Provide a brief introduction to your research question. Summarize the empirical articles you reviewed. My research question is: Does the birthplace of an individual have an effect on the anxiety levels towards students? Does gender play a role in this and change it? To further my research I looked at two empirical articles, to get a better understanding and grasp the knowledge. Different areas in the world have different teaching, standards, and importance on education. Places that hold huge significance on doing well in school tend to stress the students to perform well. Birthplace isn’t commonly talked about as a factor for stress, but your environment is very influential on it. Gender also places a huge role, in some places women aren’t aloud education let alone rights, so once those women finally get the chance they might feel a sense of stress to perform well a succeed. Furthermore males may feel significant pressure from their families to do well, so they can provide for others. Many factors lead to stress, with this study will show outcomes on how birthplace and gender have an overall effect on anxiety, and if there is a siginficant difference. ## Literature Review
Summarize the empirical article you selected. Discuss the key findings. Oh et al (2021) research showed studies around the world have found that urbanicity is associated with greater risk for types of psychiatric disorders, but the association is less evident in the United States. They analyzed data collected back from 2019 from the RND American Life Panel, which represented the general population of the United States. Oh et al used multivariable logistic regression they were able to examine association between environment of birthplace and psychiatric disorders, adjusting for the socio demographic characteristics. The research found that individuals who were born in large urban areas were associated with larger odds of having a psychiatric disorder when compared to an individual born in a rural area. When looking at specific disorders they found that being born in large urban areas was only significantly associated with greater odds of PTSD and anxiety disorder. While being born in a smaller urban area was marginally associated with anxiety disorders.
Summarize the empirical article you selected. Discuss the key findings. Hammoudeth et al (2025) conducted studies on women’s anxieties relating to place of birth. Interviews were done with 27 participants residing in Kufr. They were recruited through snowballing. Their findings were having strong anxieties about giving birth in Jerusalem’s borders. Cases of psychological distress were found relating to fears of ensuing birth at checkpoint and perceived link between political violence and negative health outcomes.
State your directional hypothesis. Specify the expected relationship between your variables. This section should be clear and concise. You need one hypothesis for IV1, one hypothesis for IV2, and one hypothesis for a predicted interaction. If you are running a logistic regression, you do not need a hypothesis for an interaction. It is hypothesized that depending on where the individual’s birthplace resided it can be associated with increasing labels of anxiety within students and this relationship will be more abundant in females than so for males.
Describe the sample used in your study. Include details about the population, sample size, and any relevant demographic information. The data set consists of around 7,000 students from the ages around 18-20 years old, recruiting people from different countries from around the world. The sample is approximately 94% male and 6% female.
List your independent and dependent variables. Explain how each variable was operationalized, including the range for continuous variables and levels for categorical variables. Independent Variable: Where the birthplace of an individual resided operationalized as the country of a person was born in. Expected range of 13.5 k individuals. Dependent Variable: Anxiety levels, shown as Generalized Anxiety Disorder 6-item (GAD6). Expected range: 0-3, with the higher numbers showcasing higher levels of anxiety.
Present the descriptive statistics for your variables. Include appropriate measures of central tendency (mean, median), variability (standard deviation, range), and frequency distributions where applicable. Use R code chunks to generate and display your results.
## vars n mean sd median trimmed mad min max range skew
## Gender* 1 13464 1.95 0.23 2 2.00 0 1 3 2 -3.23
## Birthplace* 2 13464 80.29 42.56 93 83.67 43 1 126 125 -0.43
## kurtosis se
## Gender* 13.21 0.00
## Birthplace* -1.47 0.37
Perform your chosen analysis. Make sure your output shows.
anxiety$Gender <- as.factor(anxiety$Gender)
model <- glm(Gender ~ Birthplace, data = anxiety, family = "binomial")
summary(model)##
## Call:
## glm(formula = Gender ~ Birthplace, family = "binomial", data = anxiety)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.757e+01 2.797e+03 0.006 0.995
## BirthplaceAlbania 3.379e-06 3.128e+03 0.000 1.000
## BirthplaceAlgeria 3.378e-06 3.426e+03 0.000 1.000
## BirthplaceArgentina -1.451e+01 2.797e+03 -0.005 0.996
## BirthplaceAustralia -1.447e+01 2.797e+03 -0.005 0.996
## BirthplaceAustria -1.267e+01 2.797e+03 -0.005 0.996
## BirthplaceAzerbaijan 3.380e-06 3.956e+03 0.000 1.000
## BirthplaceBahrain 3.380e-06 3.956e+03 0.000 1.000
## BirthplaceBangladesh 3.380e-06 3.064e+03 0.000 1.000
## BirthplaceBelarus 3.380e-06 3.230e+03 0.000 1.000
## BirthplaceBelgium -1.310e+01 2.797e+03 -0.005 0.996
## BirthplaceBelize 3.378e-06 4.845e+03 0.000 1.000
## BirthplaceBolivia 3.380e-06 3.956e+03 0.000 1.000
## BirthplaceBosnia and Herzegovina 3.379e-06 2.922e+03 0.000 1.000
## BirthplaceBrazil -1.462e+01 2.797e+03 -0.005 0.996
## BirthplaceBrunei 3.380e-06 4.845e+03 0.000 1.000
## BirthplaceBulgaria -1.454e+01 2.797e+03 -0.005 0.996
## BirthplaceCanada -1.494e+01 2.797e+03 -0.005 0.996
## BirthplaceChile -1.404e+01 2.797e+03 -0.005 0.996
## BirthplaceChina -1.588e+01 2.797e+03 -0.006 0.995
## BirthplaceColombia -1.416e+01 2.797e+03 -0.005 0.996
## BirthplaceCosta Rica 3.380e-06 3.064e+03 0.000 1.000
## BirthplaceCroatia -1.543e+01 2.797e+03 -0.006 0.996
## BirthplaceCuba -1.687e+01 2.797e+03 -0.006 0.995
## BirthplaceCura\xe7ao 3.379e-06 4.845e+03 0.000 1.000
## BirthplaceCyprus 3.380e-06 3.310e+03 0.000 1.000
## BirthplaceCzech Republic 3.379e-06 2.840e+03 0.000 1.000
## BirthplaceDenmark -1.372e+01 2.797e+03 -0.005 0.996
## BirthplaceDominican Republic -1.526e+01 2.797e+03 -0.005 0.996
## BirthplaceEcuador 3.379e-06 3.310e+03 0.000 1.000
## BirthplaceEgypt 3.380e-06 3.041e+03 0.000 1.000
## BirthplaceEl Salvador 3.379e-06 3.426e+03 0.000 1.000
## BirthplaceEstonia -1.457e+01 2.797e+03 -0.005 0.996
## BirthplaceEthiopia 3.379e-06 4.845e+03 0.000 1.000
## BirthplaceFaroe Islands -1.618e+01 2.797e+03 -0.006 0.995
## BirthplaceFiji 3.378e-06 3.956e+03 0.000 1.000
## BirthplaceFinland -1.475e+01 2.797e+03 -0.005 0.996
## BirthplaceFrance -1.397e+01 2.797e+03 -0.005 0.996
## BirthplaceGeorgia 3.379e-06 3.611e+03 0.000 1.000
## BirthplaceGermany -1.427e+01 2.797e+03 -0.005 0.996
## BirthplaceGibraltar 3.380e-06 4.845e+03 0.000 1.000
## BirthplaceGreece -1.455e+01 2.797e+03 -0.005 0.996
## BirthplaceGreenland 3.379e-06 3.956e+03 0.000 1.000
## BirthplaceGuatemala 3.379e-06 3.426e+03 0.000 1.000
## BirthplaceHonduras -1.687e+01 2.797e+03 -0.006 0.995
## BirthplaceHong Kong -1.555e+01 2.797e+03 -0.006 0.996
## BirthplaceHungary -1.341e+01 2.797e+03 -0.005 0.996
## BirthplaceIceland -1.473e+01 2.797e+03 -0.005 0.996
## BirthplaceIndia -1.367e+01 2.797e+03 -0.005 0.996
## BirthplaceIndonesia 3.379e-06 3.611e+03 0.000 1.000
## BirthplaceIran 3.380e-06 3.064e+03 0.000 1.000
## BirthplaceIraq 3.381e-06 3.426e+03 0.000 1.000
## BirthplaceIreland -1.349e+01 2.797e+03 -0.005 0.996
## BirthplaceIsrael 3.379e-06 2.912e+03 0.000 1.000
## BirthplaceItaly -1.347e+01 2.797e+03 -0.005 0.996
## BirthplaceIvory Coast 3.380e-06 3.956e+03 0.000 1.000
## BirthplaceJamaica 3.380e-06 3.611e+03 0.000 1.000
## BirthplaceJapan 3.380e-06 2.903e+03 0.000 1.000
## BirthplaceJordan 3.380e-06 3.230e+03 0.000 1.000
## BirthplaceKazakhstan -1.636e+01 2.797e+03 -0.006 0.995
## BirthplaceKenya 3.380e-06 3.611e+03 0.000 1.000
## BirthplaceKuwait -1.577e+01 2.797e+03 -0.006 0.996
## BirthplaceKyrgyzstan 3.381e-06 4.845e+03 0.000 1.000
## BirthplaceLatvia -1.407e+01 2.797e+03 -0.005 0.996
## BirthplaceLebanon 3.380e-06 3.230e+03 0.000 1.000
## BirthplaceLibya 3.380e-06 3.956e+03 0.000 1.000
## BirthplaceLiechtenstein 3.380e-06 4.845e+03 0.000 1.000
## BirthplaceLithuania -1.541e+01 2.797e+03 -0.006 0.996
## BirthplaceLuxembourg 3.380e-06 3.005e+03 0.000 1.000
## BirthplaceMacau -3.513e+01 4.845e+03 -0.007 0.994
## BirthplaceMacedonia 3.380e-06 3.128e+03 0.000 1.000
## BirthplaceMalaysia -1.476e+01 2.797e+03 -0.005 0.996
## BirthplaceMaldives 3.380e-06 4.845e+03 0.000 1.000
## BirthplaceMalta 3.379e-06 3.426e+03 0.000 1.000
## BirthplaceMauritius 3.379e-06 3.230e+03 0.000 1.000
## BirthplaceMexico -1.450e+01 2.797e+03 -0.005 0.996
## BirthplaceMoldova 3.378e-06 3.956e+03 0.000 1.000
## BirthplaceMongolia 3.380e-06 3.956e+03 0.000 1.000
## BirthplaceMontenegro 3.379e-06 3.426e+03 0.000 1.000
## BirthplaceMorocco 3.379e-06 3.005e+03 0.000 1.000
## BirthplaceNamibia 3.379e-06 3.956e+03 0.000 1.000
## BirthplaceNepal 3.379e-06 3.611e+03 0.000 1.000
## BirthplaceNetherlands -1.433e+01 2.797e+03 -0.005 0.996
## BirthplaceNew Zealand -1.498e+01 2.797e+03 -0.005 0.996
## BirthplaceNicaragua 3.379e-06 3.956e+03 0.000 1.000
## BirthplaceNigeria 3.379e-06 3.611e+03 0.000 1.000
## BirthplaceNorway -1.459e+01 2.797e+03 -0.005 0.996
## BirthplacePakistan -1.500e+01 2.797e+03 -0.005 0.996
## BirthplacePanama 3.379e-06 3.172e+03 0.000 1.000
## BirthplacePeru -1.553e+01 2.797e+03 -0.006 0.996
## BirthplacePhilippines -1.601e+01 2.797e+03 -0.006 0.995
## BirthplacePoland -1.488e+01 2.797e+03 -0.005 0.996
## BirthplacePortugal -1.443e+01 2.797e+03 -0.005 0.996
## BirthplacePuerto Rico 3.379e-06 2.978e+03 0.000 1.000
## BirthplaceRepublic of Kosovo 3.380e-06 3.426e+03 0.000 1.000
## BirthplaceRomania -1.471e+01 2.797e+03 -0.005 0.996
## BirthplaceRussia -1.499e+01 2.797e+03 -0.005 0.996
## BirthplaceSaudi Arabia 3.380e-06 3.005e+03 0.000 1.000
## BirthplaceSerbia 3.380e-06 2.842e+03 0.000 1.000
## BirthplaceSingapore -1.596e+01 2.797e+03 -0.006 0.995
## BirthplaceSlovakia -1.520e+01 2.797e+03 -0.005 0.996
## BirthplaceSlovenia -1.528e+01 2.797e+03 -0.005 0.996
## BirthplaceSomalia 3.379e-06 3.611e+03 0.000 1.000
## BirthplaceSouth Africa -1.393e+01 2.797e+03 -0.005 0.996
## BirthplaceSouth Korea -1.526e+01 2.797e+03 -0.005 0.996
## BirthplaceSpain -1.453e+01 2.797e+03 -0.005 0.996
## BirthplaceSri Lanka 3.379e-06 3.426e+03 0.000 1.000
## BirthplaceSt Vincent 3.380e-06 4.845e+03 0.000 1.000
## BirthplaceSweden -1.419e+01 2.797e+03 -0.005 0.996
## BirthplaceSwitzerland -1.493e+01 2.797e+03 -0.005 0.996
## BirthplaceSyria 3.380e-06 3.426e+03 0.000 1.000
## BirthplaceThailand -1.596e+01 2.797e+03 -0.006 0.995
## BirthplaceTrinidad & Tobago 3.379e-06 3.128e+03 0.000 1.000
## BirthplaceTunisia 3.380e-06 3.310e+03 0.000 1.000
## BirthplaceTurkey -1.437e+01 2.797e+03 -0.005 0.996
## BirthplaceTurkmenistan 3.380e-06 4.845e+03 0.000 1.000
## BirthplaceUK -1.477e+01 2.797e+03 -0.005 0.996
## BirthplaceUkraine -1.448e+01 2.797e+03 -0.005 0.996
## BirthplaceUnited Arab Emirates 3.380e-06 3.093e+03 0.000 1.000
## BirthplaceUnknown -1.422e+01 2.797e+03 -0.005 0.996
## BirthplaceUruguay -1.562e+01 2.797e+03 -0.006 0.996
## BirthplaceUSA -1.492e+01 2.797e+03 -0.005 0.996
## BirthplaceUzbekistan 3.379e-06 3.956e+03 0.000 1.000
## BirthplaceVenezuela 3.379e-06 2.855e+03 0.000 1.000
## BirthplaceVietnam -1.448e+01 2.797e+03 -0.005 0.996
## BirthplaceZimbabwe 3.379e-06 3.956e+03 0.000 1.000
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 5577.6 on 13463 degrees of freedom
## Residual deviance: 5375.0 on 13338 degrees of freedom
## AIC: 5627
##
## Number of Fisher Scoring iterations: 16
Run a post-hoc power analysis with the pwr package. Use
the pwr.f2.test function for multiple regression power
analysis.
##
## Multiple regression power calculation
##
## u = 2
## v = 55
## f2 = 0.175508
## sig.level = 0.05
## power = 0.8
Results are interpreted clearly using APA style; connection to hypothesis is made; statistical significance and practical implications are addressed; power level is addressed.
Include at least one table and one graph that effectively summarize your analysis and findings. Use R code chunks to generate these visualizations.
library(dplyr)
anxiety$GADE <- as.numeric(anxiety$GADE)
anxiety <- anxiety %>%
mutate(Anxiety_Split = ifelse(GAD7 > median(GAD7, na.rm = TRUE), "High Anxiety", "Low Anxiety"))
table(anxiety$Anxiety_Split)##
## High Anxiety Low Anxiety
## 5020 8444
Discuss the implications of your results for psychological theory or practice. Address the following points:
List the articles you reviewed in APA format. Do not worry about the indentations. References:
Giacaman, R., Hamayel, L., & Hammoudeh, D. (2025). Shibboleth Authentication Request. Oclc.org. https://www-sciencedirect-com.dvc.idm.oclc.org/science/article/pii/S0140673617320524
Oh, H., Goehring, J., Jacob, L., & Smith, L. (2021). The Environment of Birthplace and Self-Reported Mental Health Conditions: Findings from the American Panel of Life. Epidemiologia, 2(3), 256–261. https://doi.org/10.3390/epidemiologia2030019