Browsing by Author "McMullen, Matthew (Faculty Mentor)"

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Comparing Influenza vaccination rates before and after the H1N1 pandemic
(Montana State University Billings, 2020-04) Brandon, Connor; McMullen, Matthew (Faculty Mentor)
Immunizations are an important public health concern in order to help control the spread of diseases. Influenza is a particularly important seasonal vaccine, as it is updated every year and recommended that all people receive the vaccination. Unfortunately, not everyone receives the vaccine, which can make others more susceptible to contracting the disease andspreading it to others. Using data from the Center for Disease Control’s (CDC) National Immunization Survey (NIS), the number of child and teenage influenza vaccinations were compared before and after the H1N1 (Swine Flu) pandemic of 2009. It was hypothesized that the H1N1 outbreak would lead to an increased rate of vaccinations in both children and teenagers. The data was grouped by geographic region and socioeconomic status. The comparative results show that there was not an increased number of childhoodor teenage vaccinations relative to the total amount of influenza vaccinations that were administered, indicating that the H1N1 pandemic did not cause a greater number of influenza vaccinations in the following years.
An Exploration into Social Media Sentiment
(Montana State University Billings, 2020-04) Pratt, Ashley; McMullen, Matthew (Faculty Mentor)
Background:International and United States-specific media outlets cover the same news, but do not always utilize the same language. The COVID-19 Outbreak is an opportunity to analyze the sentiments being utilized to convey information to the masses. Exploring the words used and in what context can lead to more in-depth knowledge of what is being covered and how it is being explained by the media. Aim:The goal of this project is to analyze tweets from ten major news organizations, both local and abroad, by sentiment. News organizations will then be assessed for their portrayal of the pandemic in a positive or negative light, what sentiments they are using and the frequency, and what words are being commonly written together. This project will also be able to assess the discrepancies between US coverage and that of the world. Approach:Data will be processed through RStudio, utilizing sentiment data found in the NRC Emotion Lexicon and Bing Sentiments. The results will be correlated and graphed to show the variance between news coverage and language in the United States versus coverage during the same time abroad. Custom bigrams will also be created to explore more specific word connections, i.e., “COVID,” “corona,” “pandemic,” etc., nationally and internationally. Results: Tweets will be divided into data frames and then analyzed by word by both sentiment programs. Results for each news organization will be appropriately represented. Additionally, bigrams will be run on any words of significance. Results of the analyzed data and any statistical significance will be released. Conclusion: From the results, conclusions will be drawn regarding the sentiments nation and international news outlets utilize day-to-day.
Police Stops Analysis Within the Montana State 2009-2016
(Montana State University Billings, 2020-04) Mazel, Jeannine; McMullen, Matthew (Faculty Mentor)
The purpose of this analysis was to uncover multiple correlations in Montana police stops occurring between the years 2009-2016. Data on the Montana police stops was obtained from The Standford Policing Project. The program Rstudio was utilized in order to calculate and reveal information and correlations surrounding the following:1) Most common race/gender/age group to be stopped; 2) Most common reasons for police stops; 3) Correlations between police stops and racial features; and 4) Geographical information and correlations for police stops across Montana roads, particularly for negligent homicide and DUIs.
Relationship between Language Patterns and Antisocial Personality Disorder
(Montana State University Billings, 2021-04) Goettlich, Kiah; McMullen, Matthew (Faculty Mentor)
Background:Presently the diagnosis of personality disorders, particularly those that are concerned with manipulative traits such as those associated with Antisocial Personality Disorder, are extremely time consuming and variable from clinician to clinician. With the rise of social media platforms personal writings over time have become more widely available. If a new system of analysis were to be utilized as a way to help clinical assessments it could significantly reduce the time invested as well as the reliability of the data. Aim:This study was meant to investigate whether there are quantitative differences in language patterns of those identified with a personality disorder (through the use of the MCMI-III) and those without. Approach: Deidentified transcripts of the Adult Attachment Interview were formatted so that text-analysis could be run using the R-studio (Version 1.4.1103) software. The transcripts that were identified as persons with the disorder were randomly paired with those that were identified as not having the disorder. The content analysis included the complexity of the text through the use of each group's lexical diversity, lexical density, and word count. Sentiment analysis was run which assessed not only the number of positive words versus negative but also the most common words under each of those sentiments. Similarly, overarching themes can be seen when the most frequently used words of each condition are compared by themselves and then in pairs (using the bigrams data frame). Results:Based on previous research in this area, it was expected that those with the personality disorder would show themes that are more negative in nature (e.g., aggression, fear, etc.). When the sentiment analysis was run there were differences in common words based on sentiment. However, there were not significant differences when the analyses for the texts complexity were compared.
Trend of Internet Searches Related to "Coronavirus"
(Montana State University Billings, 2020-04) Hughes, Emily; McMullen, Matthew (Faculty Mentor)
Very evidently, the coronavirus has become a worldwide issue that has sparked panic across many nations. This project examines how Google searches related to coronavirus have spiked and fallen within the last few months since the beginning of the pandemic. Using RStudio -a coding platform -and the specific function “trendyy,” the trend of searches will be shown in graphs, as well as tables. The results show that the searches spike around January and February, and that places such as Italy showed a larger peak in searches for the disease compared to the United States.
Walking Does Not Significantly Improve Word Recall or State Anxiety in a Single Session: A Pilot Study
(Montana State University Billings, 2020-04) Brandon, Connor; McMullen, Matthew (Faculty Mentor)
Research suggests that exercise can improve memory ability (Labban & Etnier, 2011; Martins et al., 2013; Shih, 2017; Standage, 2010) and decrease anxiety (Blacklock et al., 2010; Knapens et al., 2009). The current study hypothesized that an exercise condition will recall more vocabulary words and have greater reductions in state anxiety compared to the sedentary control condition. Participants were randomly divided into either a sedentary control group or an exercise group. Both groups took the State-Trait Anxiety Inventory questionnaire (STAI-AD). Both groups were then given ten minutes to learn15 vocabulary words while either sitting in a chair or walking on a treadmill at 3mph, followed by a 20-minute consolidation period. Participants were asked to recall as many words as they could remember from their task and took the STAI-AD a second time. Paired t-tests were performed for analyzing the reduction in state anxiety and amount of words recalled in both conditions. The pilot results showed the exercise group (n=4) did not remember more vocabulary words compared to the control group (n=2; t = 0.4078, p-value = 0.749). The exercise group did show greater reductions in state anxiety compared to the control group (t = 1.1847, p-value = 0.4298). However, both analyses returned statistically insignificant results due to small sample sizes. Further data will be collected to obtain statistical significance and retest the hypothesis.