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Statistics Presentation

Transcript: Niemz, M., Griffiths, M., & Banyard, P. (2005). Prevalence of pathological internet use among university students and correlations with self-esteem, the General Health Questionnaire (GHQ), and disinhibition. CyberPsychology & Behavior, 8(6), 562-570. Rosen, L.D., Whaling, K., Carrier, L.M., & Cheever, N.A. (2013). Is Facebook creating “iDisorders”? The link between clinical symptoms of psychiatric disorders and technology use, attitudes and anxiety. Computers in Human Behavior. Seidman, G. (2013). Self-presentation and belonging on Facebook: How personality influences social media use and motivations. Personality and Individual Differences, 54, 402-407. A hierarchical multiple regression was performed and adjusted for demographics Determined which variables provided significant prediction in a simultaneous regression More time online and more FB impression management related to more clinical symptoms of major depression More FB friends, fewer symptoms of dysthymia Predictors of mania: more FB general use, more FB impression management, more FB friends Predictors of narcissism & histrionic PD: more FB friends, more impression management, and more general use General use and impression management also predicted more signs of ASPD & compulsive disorders Predictors of paranoia & schizoid PD: more FB general use and fewer FB friends Predictors of mania: More general FB use, more FB impression management, and more FB friends Limitations Methods Hypotheses & Methods References 18.3% of sample were pathological internet users, 51% had limited symptoms, and 30.5% had no symptoms Pathological internet users had lower self-esteem and were more socially disinhibited more online friends, friendlier, more liberated and open, more likely to share secrets Pathological internet use caused problems in academic, social, and interpersonal areas of life Males had a higher average number of pathological symptoms (M=2.6) than females (M=1.5) ANOVA showed this was statistically significant, p<.01 Positive correlation between hours spent online and PI symptoms, p<.01 No significant relationship between PIU and GHQ, p=.118 Not evidence based Not a broad demographic studied mostly college/young adult age No longitudinal data Mostly self-report--people lie! What do we do with this information? 184 undergraduate students participated in an online survey for extra credit Big Five assessed with Snacier's (1994) version of Goldberg's Big 5 markers Belongingness four scales two assessing belongingness behaviors two assessing motivations Self-presentation six scales two assessing self-presentational behaviors one assessing attention-seeking motivation three assessing the extent to which FB was used to express actual, hidden, and ideal self-aspects Teens, young adults, & adults (N=1143) completed an anonymous online questionnaire assessing internet and technology related behaviors, as well as symptoms of psychopathology Clinical symptoms of psychological disorders measured with the MCMI-III: Million Multiaxial Clinical Inventory Questions specific to FB usage: frequency of reading postings, posting status updates, posting photos, commenting on posts or statuses, commenting on photos, "checking in", changing or updating profile, browsing profiles, browsing photos, “liking” things, adding or requesting new friends, FB chatting, joining or creating events, playing games, joining or creating groups also asked about # of friends and how many they’d actually met Purpose: Past research has shown mixed results with regard to psychopathology and social networking use Some studies have shown positive correlations with SNS frequency of use and MDD, others have shown negative correlations or no relationship Studied if FB use, technology attitudes, and anxieties were predictive of personality and/or mood disorders Hypotheses: Adults who use more technology & media, particularly social media, will show increased clinical symptoms of psychiatric disorders Adults who show more negative attitudes toward technology will show increased clinical symptoms of psychiatric disorders Is Facebook Creating "iDisorders"? The link between clinical symptoms of psychiatric disorders and technology use, attitudes, and anxiety Prevalence of Pathological Internet Use among University Students and Correlations with Self-esteem, the GHQ, and Disinhibition. G. Seidman Methods Stats and Results Purpose: Internet Addiction Disorder (Pathological internet use) is a growing problem. Past studies have shown that PI users are more likely to be male, college-age, have lower self-esteem, and be less inhibited than the general population. Symptoms of IAD: withdrawal symptoms such as anxiety and depression when not online, tolerance (e.g. spending longer amounts of time in chat rooms), mood-altering, and preoccupation with one's online activities This study aimed to replicate past findings and see if they were applicable to the British college population. L.D. Rosen, K. Whaling, S. Rab,

Statistics Presentation

Transcript: -3.09 -3.09 -2.09 -4.09 -6.91 5.91 -0.09 -6.09 -0.09 -5.09 1.91 -3.09 5.91 4.91 -0.09 1.91 -1.09 -3.09 0.91 -1.09 0.91 1.91 0.91 Name x-x̅ 2 1 1 0 0 0 1 1 1 3 0 1 2 0 1 1 2 2 1 1 3 2 0 Frequency 6 10 5 2 Low (Minimum) = 0 Quartile 1 = 0 Quartile 2 (Median) = 1 Quartile 3 = 2 High (Maximum) = 3 x Cap'n'Crunch Cocoa Trix Apple Jack Corn Chex Corn Flake Nut & Honey Smacks MultiGrain Cracklin' Grape-Nuts Honey N Nutri-Grain Product 19 Total Raisin Wheat Oatmeal Life Maypo Quaker Muesli Cheerios Special K 64.49 49.04 22.72 24.01 14.72 42.14 0.8 47.93 -0.1 -5.75 26.99 24.32 12.59 15.37 0.89 21.26 9.67 -18.94 14.68 -12.13 -4.43 23.17 12.58 Section 6: Create a Scatter Plot Draw an Appropriate Graph for the Categorical Data 5 Number Summary 12 12 13 11 22 21 15 9 15 10 17 12 21 20 15 17 14 12 16 14 16 17 16 Math 13: Statistics Group Project Our categorical data was focused on the manufacturers for 23 different brands of cereal. Looking at the table, there are 6 different manufacturers for the cereals in the sample. Kelloggs produces the most amount of cereal, being the manufacturer responsible for 34.78%. The next manufacturer is General and is responsible for 30.43%. Ralston and Quaker Oats are equal in production, each being responsible for 13.04%. Finally, the manufacturers Post and America are the least prominent, each only being responsible for 4.35%. Section 7: Determine Correlation Coefficient -20.87 -15.87 -10.87 -5.87 2.13 7.13 -8.87 -7.87 1.13 1.13 14.13 -7.87 2.13 3.13 -9.87 11.13 -8.87 6.13 16.13 11.13 -4.87 12.13 14.13 x 0 1 2 3 Mean= 26/23 = 1.13 Mode = 1 Cap'n'Crunch Cocoa Trix Apple Jack Corn Chex Corn Flake Nut & Honey Smacks MultiGrain Cracklin' Grape-Nuts Honey N Nutri-Grain Product 19 Total Raisin Wheat Oatmeal Life Maypo Quaker Muesli Cheerios Special K 9.55 9.55 4.37 16.73 47.75 34.93 0.01 37.09 0.01 25.91 3.65 9.55 34.93 24.11 0.01 3.65 1.19 9.55 0.83 1.19 0.83 3.65 0.83 Σ(x-x̅)²=18.68 In regards to numerical data, we chose to use the amount of fat in grams in 23 different cereal brands. The mean amount of fat is 1.13 grams and the mode is 1, meaning most of the 23 brands have 1 gram of fat. In the 23 brands of cereal, the lowest amount of fat is 0 grams while the highest is 3 grams. When arranged in order from least to greatest, the lower quartile is 0, the middle quartile or median is 1, and the upper quartile is 2. It also has a standard deviation of 0.92. Fat (g) 2 1 1 0 0 0 1 1 1 3 0 1 2 0 1 1 2 2 1 1 3 2 0 (x-x̅)(y-ȳ) Thank You!! Section 5: Description of Findings Section 3: Mean, Median, 5 Number Summary Group 10: Keycee Alvarez, Julian Bayani, Liming Liang Section 4: Calculating Standard Deviation 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 435.56 251.86 118.16 34.56 4.54 50.84 78.68 61.94 1.28 1.28 199.66 61.94 4.54 9.8 97.42 123.88 78.68 37.58 260.18 123.88 23.72 147.14 199.66 (Frequency)(x) 0 10 10 6 18 23 28 33 41 46 30 31 40 40 53 31 41 42 29 50 30 45 55 50 34 51 53 Manufacturer Quaker Oats General General Kelloggs Ralston Kelloggs Kelloggs Kelloggs General Kelloggs Post General Kelloggs Kelloggs General Ralston General Quaker Oats America Quaker Oats Ralston General Kelloggs Name Cap'n'Crunch Cocoa Trix Apple Jack Corn Chex Corn Flake Nut & Honey Smacks MultiGrain Cracklin' Grape-Nuts Honey N Nutri-Grain Product 19 Total Raisin Wheat Oatmeal Life Maypo Quaker Muesli Cheerios Special K Assigned Data Section 1: Choose one set of Numerical and Categorical Data Categorical Data Numerical Data CRRating(y) x-x̅ 0.76 0.02 0.02 1.28 1.28 1.28 0.02 0.02 0.02 3.5 1.28 0.02 0.76 1.28 0.02 0.02 0.76 0.76 0.02 0.02 3.5 0.76 1.28 Numerical: Fat (g) || Categorical: Manufacturer of Cereal 0.87 -0.13 -0.13 -1.13 -1.13 -1.13 -0.13 -0.13 -0.13 1.87 -1.13 -0.13 0.87 -1.13 -0.13 -0.13 0.87 0.87 -0.13 -0.13 1.87 0.87 -1.13 One of the data sets assigned to us was carbs. Among the 23 brands of cereal, the mean is 15.09. Additionally, the standard deviation for this data set is 3.57. The second data set assigned to us was the Consumer Report Rating, or the CRRating. The mean rating is 38.87 and the standard deviation for this data set is 10.46. Assigned Data: Carbs and CRRating Section 2: Choose the Type of Graph for Each Data Set Name (x-x̅)² Carbs(x) (x-x̅)² (y-ȳ)² y-ȳ

Statistics Presentation

Transcript: Both of these variables have influence on the topic yet one is the one being manipulated and the other is one that could make the study's findings wrong. Although both of these are studies they are in their own way different. A confounding variable is one that influences the dependent or outcome variable but cannot be separated from the independent variable. Experimental Study An independent variable is exactly what it sounds like. It is a variable that stands alone and isn't changed by the other variables you are trying to measure. For example, someone's age might be an independent variable. Other factors (such as what they eat, how much they go to school, how much television they watch) aren't going to change a person's age. In fact, when you are looking for some kind of relationship between variables you are trying to see if the independent variable causes some kind of change in the other variables, or dependent variables. A confounding variable, also known as a third variable or a mediator variable, can adversely affect the relation between the independent variable and dependent variable. This may cause the researcher to analyze the results incorrectly. The results may show a false correlation between the dependent and independent variables, leading to an incorrect rejection of the null hypothesis. References: Statistics Book Thanks for listening to our presentation! 1.5 as an observational study uses what you see, an experimental you try something new to view the changes between variables Imagine that you were interested in whether karaoke singers who are more animated would receive more applause from the audience. You could visit a restaurant that has karaoke and observe karaoke singers during one evening. You could rate each karaoke singer with respect to how animated the singer was. Moreover, you could rate the degree of applause that each singer received. You could then determine whether there was any correlation between how animated the singers were and the applause that they received from the audience. Independent Variable: Observational Study Group Members: KayCee Michael Abbott Michael Howard JoAnna Brennen Observational and Experimental Studies LETS GO! In an observational study, the researcher merely observes what is happening or what has happened in the past and tries to draw conclusions based on these observations Unfortunately, when the researchers do a crosscheck with their peers, the results are ripped apart, because their peers live just as long - maybe there is another factor, not measured, that influences both drinking and living age? The weakness in the experimental design was that they failed to take into account the confounding variables, and did not try to eliminate or control any other factors. In an experimental study, the researcher manipulates one of the variables and tries to determine how the manipulation influences other variables A group of students is interested in knowing if the number of times they can sink a basketball is related to the color of the basketball. The students shoot a series of baskets and record their success using a regulation colored basketball. They then switch to a blue colored basketball and shoot the same series of baskets. For example, a research group might design a study to determine if heavy drinkers die at a younger age.They proceed to design a study, and set about gathering data. Their results, and a battery of statistical tests, indeed show that people who drink excessively are likely to die younger. In this presentation you will learn about two kinds of statistical studies and two kinds of variable Confounding Variable The independent variable in an experimental study is the one being manipulated by the researcher. The independent variable is also called the explanatory variable. The resulting variable is called the dependent variable or the outcome variable.

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