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seen from Poland
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seen from China

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Speaking of Polybius, how about 🎶+Less Than (Nine Inch Nails)?
He stops a moment, staring at the video, completely transfixed. His fingers twitch around a nonexistent controller.
“.... I’ve seen that game before,” he says, absentmindedly. “Not--actually, no. Mine was just green--the machine couldn’t process other colors. I would have beaten my high score if Chad didn’t---”
He blinks out of his trance and shuts the video off. He’s suddenly agitated. “What were you trying to do with that? Were you trying to blind me?”
Investigating the Relationship Between Smoking Frequency and Nicotine Dependence, and the Role of Socioeconomic Status
Blog Entry: Investigating the Relationship Between Smoking Frequency and Nicotine Dependence, and the Role of Socioeconomic Status
Dataset Selection
After reviewing the available codebooks, I selected the NESARC dataset (National Epidemiologic Survey on Alcohol and Related Conditions). It is an extensive and nationally representative dataset that provides in-depth information on substance use, addiction behaviors, and a broad range of demographic and socioeconomic variables. It is ideally suited for studying smoking behaviors and their psychosocial correlates.
Primary Topic and Research Question
Primary Topic: The core of my investigation is to explore how regular smoking behavior is associated with nicotine dependence.
Research Question: Is there an association between smoking frequency/intensity and nicotine dependence among adults in the U.S., and does socioeconomic status influence this relationship?
Personal Codebook Certainly! Here is Step 3: Personal Codebook in plain English text (without the table) that you can copy and paste:
Personal Codebook
S3AQ3B1: Number of days smoked in the past 30 days – represents smoking frequency.
S3AQ3C1: Average number of cigarettes smoked per day – represents smoking intensity.
S3BQ1A5: Age when regular smoking began – captures when smoking behavior was established.
S3AQ8A: Difficulty quitting smoking – indicates behavioral signs of nicotine dependence.
TAB12MDX: Nicotine dependence diagnosis (DSM-IV criteria) – clinical measure of addiction.
EDUCA: Education level – socioeconomic status indicator.
INCOME: Income level – socioeconomic status indicator.
EMPSTATY: Employment status – socioeconomic status indicator.
Secondary Topic
In addition to the direct relationship between smoking behavior and dependence, I am also interested in how socioeconomic status (SES) may impact this relationship. Prior research has shown that factors such as education, income, and employment status can significantly influence health behaviors, including addiction risk.
Literature Review
I conducted a literature search using Google Scholar with key phrases like:
“nicotine dependence and smoking behavior”
“socioeconomic status and addiction”
Key References:
Lopez-Quintero, C., Pérez de los Cobos, J., Hasin, D. S., Okuda, M., Wang, S., Grant, B. F., & Blanco, C. (2011). Probability and predictors of transition from first use to dependence on nicotine, alcohol, cannabis, and cocaine: Results of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Drug and Alcohol Dependence, 115(1–2), 120–130.
Summary: This study utilized NESARC data to demonstrate that individuals who smoke more frequently or start at an earlier age are significantly more likely to become dependent. The risk increases with the intensity and regularity of use.
Hiscock, R., Bauld, L., Amos, A., Fidler, J. A., & Munafò, M. (2012). Socioeconomic status and smoking: A review. Annals of the New York Academy of Sciences, 1248(1), 107–123.
Summary: This review emphasizes that lower socioeconomic groups are more likely to smoke heavily and experience greater difficulty quitting. It suggests that SES is a strong moderator in the addiction pathway.
Terms and Variables Clarified in Literature:
Nicotine Dependence: Often measured by the presence of withdrawal symptoms, craving, and inability to quit despite attempts.
Smoking Frequency/Intensity: Typically refers to the number of days smoked and cigarettes per day.
Socioeconomic Status (SES): Measured using indicators like education, income, and employment. Lower SES is linked to higher health risks and substance use.
Hypothesis Development
Hypothesis: Greater smoking frequency and intensity are associated with higher likelihood of nicotine dependence. Additionally, individuals with lower levels of education, income, and employment are more likely to develop dependence even with similar smoking levels, indicating that socioeconomic status moderates the smoking-dependence relationship.
Summary of Association to Be Studied
This study will focus on the association between smoking behavior (frequency, intensity, and age of onset) and nicotine dependence, as diagnosed by DSM-IV criteria. It will further explore how socioeconomic status variables (education, income, employment) influence this relationship—whether by increasing vulnerability to addiction or by limiting access to cessation resources.
Conclusion
This investigation is grounded in a rich dataset (NESARC), backed by established literature, and guided by a clear hypothesis. It aims to contribute to understanding how behavioral and structural factors combine to affect addiction risk, particularly in vulnerable populations.
Exploring the Relationship Between Regular Smoking and Nicotine Dependence, and the Role of Socioeconomic Status
Blog Entry: Exploring the Relationship Between Regular Smoking and Nicotine Dependence, and the Role of Socioeconomic Status
Dataset Selection
I selected the NESARC dataset (National Epidemiologic Survey on Alcohol and Related Conditions) due to its comprehensive data on mental health, addiction behaviors, and socioeconomic factors.
Primary Topic
The focus of my exploration is the relationship between regular smoking levels and nicotine dependence. I've noticed that some smokers develop addiction early on, while others seem to resist it for years. This pattern inspired my research question.
Personal Codebook – Selected Variables:
S3AQ3B1: Number of days smoked in the past 30 days.
S3AQ3C1: Average number of cigarettes per day.
S3BQ1A5: Age when regular smoking began.
S3AQ8A: Difficulty quitting smoking.
TAB12MDX: Nicotine dependence diagnosis (DSM-IV).
Secondary Topic: Socioeconomic Influence
I decided to include a secondary focus: how socioeconomic status affects nicotine dependence. Prior studies suggest that individuals with lower income or education levels are more prone to smoking and addiction.
Selected Socioeconomic Variables:
EDUCA: Education level.
INCOME: Income level.
EMPSTATY: Employment status.
Literature Review
I searched Google Scholar using terms like “nicotine dependence and smoking frequency” and “socioeconomic status and smoking behavior”.
Relevant studies include:
Lopez-Quintero et al. (2011) found that more frequent cigarette use increases the likelihood of dependence.
Hiscock et al. (2012) showed that people from lower SES groups tend to smoke more and are more likely to be dependent.
Research Hypothesis
Based on this review, my hypothesis is: The more regularly an individual smokes, the higher the likelihood of nicotine dependence, and this relationship may be influenced by socioeconomic factors such as education and income.

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The update is available for Windows and macOS In 2020, scientists decided to simply rework the alphanumeric characters they used to represent genes, rather than try to figure out the Excel function that interpreted their data as dates and automatically reformatted it. Recently, a member of the Excel team announced that the company is releasing an update for Windows and macOS. Excel’s automatic conversions are designed to make entering certain types of frequently entered data, such as numbers and dates, easier and faster. But for scientists, this feature can corrupt input data, as a 2016 study found. Microsoft Fixes Excel Function That […]
The update is available for Windows and macOS In 2020, scientists decided to simply rework the alphanumeric characters they used to represent genes, rather than try to figure out the Excel function that interpreted their data as dates and automatically reformatted it. Recently, a member of the Excel team announced that the company is releasing an update for Windows and macOS. Excel's automatic conversions are designed to make entering certain types of frequently entered data, such as numbers and dates, easier and faster. But for scientists, this feature can corrupt input data, as a 2016 study found. [caption id="attachment_72975" align="aligncenter" width="780"] Microsoft Fixes Excel Function[/caption] Microsoft Fixes Excel Function That Was Breaking Scientific Data Microsoft detailed the update on its blog, adding an option in Settings to “Convert continuous letters and numbers to a date.” The update builds on the automatic data conversion settings the company added last year, which included Excel's ability to let you load a file without automatic conversion so you can be sure the feature won't mess anything up. Microsoft's blog has caveats - for example, Excel avoids conversion by storing data as text, which means the data may not work for further calculations. There is also a known issue where conversions cannot be disabled when running macros.