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Using Multivariate Statistics

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

http://www.gruberpeplab.com/teaching/psych231_fall2013/documents/231_Pennebaker2007.pdf

Linguistic Inquiry and Word Count (LIWC2007)

The self-medication hypothesis of addictive disorders derives primarily from clinical observations of patients with substance use disorders. Individuals discover that the specific actions or effects of each class of drugs relieve or change a range of painful affect states. Self-medication factors occur in a context of self-regulation vulnerabilities--primarily difficulties in regulating affects, self-esteem, relationships, and self-care. Persons with substance use disorders suffer in the extreme with their feelings, either being overwhelmed with painful affects or seeming not to feel their emotions at all. Substances of abuse help such individuals to relieve painful affects or to experience or control emotions when they are absent or confusing. Diagnostic studies provide evidence that variously supports and fails to support a self-medication hypothesis of addictive disorders. The cause-consequence controversy involving psychopathology and substance use/abuse is reviewed and critiqued. In contrast, clinical observations and empirical studies that focus on painful affects and subjective states of distress more consistently suggest that such states of suffering are important psychological determinants in using, becoming dependent upon, and relapsing to addictive substances. Subjective states of distress and suffering involved in motives to self-medicate with substances of abuse are considered with respect to nicotine dependence and to schizophrenia and posttraumatic stress disorder comorbid with a substance use disorder.

The Self-medication Hypothesis of Substance Use Disorders: A Reconsideration and Recent Applications

/pdf/the-development-and-psychometric-properties-of-liwc2015-n5qek2rwxf.pdf

The Development and Psychometric Properties of LIWC2015

Background and AimsSmartphone addiction, its association with smartphone use, and its predictors have not yet been studied in a European sample. This study investigated indicators of smartphone use, smartphone addiction, and their associations with demographic and health behaviour-related variables in young people.MethodsA convenience sample of 1,519 students from 127 Swiss vocational school classes participated in a survey assessing demographic and health-related characteristics as well as indicators of smartphone use and addiction. Smartphone addiction was assessed using a short version of the Smartphone Addiction Scale for Adolescents (SAS-SV). Logistic regression analyses were conducted to investigate demographic and health-related predictors of smartphone addiction.ResultsSmartphone addiction occurred in 256 (16.9%) of the 1,519 students. Longer duration of smartphone use on a typical day, a shorter time period until first smartphone use in the morning, and reporting that social networking was the mo...

/pdf/smartphone-use-and-smartphone-addiction-among-young-people-2ksjv1vvl0.pdf

Smartphone use and smartphone addiction among young people in Switzerland

Zusammenfassung. Im Beitrag wird die deutsche Adaptation des Linguistic Inquiry and Word Count (LIWC) vorgestellt. Das LIWC ist ein computergestutztes Textanalyseprogramm mit integriertem Worterbuch, das von Pennebaker und Kollegen (2001) entwickelt wurde, um Essays aus Experimenten zum expressiven Schreiben zu untersuchen. Es werden zwei Studien zur Gute und Praktikabilitat des deutschen LIWC berichtet: (1) An einer Stichprobe zweisprachig vorliegender Texte (N = 122) wird die Aquivalenz der deutschen Ubersetzung mit dem Original uberpruft. (2) An einer Stichprobe von N = 104 E-Mails wird die Robustheit des Verfahrens gegenuber Schreibfehlern untersucht. Fur die meisten LIWC-Kategorien zeigt sich eine gute Aquivalenz. Des Weiteren erweist sich das LIWC als robust gegenuber Schreibfehlern. Diese Befunde sprechen fur die Brauchbarkeit des LIWC fur die Analyse deutscher Texte. Die beobachtete Robustheit ist von erheblicher praktischer Bedeutung fur die Analyse naturlicher geschriebener Sprache aus der compu...

Computergestützte quantitative textanalyse: Äquivalenz und robustheit der deutschen Version des Linguistic Inquiry and Word Count.

INTRODUCTION The objective of this study was to test the feasibility and acceptance of an intervention using text messaging (short message service [SMS]) for continuous individual support of smoking cessation in young adults. Additionally, the optimal feedback intensity was investigated, and short-term efficacy of the intervention was explored. METHODS In a cafeteria of the University of Greifswald, 575 visitors were screened for smoking status and usage of text messaging. From these, 194 persons who fulfilled the inclusion criteria of daily smoking and weekly usage of SMS were invited for participation in an SMS-based intervention. From these, 174 (90%) consented to participate. The participants were randomly allocated to one of three study groups: (a) control condition without intervention, (b) intervention with one weekly SMS feedback (1SMS), or (c) intervention with three weekly SMS feedbacks (3SMS). In study groups (b) and (c), individualized SMS feedbacks were sent to the participants weekly, based on data from the baseline assessment and a weekly SMS assessment of the stages of change according to the transtheoretical model. Program use and acceptance were compared between the two intervention groups differing in support intensity. An exploration of the short-term efficacy of the program was conducted by comparing the three study groups at the end of the 3-month intervention program on smoking variables. RESULTS The median number of replies to the weekly SMS assessments was 12.5 in the 1SMS group and 13.0 in the 3SMS group (not significant). The acceptance of the program did not differ between the intervention groups. At postassessment, no significant differences between the three study groups emerged on the examined smoking variables. DISCUSSION The high participation and retention rates suggest that SMS-based smoking cessation interventions are attractive for young adults. Support intensity did not affect the acceptance of the program. Longer follow-up periods and larger samples are required to obtain conclusive results about the efficacy of this intervention approach.

Continuous individual support of smoking cessation using text messaging: a pilot experimental study.

Background: Smoking prevalence remains high, particularly among adolescents and young adults with lower educational levels, posing a serious public health problem. There is limited evidence of effective smoking cessation interventions in this population. Objective: To test the efficacy of an individually tailored, fully automated text messaging (short message service, SMS)–based intervention for smoking cessation in young people. Methods: A 2-arm cluster randomized controlled trial, using school class as the randomization unit, was conducted to test the efficacy of the SMS text messaging intervention compared to an assessment-only control group. Students who smoked were proactively recruited via online screening in vocational school classes. Text messages, tailored to demographic and smoking-related variables, were sent to the participants of the intervention group at least 3 times per week over a period of 3 months. A follow-up assessment was performed 6 months after study inclusion. The primary outcome measure was 7-day smoking abstinence. Secondary outcomes were 4-week smoking abstinence, cigarette consumption, stage of change, and attempts to quit smoking. We used regression models controlling for baseline differences between the study groups to test the efficacy of the intervention. Both complete-case analyses (CCA) and intention-to-treat analyses (ITT) were performed. Subgroup analyses were conducted for occasional and daily smokers. Results: A total of 2638 students in 178 vocational school classes in Switzerland participated in the online screening. Overall, 1012 persons met the inclusion criteria for study participation, and 755 persons (74.6%) participated in the study (intervention: n=372; control: n=383). Of the 372 program participants, 9 (2.4%) unsubscribed from the program during the intervention period. Six-month follow-up data were obtained for 559 study participants (74.0%). The 7-day smoking abstinence rate at follow-up was 12.5% in the intervention group and 9.6% in the control group (ITT: P =.92). No differences between the study groups were observed in 4-week point prevalence abstinence rates. The decrease in the mean number of cigarettes smoked per day from baseline to follow-up was higher in the intervention group than in the control group (ITT: P =.002). No differences between the groups were observed in stage of change (ITT: P =.82) and quit attempts (ITT: P =.38). The subgroup analyses revealed lower cigarette consumption in both occasional and daily smokers in the intervention group compared to the control group. Occasional smokers in the intervention group made more attempts to quit smoking than occasional smokers in the control group. Conclusions: This study demonstrated the potential of an SMS text message–based intervention to reach a high proportion of young smokers with low education levels. The intervention did not have statistically significant short-term effects on smoking cessation; however, it resulted in statistically significant lower cigarette consumption. Additionally, it resulted in statistically significant more attempts to quit smoking in occasional smokers. Trial Registration: International Standard Randomized Controlled Trial Number (ISRCTN): 19739792; http://www.controlled-trials.com/ISRCTN19739792 (Archived by WebCite at http://webcitation.org/6IGETTHmr). [J Med Internet Res 2013;15(8):e171]

https://www.zora.uzh.ch/id/eprint/83903/1/SH_JMIR15_8_2013.pdf

Efficacy of a text message-based smoking cessation intervention for young people: a cluster randomized controlled trial.

Background: Modern communication technologies offer novel opportunities for the provision of stepped care for patients with mental disorders. This study investigated the effectivene

Severin Haug

Papers

Smartphone use and smartphone addiction among young people in Switzerland

Computergestützte quantitative textanalyse: Äquivalenz und robustheit der deutschen Version des Linguistic Inquiry and Word Count.

Continuous individual support of smoking cessation using text messaging: a pilot experimental study.

Efficacy of a text message-based smoking cessation intervention for young people: a cluster randomized controlled trial.

The exploration of the effectiveness of group therapy through an Internet chat as aftercare: a controlled naturalistic study.