PLSC 201 Lecture Notes - Egypt Exploration Society, Tutankhamun

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holdengames19

26 May 2023

School

Winthrop University

Department

Political Science

Course

PLSC 201

Professor

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Document Summary

Introduction howard carter was a british archaeologist and egyptologist who is best known for discovering the tomb of tutankhamun, the boy pharaoh, in 1922. Carter"s discovery of the tomb is considered one of the greatest archaeological finds of the 20th century and his work has greatly contributed to our understanding of ancient egypt. Body 1 howard carter was born in london in 1874 and developed an interest in archaeology at a young age. He began his career as an artist and later worked for the egypt exploration fund, where he gained experience in excavating archaeological sites. In 1907, he was appointed as the chief inspector of. Antiquities for upper egypt, where he oversaw the excavation and preservation of many ancient. Body 2 carter"s most significant discovery occurred in 1922, when he discovered the tomb of. The tomb contained a wealth of treasures, including the famous golden mask of tutankhamun. The discovery sparked worldwide interest and made carter a household name.

Related Questions

1 Introduction

Your goal is to formulate a set of psycholinguistic hypotheses that interest you and use previously collected data to investigate these hypotheses. Your hypothesis should be plausible-ish, but feel free to be creative in what you investigate. Any statistical methods are ok to use in your analysis. It’s not required that you have statistically significant results, but it is a requirement to do the analyses properly.

GIVE R CODES TO RUN

What I expect: To be written up in the form of a lab report that discusses your findings.

It should include an introduction, hypotheses, methods (what data was used, where it came from, what statistical tests were chosen and why, etc), results, discussion (what do the results mean scientifically), and a conclusion.
The number of tests to use, doing at least two analyses (complete with checks for assumptions).
You should make sure to use tables and plots when appropriate. And test assumptions!
Remember to motivate your choices and adequately illustrate your findings using plots and graphs.

2 Working with the English Lexicon Project

The English Lexicon Project (ELP) is a corpus of English lexical items with behavioral data for said items. That is, it contains accuracy and reaction times in lexical decision experiments and naming latencies in picture naming experiments. In addition to these latencies, you are also provided with the standard deviations for each word for each experiment-type, as well as the number of observations for each item. Basically, they’ve already run the experiments for you, so you just need to analyze them.

The data frame for the ELP is massive. It has 12704 observations and 24 variables. But, it’s unlikely that you’ll be using that much data. Instead, the observations and variables you use will be determined by your experimental hypothesis and the particular analysis you select.

Reading in a .csv file is slightly different than a .txt file, since it’s separated by commas and not tabs. You can use the following (remember to change file.choose() if you are on a system that uses a different command to bring up the user interface):

data <- read.csv(file.choose(), header=T)

Type head(data) to ensure you correctly loaded the file. And save your work often!

2.1 Explanation of the columns in the ELP dataset

There are a lot of columns in the ELP dataset. Here is a brief description of each column, and full descriptions can be found in the information on the ELP website.

2.1.1 Independent variables

Column Explanation

Word The specific lexical item

Length Number of orthographic characters (letters) composing the word Freq_Hal Word frequency

Ortho_N Number of orthographic neighbours (e.g., ace = ate, act, ale, are, …) Phono_N Number of phonological neighbours (e.g., wings = wins, rings, …) Phono_N_H Number of phonological neighbours including homophones Freq_N Average frequency of an orthographic neighbourhood of a particular word

Freq_N_P Average frequency of the phonological neighbourhood of a particular word

BG_Sum Sum of the bigram count for a particular word

BG_Mean Average bigram count for a particular word

BG_Freq_By_Pos Sum of the bigram count (by position) for a particular word NPhon Number of phonemes in the standard pronunciation for a particular word2

NSyll Number of syllables in the standard pronunciation for a particular word

MorphSp Morphological (orthographic) composition of a particular word. See for a key to the symbols.

MorphPr Morphological (phonetic) composition of a particular word. See for a key to the symbols.

NMorph Number of morphemes

2.1.2 Dependent variables

Lexical decision task

Column Explanation

I_Mean_RT Mean reaction time for a particular word (in ms) across all participants

I_SD Standard deviation of mean reaction time for a particular word (in ms) across all participants

Obs Number of observations comprising reaction time data for a particular word

I_Mean_Accuracy Average accuracy for a particular word across all participants

Naming task

Column Explanation

I_NMG_Mean_RT Mean naming latency for a particular word (in ms) across all participants

I_NMG_SD Standard deviation of mean naming latency for a particular word (in ms) across all participants

I_NMG_Obs Number of observations comprising naming latency data for a particular word

I_NMG_Mean_Accuracy Average accuracy for a particular word across all participants

4 Hints

You will probably want to do some serious subsetting of the data. Isolate just the rows and columns you want. Decide if you want to bin your lexical variables, e.g., create nominal categories such as high vs low frequency words, or use continuous measures, e.g., correlate your dependent variable against your independent variables. In the LADEC data, there are also rows with missing data (what R calls NA). Since many statistical tests prefer to have groups of equal sizes, you might need to remove rows with missing data. Do this using the na.omit() function, but only do this after you have selected which rows you’ll be working with. For instance, if the data was stored in d, and you only wanted columns 1, 2, 3, 40, 41, and 42, you can do

d2 <- d[,c(1,2,3,40,41,42)]

d2 <- na.omit(d2)

to save just those columns to d2, and then strip out the rows with NA values. One way to get started is to pick a column from a dataset and think about whether there are groups you could subset out, based on data from other columns. Or, you can take that column and think about how it might correlate with another column in the dataset. These are only suggestions, though. There’s a lot of data here, and lots of things one could look at. You don’t need statistically significant results to write the paper, so you can be creative in what you test.

Feel free to search the primary literature for inspiration. If the methods others use are too tricky, do not let yourself get stuck on one paper. For any particular topic, there are almost always many papers out there once you discover the hidden keywords to use (they’re most often in the introduction or background, and in the discussion).

Pick a question and remember to follow the steps before proceeding with the actual statistical tests. Remember to report your statistics in the appropriate manner, including for your tests for assumptions and for the main test you’re running. Consult the Gries book for some basic forms you can borrow from if you need help with the style for reporting results.

Other help: For help with plotting, use Quick-R ( Or, if you’re more adventurous, you could try ggplot2 ( which makes prettier plots than the standard R plotting tools.

astray

Edward Witten - How Do Scientific Breakthroughs Happen? Closer To Truth

"Asymptotic freedom," the last big breakthrough, was discovered by Gross, Wilcheck and Pulitzer just before I started my graduate school. It took me a year to understand what was happening in the world of physics. The standard model involved new mathematics unfamiliar to both physicists and mathematicians at the time, and whose exploration proved to be very rich.

The standard model is based on a nonlinear version of Maxwell's equations, where two waves interact and scatter each other but cannot be treated classically the way light waves can be treated, so it has to be treated quantum mechanically. In 1976, right after I finished my graduate school, there was a breakthrough in the math of nonbillion gauge theory applied to these strong interactions.

In 1982 and 1983, John Schwartz, Michael Green and Lars Brink made enough progress in string theory that I started to take their work seriously, though I knew it was a long-term proposition. In 1984, a colleague who had just returned from Aspen mentioned the development that had been achieved, and at that instant, I knew I would be working on string theories because it was like magic.

The breakthrough of Green and Schwartz in August 1984 made the difficulties in elementary particle physics go away. It had a huge impact on me, and I was sure that my interest would never be the same again. By the mid-1980s, I had caught up to where others were. As a graduate student, I was obsessed with the fact that the equations of the Standard Model are challenging to solve because the quantum effects are significant.

In 1994, Cyborg and I collaborated on an interesting instance of what people in the physics and mathematics fields call "Cyborg written theory." The equations of the Standard Model are tough to solve because quantum effects are large, and it proved to be particularly captivating.

The story that I presented in a lecture at USC (University of Southern California) in 1995, where I sort of unified the string theories, really resulted from an attempt to disprove some of them. In 1995, I was successful in doing something similar for string theory. I tried to reduce the number of string theories by showing that some were inconsistent.

abbasalichak54

Prepare Notes

Outline your response to the prompt in the box below. Then, write a few questions to ask your peers to better understand their environmental outlooks.

I bring a variety of experiences, attitudes, and perspectives when considering environmental science issues. Firstly, my experiences living in both rural and urban environments have given me the opportunity to see firsthand the various environmental challenges faced by different communities. Additionally, I have been fortunate enough to have the opportunity to travel and see the impacts of environmental issues on a global scale. These experiences have fostered within me a deep appreciation and respect for the natural world and a desire to protect it.

In terms of attitudes, I am strongly motivated by a sense of social and environmental justice. I believe that it is our responsibility to be stewards of the planet and to ensure that future generations have the opportunity to enjoy a healthy and sustainable environment. I also believe that addressing environmental issues requires a holistic and intersectional approach, considering the impacts on both human and non-human communities.

As for perspectives, I approach environmental science issues with a scientific mindset, valuing evidence-based decision making and a willingness to consider multiple viewpoints. However, I also recognize the importance of incorporating indigenous knowledge and the perspectives of marginalized communities into the conversation.

Questions for Peers:

What experiences have you had that have shaped your environmental outlook?

What values do you prioritize when considering environmental science issues?

How do you approach environmental issues from a scientific perspective?

How do you incorporate the perspectives of marginalized communities into your understanding of environmental science issues?

Final Product: Socratic Seminar on Sustainability

Instructions: For this final product, you will engage in a college-style Socratic seminar on sustainability. You will submit your Preparation notes, and use those notes to support you as you actively listen and contribute to the discussion.

Preparation

Paste your final Preparation notes below.

Before the Discussion

As you get ready for the discussion, set at least two specific goals for your participation during the discussion. Use the Contributing to Evidence-based Discussions and Norms/Active Listening rubrics in the Platform to inform your goals.

Sample Goals:

I plan to share at least one piece of information from my Preparation notes during the discussion.

I plan to connect my ideas to something another person says during the discussion.

What is your first goal?:

What is your second goal?:

After the Discussion

To what extent did you accomplish your first goal?

To what extent did you accomplish your second goal?:

How did you do with CONTRIBUTING TO EVIDENCE-BASED DISCUSSIONS? Give yourself a score based on the Cognitive Skills Rubric and explain why you deserve this score:

How did you do with NORMS/ACTIVE LISTENING? Give yourself a score based on the Cognitive Skills Rubric and explain why you deserve this score:

What did you enjoy or find the most interesting in the discussion?:

Was there anything you wish the discussion talked about but didn’t?:

Any other notes or reflections on today’s discussion:

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PLSC 201 Lecture Notes - Egypt Exploration Society, Tutankhamun

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