another common test to use . Descriptives N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum Lower Bound Upper Bound Self_Esteem 1 Male 302 18.7517 5.14261 .29592 18.1693 19.3340 3.00 33.00 2 Female 639 19.8279 5.49794 .21750 19.4008 20.2549 10.00 36.00 Total 941 19.4825 5.40714 .17627 19.1365 19.8284 3.00 36.00 SleepDisturbance 1 Male 302 21.3013 6.10255 .35116 20.6103 21.9924 8.00 36.00 2 Female 639 22.7731 5.68644 .22495 22.3313 23.2148 8.00 40.00 Total 941 22.3007 5.86042 .19104 21.9258 22.6757 8.00 40.00 Optimism 1 Male 302 10.5166 4.41886 .25428 10.0162 11.0169 -9.00 20.00 2 Female 639 10.1424 4.72870 .18706 9.7751 10.5097 -4.00 20.00 Total 941 10.2625 4.63247 .15101 9.9661 10.5589 -9.00 20.00 Resilience 1 Male 302 3.5579 .74390 .04281 3.4737 3.6422 1.00 5.00 2 Female 639 3.3905 .79065 .03128 3.3290 3.4519 1.00 5.00 Total 941 3.4442 .77949 .02541 3.3943 3.4941 1.00 5.00 Anxiety 1 Male 301 8.1728 7.93621 .45744 7.2726 9.0729 .00 42.00 2 Female 638 9.5047 8.84362 .35012 8.8172 10.1922 .00 42.00 Total 939 9.0777 8.58156 .28005 8.5281 9.6273 .00 42.00 Depression 1 Male 302 7.9272 8.23460 .47385 6.9947 8.8596 -12.00 42.00 2 Female 638 8.1693 8.98260 .35562 7.4709 8.8676 .00 42.00 Total 940 8.0915 8.74561 .28525 7.5317 8.6513 -12.00 42.00 Stress 1 Male 301 12.5914 8.64614 .49836 11.6106 13.5721 .00 42.00 2 Female 638 13.6520 9.16607 .36289 12.9394 14.3646 .00 42.00 Total 939 13.3120 9.01169 .29409 12.7349 13.8892 .00 42.00 PSYC 3304 & 3104 , Research Project Findings Page 4 of 6 See the copyright statement in the course syllabus. Gender differences (continued): ANOVA Sum of Squares df Mean Square F Sig. Self_Esteem Between Groups 237.522 1 237.522 8.186 .004 Within Groups 27245.438 939 29.015 Total 27482.961 940 SleepDisturbance Between Groups 444.213 1 444.213 13.101 .000 Within Groups 31839.676 939 33.908 Total 32283.889 940 Optimism Between Groups 28.708 1 28.708 1.338 .248 Within Groups 20143.458 939 21.452 Total 20172.166 940 Resilience Between Groups 5.753 1 5.753 9.555 .002 Within Groups 565.401 939 .602 Total 571.154 940 Anxiety Between Groups 362.822 1 362.822 4.947 .026 Within Groups 68714.502 937 73.335 Total 69077.325 938 Depression Between Groups 12.017 1 12.017 .157 .692 Within Groups 71808.115 938 76.554 Total 71820.132 939 Stress Between Groups 230.084 1 230.084 2.839 .092 Within Groups 75945.490 937 81.052 Total 76175.574 938 PSYC 3304 & 3104 , Research Project Findings Page 5 of 6 See the copyright statement in the course syllabus. Differences between smokers and non -smokers : One -way analys es of v ariance (ANOVA) were conducted to examine the differences between smokers and non -smokers. Note: With 2 groups (smokers and non -smokers), an independent -samples t test is another common test to use . Descriptives N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum Lower Bound Upper Bound Self_Esteem 1 Smoker 138 21.0217 6.39510 .54439 19.9453 22.0982 3.00 36.00 2 Non -Smoker 807 19.2230 5.17460 .18215 18.8655 19.5806 10.00 36.00 Total 945 19.4857 5.40383 .17579 19.1407 19.8307 3.00 36.00 SleepDisturbance 1 Smoker 138 24.0000 5.24022 .44608 23.1179 24.8821 10.00 35.00 2 Non -Smoker 807 22.0112 5.90526 .20787 21.6031 22.4192 8.00 40.00 Total 945 22.3016 5.85263 .19039 21.9280 22.6752 8.00 40.00 Optimism 1 Smoker 138 8.8261 5.41411 .46088 7.9147 9.7374 -9.00 20.00 2 Non -Smoker 807 10.5155 4.43362 .15607 10.2091 10.8218 -4.00 20.00 Total 945 10.2688 4.62534 .15046 9.9735 10.5641 -9.00 20.00 Resilience 1 Smoker 138 3.2742 .85170 .07250 3.1308 3.4175 1.17 5.00 2 Non -Smoker 807 3.4729 .76448 .02691 3.4201 3.5258 1.00 5.00 Total 945 3.4439 .78051 .02539 3.3941 3.4937 1.00 5.00 Anxiety 1 Smoker 137 12.3066 9.50508 .81207 10.7006 13.9125 .00 42.00 2 Non -Smoker 806 8.5484 8.29889 .29232 7.9746 9.1222 .00 40.00 Total 943 9.0944 8.58222 .27948 8.5459 9.6428 .00 42.00 Depression 1 Smoker 138 11.1159 11.16569 .95049 9.2364 12.9955 -12.00 42.00 2 Non -Smoker 806 7.5831 8.16871 .28773 7.0183 8.1479 .00 36.00 Total 944 8.0996 8.75413 .28492 7.5404 8.6587 -12.00 42.00 Stress 1 Smoker 137 16.2336 9.94711 .84984 14.5530 17.9142 .00 42.00 2 Non -Smoker 806 12.8238 8.76165 .30862 12.2180 13.4296 .00 42.00 Total 943 13.3192 9.01844 .29368 12.7428 13.8955 .00 42.00 PSYC 3304 & 3104 , Research Project Findings Page 6 of 6 See the copyright statement in the course syllabus. Differences between smokers and non -smokers (continued): ANOVA Sum of Squares df Mean Square F Sig. Self_Esteem Between Groups 381.271 1 381.271 13.226 .000 Within Groups 27184.786 943 28.828 Total 27566.057 944 SleepDisturbance Between Groups 466.148 1 466.148 13.793 .000 Within Groups 31868.900 943 33.795 Total 32335.048 944 Optimism Between Groups 336.347 1 336.347 15.971 .000 Within Groups 19859.382 943 21.060 Total 20195.729 944 Resilience Between Groups 4.657 1 4.657 7.699 .006 Within Groups 570.426 943 .605 Total 575.083 944 Anxiety Between Groups 1653.863 1 1653.863 22.978 .000 Within Groups 67728.737 941 71.975 Total 69382.600 942 Depression Between Groups 1470.564 1 1470.564 19.567 .000 Within Groups 70796.075 942 75.155 Total 72266.640 943 Stress Between Groups 1361.414 1 1361.414 17.024 .000 Within Groups 75253.508 941 79.972 Total 76614.923 942 Results and Discussion Sections You have worked hard on this research project, and you are over halfway done! In the last project assignment, based on the data collected from your participants, you co PSYC 3304 & 3104 , Results & Discussion Sections Page 1 of 16 See the copyright statement in the course syllabus. Results and Discussion Section s Grade Points: 35 points Due Date: Sunday, 7/11/2021 , 11:59 PM Preparation:  Download the Findings PDF on Canvas. It shows the results and data collected from your participants.  The assigned readings are meant to help you with interpret ing the data and writing your research paper. Review the following chapters/sections if you need:  Chapter 16.3 of the Gravetter & Forzano (GF) textbook  Chapter 3 of the Mitchell, Jolley, & O’Shea (MJO) textbook  Chapter 15 of the GF textb ook – for data analysis and interpretation  Chapter s 6 & 12 of the GF textbook – for evaluating limitations and threats to the validity of a study  APA, APA Everywhere page on Canvas  Carefully go over the instructions and examples below as well as the Results and Discussion sections within the sample papers posted on Canvas.  Note that the sample papers are NOT the sample proposals you have already gone over. They are research papers (not proposals), so be sure to go over them.  You can use the sample papers and examples to guid e you. However, write your study/paper in your own words ; do not plagiarize, or do not simply copy and paste from other people’s work (e.g., samples/examples, articles). Instruction: Results  The main purpose of the Results section is to tell the readers what statistical tests you used to analyze the data and the statistical results. Therefore, this section is quite technical and straightforward. One of the prerequisites for this course is Intr oductory Statistics (PSYC 3301). The Results section is where you would utilize what you learned in that class (and what you read in Chapter 15 of the GF textbook).  See the Findings PDF on Canvass and pages 5-16 of this file (which contain the SPSS output s that show the statistical findings of this research project ).  There are various ways to write a Results section. A straightforward method is to break it down by hypotheses and talk about them one by one. Here is a checklist for your Results section:  Restate your first hypothesis (what you wrote for your Introduction section).  Then describe what test was conducted to test the first hypothesis.  Report and interpret the findings.  Include the statistical results: M, SD , degrees of freedom (df), r or F (depending on the test), and p value.  Restate your second hypothesis.  Then describe what test was conducted to test the second hypothesis.  Report and interpret the findings. PSYC 3304 & 3104 , Results & Discussion Sections Page 2 of 16 See the copyright statement in the course syllabus.  Include the statistical results: M, SD , degrees of freedom (df), r or F (depending on the test), and p value.  Restate your third hypothesis.  Then describe what test was conducted to test the second hypothesis.  Report and interpret the findings.  Include the statistical results: M, SD , degrees of freedom (df), r or F (depe nding on the test), and p value.  When reporting the results. Do not merely state if the result s were significant or not; provide interpretation s.  For example, instead of writing “ chocolate consumption was significantly related to happiness ,” here is a better way to report this finding: “results showed that happiness level increased as chocolate consumption increased.”  Another example: “results showed that there was a significant negative correlation between age and chocolate consumption; the younger the participants, the more chocolate they consumed.”  See how to interpret the SPSS output s and examples on pages 5 to 16.  Pearson correlations: pages 5-7  Gender differences: pages 8-10  Differences between smokers and non -smokers: pages 11 -13  Sample Results section s: page s 14-16 Discussion  Begin with a restatement of the purpose of the present study and your hypotheses.  Briefly restate your major results.  Mention whether y our hypotheses were supported.  Do NOT repeat all the numerical statistics that appear in the Results section .  Example ( based on the sample Results shown on page 15 of this file ): “The researcher hypothesized that resilience would increase as self -esteem increases. It was also hypothesized t hat males would have higher resilience level and lower self -esteem scores compared to females. The correlation found between resilience and self -esteem was not in the same direction as the researcher expected. It was found that resilience decreased as self -esteem increased. The last two hypotheses, however, were supported by the data. Compared to their counterparts, females showed higher levels of self -esteem, and males reported stronger resilience.  Then, relate your res ults to the work of others; explain how your study fits into the existing structure of knowledge of the area.  You may talk about the research studies you discussed in your I ntroduction section and how your findings relate to those studies.  Keep in mind that you must cite your sources; otherwise, it is considered as plagiarism. PSYC 3304 & 3104 , Results & Discussion Sections Page 3 of 16 See the copyright statement in the course syllabus.  Next, discuss the limitation s of your study , especially factors that affect the generalization of the results. Talk about the improvements that could be made to the study.  Typically, you would wan t to report 1 to 3 limitations.  Chapter 6 of the GF textbooks talks about how to identify threats to the internal and external validity of a study, and Chapter 12 discusses the limitations of correlational research. You can use the textbook to help you br ainstorm and write about the limitations of your study in this section.  If you did not find statistical significance, you can discuss issues with the methods of your study that may have led to the non -significant findings.  If the results are different th an what you expected (your hypotheses), you can discuss issues with the methods of your study that may have led to the unexpected findings.  Finally, suggest furth er research that could be done.  The page limit for the Discussion section is 1.5 -2 full pa ges . This limit does not include the title page, Results, References, or other sections you may have in this paper .  See the sample p apers on Canvas and use them as a guide. References  The References section starts on a separate page. It does not immediately follow your last paragraph in the proposal; your References section should start on a new (separate) page.  Provide a list of the articles you have cited in your Discussion sections.  Note that this may be different from the list you have for y our proposal (i.e., Introduction and Method section); for this assignment, only include the ones discussed in your Discussion section (and Results section if you include any citations there).  Your in -text citations and references should be consistent. If a source/reference is not discussed in text (i.e., in the paper), do not cite it in the References.  You must use the latest version (7th edition) of American Psychological Association (APA) style for your c itations .  Review the APA, APA Everywhere page on Canvas .  You can also use the Purdue Online Writing Lab ( OWL ) website to help you check your citations: o https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_st yle_gui de/in_text_citations_author_authors.html o https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_form atting_and_style_gui de/reference_list_author_authors.html PSYC 3304 & 3104 , Results & Discussion Sections Page 4 of 16 See the copyright statement in the course syllabus. Required Format and Style  Type your paper/assignment directly in a word document. Do not submit a hand -written version.  As Results and Discussion sections are parts of a research paper (which is typically written after data collection and analysis), use past tense when describing this study and the results .  Your paper/assignment must be typed, double -spaced with 1 -inch margins and typed using 12 -point Times New Roman font.  The for mat of the paper/assignment should be APA style , including how you report the statistical findings . For instance, in the Results section, M, SD , r, F, and p are italicized.  All articles/references that you include in your proposal must be properly cited (both in text and in the References section) using the APA style .  The page limit for the Discussion section is 1.5 -2 full pages . This limit does not include the title page, Results , References, or other sections you may have in this paper/assignment .  There is no page limit for the Results section; however, be comprehensive yet concise.  Use complete sentences and check your grammar , punctuation, spelling, and word usage.  When talking about statistical results/findings that a re not significant, use non -significant instead of insignificant . The word, insignificant , implies that the study is not important, which is not the same as not finding statistical significance in the study.  Instead of look at or see , use conduct , examine , study , assess , observe , measure , count , etc.  Use because or as instead of since . Use since only when referring to time (meaning “after”).  Limit the use of while to its temporal meaning (i.e., events that occur simultaneously). Consider using although , even though , whereas , etc .  Important reminder: Examples provided in this file and sample papers are meant to help you learn how to interpret the results. You may wish to follow similar format as the examples are APA style. However, do NOT simply copy them and change only a few word s. You need to use your own words when writing your papers/ assignments. Otherwise, it is considered as plagiarism.  Points will be deducted if your paper does not adhere to the required format /style. Submit Your Proposal  Save your file regularly. When you are done, upload your saved word document ( must be .doc or .docx ) to the Results and Discussion Sections under the Assignments tab on Canvas. This assignment must be completed and submitted individually as indicated in the course syllabus. Let the instructor know if you have any questions or concerns about this assignment. Again, see the Findings PDF on Canvas for the data analysis results. Go over pages 5 -16 of this file to see how to interpret the statistical results; some examples are also p rovided in this file. Also, remember to go over the sample papers on Canvas . Use the sample papers and examples in this file as a guide when writing your Results, Discussion, and research paper. PSYC 3304 & 3104 , Results & Discussion Sections Page 5 of 16 See the copyright statement in the course syllabus. Pearson Co rrelations (Apply to All Students)  Use the Findings PDF file to examine whether your hypotheses were supported.  Look for the information you need to report your results based on what variab les you chose for your project .  In this e xample , I chose self -esteem , resilience , and gender . H1: The resea rcher hypothesized that there would be a positive correlation between self -esteem and resilience ; resilience would increase as self -esteem increases. o My first hypothesis tests the relationship between self -esteem and resilience . o Pages 1 -2 of the Findings PDF shows the descriptive statistics and correlations am ong the variables. I will look for the informatio n I need based on the variables I chose , self -esteem and resilience , which are highlighted below. In the Correlations matrix, you can see that each comparison has been done twice. The two highlighted cells have the same numbers. This is because SPSS correlated Self -Esteem with Resilience , and then Resilience with Self - Esteem , which are the same compari son. Therefore, when interpreting the Correlations matrix, you can draw a diagonal line across the table and focus on either the top half or the bottom half of the table. PSYC 3304 & 3104 , Results & Discussion Sections Page 6 of 16 See the copyright statement in the course syllabus. Here, I will choose the top half to focus on (but if you use the bottom half is fine as well): 1. How would I report these numbers using APA style?  r(943 ) = -.60, p < .001 2. What does this mean? (Here is a brief review for the Introductory Statistics course you took)  r stands for correlation.  943 : this is the degrees of freedom (df). In correlation, df = N – 2, which is the sample size used to compute this correlation minus the number of variables you have for that correlation. In the highlighted cell, it shows that N = 945 , and you have 2 variable s (resilience and self -esteem) for this correlation. Therefore, 945 – 2 = 943 .  -.60: This is the correlation between self -esteem and resilience . There is a negative sign, which means that there is a negative correlation between the two variables. In other words, resilience decreased as self - esteem increase d, or self -esteem decreased as resilience increased. This finding does not support my first hypothesis (stated on page 3). Note that we typically round the correlation coefficients to 2 decimal places.  The row “Sig. (2 -tailed)” shows you the p values . o A p value is the probability that the results you see are due to chance (and not due to relationship between the variables, gender differences, treatment effect, or whatever you are testing ). That’s why we would like the p value to be small (meaning unlikely due to chance ). Typically, f or the findings to be considered significant, p needs to be smaller or equal to .05 (5% of chan ce). Review pages 395 (C hapter 15) of the GF textbook . o It shows “.000” in the table; this p value is smaller than .05, which means the correlation between these two variables is significant. o Keep in mind that “.000” does not mean that p = .000 (do NOT ever report p = .000 because p value would never be exactl y 0). It shows “.000” because the p value is very small (smaller than .001). o If you double -click on the p value in the cell using SPSS , you can actually see the exact p value. In this example (shown in the screenshot to the right) , the p value is 3 x 10 -94, meaning there are 93 zeros in front of 3 before reaching the decimal point. As you can see, that’s a very small p value. o Therefore, if you see “.000” for the p value, put p < .001 in your paper. 3. How do I write up this finding for my hypothesis testing?  Restate the hypothesis.  Mention what test was used (i.e., Pearson correlation) .  Include the statistics. For P earson correlations, report M and SD for the variables, and r, df, and p for the correlation.  Talk about if it’s statistically significant (based on the p value) and interpret the relationship.  Discuss whether the hypothesis was supported.  See examples on the next page (page 7) . PSYC 3304 & 3104 , Results & Discussion Sections Page 7 of 16 See the copyright statement in the course syllabus. Here is an example of how to write up this finding : It was hypothesized that there would be a positive correlation between self -esteem and resilience ; resilience would increase as self -esteem increases. To test this hypothesis, Pearson correlation was conducted. Results showed that there was a significant negative correlation between self -esteem (M = 19.49 , SD = 5.40 ) and resilience (M = 3.44 , SD = 0.78) , r(943 ) = -.60, p < .001 . In other words, resilience decreased as self - esteem increased . Therefore, the first hypothesis was not supported. Here is another example of how to write up the same finding: The researcher hypothesized that resilience would increase as self -esteem increases . However, this hypothesis was not supported as the results of Pearson correlation showed the opposite direction. T here was a significant negative correlation between these two variables, r(943 ) = -.60, p < .001 ; r esilience (M = 3.44 , SD = 0.78 ) decreased as self -esteem (M = 19.49 , SD = 5.40 ) increased . 4. How do I report my p value if it’s smaller than .05 but larger than .001? For example : the correlation between age and depression  Report close to exact p value.  In this example, age and depression : r(924 ) = -.08 , p = .0 13 5. What if my p value is larger than .05 (non -significant) ? For example: age and sleep disturbance .  Report close to exact p value.  In this example, a ge and sleep disturbance : r(925 ) = -.03 , p = .297  This finding means that there was no significant correlation between age and sleep disturbance .  However, you still need to report the results . See the example below .  Here is an example for how to write up a non -significant correlation : Researcher hypothesized that sleep disturbance would increase as age increases. However, this hypothesis was not supported. Results of Pearson correlation showed a non -significant relationship between age (M = 31. 06 , SD = 12.68 ) and sleep disturbance (M = 22.30 , SD = 5.85 ), r(925) = -.03, p = .297 . 6. Looking at the correlation matrix table, why are some Ns different? For instance, self -esteem and resilience has N of 945, whereas self -esteem and age has N of 927.  Some people did not report their age in the survey, so those data points could not be included in assessing the correlation between age and another variable. 7. For the demographic variable, if I chose age , do I use the same Correlation matrix and Descriptive Statistics table to interpret the findings?  Yes. For instance, if I chose self -esteem , resilience , and age , I will examine the correlations between ( 1) age and self -esteem as well as between ( 2) age and resilience .  The way to interpret these correlations and write them up are the same as the examples shown above.  Here is another example: It was hypothesized that resilience would increase as age increases . This hypothesis was supported as the results of Pearson correlation showed that there was a significant positive correlation between age (M = 34.06 , SD = 12.68 ) and resilience (M = 3.44 , SD = 0.78 ), r(925) = .15 , p < .001 . Older individuals showed higher resilience compared to younger individuals. PSYC 3304 & 3104 , Results & Discussion Sections Page 8 of 16 See the copyright statement in the course syllabus. Gender D ifferences (For Students who Chose Gender for Their Project) Example: I chose self -esteem , resilience , and gender . H2: It was hypothesized that females would have higher self -esteem compared to males. H3: It was hypothesized that males would have higher resilience compared to females. o My last two hypotheses test whether gender is associated with self -esteem and resilience . o Page s 3 -4 of the Findings PDF shows the gender differences. I will look for the informatio n I need based on the variables I chose , self -esteem and resilience , which are highlighted below. o For this project , 4 participants reported other for their gender. Because of the drastic difference s between the group sizes (4 versus 302 males and 4 versus 639 females), these 4 participants were excluded from the hypotheses testing. If you chose gender as your demographic variable, mention this in y our Results section. For this project, one -way analys es of variance (ANOVA s) were conducted to test the gender differences. However, for your reference: w ith 2 groups (male and female), an independent -samples t test is another common test to use. PSYC 3304 & 3104 , Results & Discussion Sections Page 9 of 16 See the copyright statement in the course syllabus. Gender differences in self -esteem (my example H2) and depression (example H3) : 8. How would I report these numbers using APA style?  Gender difference in self -esteem between male (M = 18.75 , SD = 5.14 ) and female (M = 19.83 , SD = 5.50 ): F(1, 939) = 8.19, p = .004.  Gender difference in resilience between male (M = 3.56 , SD = 0.74 ) and female (M = 3.39 , SD = 0.79 ): F(1, 939 ) = 9.56 , p = .002 . 9. What does this mean?  F is the ratio of between -groups variability and within -group variability. o Self -esteem: 8.186 = 237 .522 29.015 o Resilience: 9.555 = 5.753 0.602 o In hypothesis testing, if there are difference s between groups, in this case between males and females, we would th ink that they are due to gender difference. o If there are differences within groups, we think of these differences as things we c an not explain (error). We don’t know why people in the same group have different scores; these are things we cannot account for in our study (e.g., individual differences, error). o Therefore, the ratio of between -groups variability and within -group s variability is basically the ratio between what we can explain and what we cannot explain , what we know versus what we don’t know , or what we can account for versus what we cannot account for .  1: this is the degrees of freedom (df) between groups. df between = number of groups – 1 = 2 groups (males and females) – 1 = 1  939 : this is df within groups. df within = (n1 – 1) + (n 2 – 1) = ( number of males – 1) + (number of females – 1) = ( 302 – 1 ) + ( 639 – 1) = 939  The row “Sig.” shows you the p values . o See the p value explanations on page 6 of this file (under Question #2) . o Self -esteem:  p = .004, which is smaller than .05. Therefore, the gender difference was statistically significant for this variable .  In other words, the average (mean ) self -esteem score for males was 18.75 , and the m ean self -esteem score for females was 19.83 . The mean d ifference between the two groups was 1.08 (19. 83 – 18.75 ), and having a p value smaller than .05 means that this 1.08 -point difference i n self -esteem was statistically significant. To be more specific, females showed a significantly higher level of self -esteem compared to males. o Resilience:  p = .002, which is smaller than .05. Therefore, the gender difference was statistically significant for this variable.  In other words, the mean resilience score for males was 3.5 6, and the m ean resilience score for females was 3.39. The mean difference between the two groups was 0.1 7 (3.5 6 – 3.39), and having a p value smaller than .05 means th at this 0.17 -point difference in resilience was statistically significant. To be more specific, males scored significantly hi gher on the resilience scale compared to females. PSYC 3304 & 3104 , Results & Discussion Sections Page 10 of 16 See the copyright statement in the course syllabus. 10. How do I write up this finding for my hypothesis testing?  Restate the hypothes es.  Mention what test s were used ( i.e., one -way analys es of variance [ANOVAs] ).  Include the statistics. For each ANOVA, repor t M and SD for the groups, and F, df, and p for the gender differences.  Talk about if it’s statistically significant (based on the p value) and interpret the relationship.  Discuss whether the hypothesis was supported.  See the example below.  Here is an example: Two one -way analyses of variance (ANOVAs) were conducted to test the last two hypotheses , which focused on gender differences. Four participants reported other for their gender. Because of the differences between the group sizes, these fou r participants were excluded from the hypotheses testing. It was hypothesized that females would have higher self -esteem compared to males. Results supported the second hypothesis in showing that female participants (M = 19.83, SD = 5.50) had significantly higher self -esteem compared to the male participants ( M = 18.75, SD = 5.14), F(1, 939) = 8.19, p = .004. The third hypothesis states that males would have higher resilience compared to females. The findings also supported the las t hypothesis as males (M = 3.56, SD = 0.74) scored higher on the resilience scale compared to females (M = 3.39, SD = 0.79), F(1, 939) = 9.56, p = .002. 11. What if my p value is larger than .05 (non -significant) ?  As you can see on page 4 of the Findings PDF, gender differences in optimism , depression , and stress were not statistically significant (i.e., their p values are larger than .05) .  If you chose one or more of those non -significant variables for your project, you still need to report the results and the close to exact p value. See the example below.  Here is an example for the non -significant gender difference in optimism : Researcher hypothesized that males would be more optimistic compared to females. However, this hypothesis was not supported. Results of a one -way ANOVA showed a non -significant difference in optimism between males (M = 10.52, SD = 4.42) and females (M = 10 .14, SD = 4.73) , F(1, 939) = 1.34, p = .248.  What does this non -significant p value (p > .05) mean? o In this example, the gender difference in optimism yielded p = . 248 , which is larger than .05. Therefore, it means that the results were likely due to chance (not due to gender difference). o In other words, the mean optimism score for males was 10.52 , and the m ean self -esteem score for females was 10.14 . The mean difference between the two groups was 0. 38 (10.52 – 10.14 ), and having a p value larger than .05 means that this 0.38 -point difference i n optimism was NOT statistically significant. To be more specific, there was no gender difference in optimism levels. o Keep in mind that when reporting statistical results/findings that are not significant, use non – significant instead of insignificant . The word, insignificant , implies that the study is not important, which is not the same as not finding statistical significance in the study. PSYC 3304 & 3104 , Results & Discussion Sections Page 11 of 16 See the copyright statement in the course syllabus. Differences between Smokers and Non -Smokers (For Students who Chose Smoking Status for Their Project) In the example below , I chose resilience , depression , and smoking status . H2: It was hypothesized that non -smokers would have higher resilience compared to smokers. H3: It was hypothesized that smokers would have higher depression scores compared to non -smokers. o My last two hypotheses test whether smoking status is associated with resilience and depression . o Page s 5-6 of the Findings PDF shows differences between smokers and non -smokers. I will look for the informatio n I n eed based on the variables, resilience and depression , which are highlighted below. For this project, one -way analys es of variance (ANOVA s) were conducted to test differences between smokers and non -smokers. However, for your reference: with 2 groups ( smokers and non -smokers ), an independent -samples t test is another common test to use. PSYC 3304 & 3104 , Results & Discussion Sections Page 12 of 16 See the copyright statement in the course syllabus. Differences between smokers and non -smokers in resilience (H2) and depression (H3) : 12. How would I report these numbers using APA style?  Difference in resilience between smokers (M = 3.27 , SD = 0.85 ) and non -smokers (M = 3.47 , SD = 0.76 ): F(1, 943 ) = 7.70 , p = . 006  Difference in depression between smokers (M = 11.12 , SD = 11.17 ) and non -smokers (M = 7.58 , SD = 8.17 ): F(1, 943 ) = 19.57 , p < .001 13. What does this mean?  F is the ratio of between -groups variability and within -group variability. o Resilience: 7.699 = 4.657 0.605 o Depression : 19.567 = 1470 .564 75.155 o In hypothesis testing, if there are differences between groups, in this case we would think that they are due to the differences between smokers and non -smokers . o If there are differences within groups, we think of these differences as things we c an not exp lain (error). We don’t know why people in the same group have different scores; these are things we cannot account for in our study (e.g., individual differences, error). o Therefore, the ratio of between -groups variability and within -groups variability is basically the ratio between what we can explain and what we cannot explain , what we know versus what we don’t know , or what we can account for versus what we cannot account for .  1: this is the degrees of freedom (df) between groups. df between = number of groups – 1 = 2 groups (males and females) – 1 = 1  943 : this is df within groups. df within = (n1 – 1) + (n 2 – 1) = (number of smokers – 1) + (number of non - smokers – 1) = ( 138 – 1 ) + ( 807 – 1) = 943  The row “Sig.” shows you the p values . o See the p value explanations on page 6 of this file (under Question #2) . o Resilience:  p = . 006, which is smaller than .05. Therefore, the difference between smokers and non - smokers was statistically significant for this variable.  In other words, the mean resilience score for smokers was 3.27 , and the m ean resilience score for non -smokers was 3.47 . The mean difference between the two groups was 0. 2 (3.4 7 – 3.27 ), and having a p value smaller than .05 means that this 0.2-point difference i n resilience was statistically significant. To be more specific, non -smokers showed significantly higher resilience compared to smokers. o Depression :  p < .001, which is smaller than .05. Therefore, the difference between smokers and non - smokers was statistically significant for this variable.  In other words, the mean depression score for smokers was 11.12 , and the mean depression score for non -smokers was 7.58 . The mean difference between the two groups was 3.54 (11.12 – 7.5 8), and having a p value small er than .05 means that this 3.54 -point difference in depression was statistically significant. To be more specific, smokers scored significantly higher on the depression scale compared to non -smokers. PSYC 3304 & 3104 , Results & Discussion Sections Page 13 of 16 See the copyright statement in the course syllabus. How do I write up this finding for my hypothesis testing?  Restate the hypotheses.  Mention what tests were used ( i.e ., one -way analyses of variance [ANOVAs]).  Include the statistics. For each ANOVA, report M and SD for the groups, and F, df, and p for the gender differences.  Talk about if it’s statistically significant (based on the p value) and interpret the relationship.  Discuss whether the hypothesis was supported.  See the example below.  Here is an exam ple: Two one -way analyses of variance (ANOVAs) were conducted to test the last two hypotheses, which focused on differences between smokers and non -smokers . It was hypothesized that non -smokers would have higher resilience and lower depression scores compared to smokers. The last two hypotheses were supported by the data. R esults showed that non -smokers (M = 3.47, SD = 0.76 ) had significantly higher resilience compared to smokers (M = 3.27, SD = 0.85 ), F(1, 943) = 7.70, p = .006 . Furthermore, non -smokers (M = 7.58, SD = 8.17) scored significantly lower on the depression scales compared to smokers (M = 11.12, SD = 11.17) , F(1, 943) = 19.57, p < .001 . What if my p value is larger than .05?  As you can see on page 6 of the Findings PDF, all differences between smokers and non -smokers were statistically significant (i.e., all p values were ≤ .05) .  However, for the sake of practice and learning, see page 10 of this file regarding non -significant differences between 2 groups (under Question #11) . PSYC 3304 & 3104 , Results & Discussion Sections Page 14 of 16 See the copyright statement in the course syllabus. Results Pearson correlations were conducted to test the three hypotheses in this study . The researcher hypothesized that resilience would increase as self -esteem increases . However, this hypothesis was not supported as the results showed the opposite direction. There was a significant negative correlation between these two variables, r(943) = -.60, p < .001; resilience ( M = 3.44, SD = 0.78) decreased a s self -esteem ( M = 19.49, SD = 5.40) increased. The second hypothesis stated that older individuals would have higher self -esteem. Results supported this hypothesis in showing that self -esteem increased as age ( M = 34.06 , SD = 12.68 ) increased. Lastly, it was hypothesized that resilience would increase as age increases . This hypothesis was supported, as the results of showed that there was a significant positive correlation between age and resilience, r(925) = .15 , p < .001 . Older individuals showed highe r resilience compared to younger individuals. Example 1: self -esteem, resilience, and age You do not need to repeatedly report the means ( Ms) and standard deviations ( SD s) of the variables every time you mention them. In the above example, you can see that the Ms and SD s of resilience and self -esteem were reported when discussing the first hypothesis. I did not report them again, even when they were mentioned in the last two hypotheses. Similarly, the M and SD of age were reported only once in this Results section. PSYC 3304 & 3104 , Results & Discussion Sections Page 15 of 16 See the copyright statement in the course syllabus. Results The researcher hypothesized that resilience would increase as self -esteem increases . To test this hypothesis, Pearson correlation was conducted. Results showed that there was a significant negative correlation between resilience ( M = 3.44, SD = 0.78) and self -esteem ( M = 19.49, SD = 5.40), r(943) = -.60, p < .001. In other words, resilience decreased as self -esteem increased. Therefore, the first hypothesis was not supported. To examine gender differ ences in resilience and self -esteem , one -way analyses of variance (ANOVAs) were conducted to test the last two hypotheses. Four participants reported other for their gender. Because of the differences between the group sizes , these four participants were excluded from the hypotheses testing. It was hypothesized that females would have higher self -esteem compared to males. Results supported the second hypothesis in showing that female participants ( M = 19.83, SD = 5.50) had significantly higher self - esteem compared to the male participants ( M = 18.75, SD = 5.14), F(1, 939) = 8.19, p = .004. The third hypothesis states that males would have higher resilience compared to females. The findings also supported the last hypothesis as males ( M = 3.56, SD = 0.74) scored higher on the resilience scale compared to females ( M = 3.39, SD = 0.79), F(1, 939) = 9.56, p = .002. Example 2: self -esteem, resilience, and gender PSYC 3304 & 3104 , Results & Discussion Sections Page 16 of 16 See the copyright statement in the course syllabus. Results The researcher hypothesized that there would be a negative correlation between resilience and depression, which was supported by the data. Results of Pearson correlation showed that higher resilience was significantly associated with lower depression levels ( Mresilience = 3.44, SD resilien ce = 0.78 ; Mdepression = 8.10 , SD depression = 8.75), r(942) = -.50, p < .001 . Two one -way analyses of variance (ANOVAs) were conducted to test the last two hypotheses, which focused on differences between smokers and non -smokers. It was hypothesized that non -smokers would have higher resilience and lower depression scores compared t o smokers. The last two hypotheses were supported by the data. Results showed that non -smokers (M = 3.47, SD = 0.76) had significantly higher resilience compared to smokers (M = 3.27, SD = 0.85) , F(1, 943) = 7.70, p = .006. Furthermore, non -smokers (M = 7.58, SD = 8.17) scored significantly lower on the depression scales compared to smokers (M = 11.12, SD = 11.17) , F(1, 943) = 19.57, p < .001. Example 3: resilience, depression, and smoking status




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