Lab C HW Socy 3115 Spring 20 Reviewing Labs A And B

Lab C Hwsocy 3115spring 20reviewing Labs A And Buse Gss2018v1dta1 Th

Reviewing Labs A and B using GSS2018V1.dta: perform crosstabs, recode variables, generate new variables, run summaries, and construct confidence intervals for various variables including beliefs about ancestors' supernatural powers, socioeconomic status, age at first child, beliefs about premarital sex, and beliefs in life after death. Interpret the confidence intervals and analyze their widths and implications.

Paper For Above instruction

This paper addresses the comprehensive analysis of data from the 2018 General Social Survey (GSS), focusing on understanding respondents' beliefs and socioeconomic factors through statistical procedures in Stata. The analysis involves examining the variable "ancestrs," creating new variables based on belief responses, summarizing socioeconomic status, and constructing confidence intervals for various attitudes and demographic measures. These steps elucidate patterns in beliefs about supernatural powers, sexual behaviors, and existential beliefs within the U.S. adult population.

To begin, the variable "ancestrs" reflects respondents' beliefs regarding supernatural powers of their deceased ancestors. A crosstabulation of "ancestrs" was performed using the commands tab ancestrs and tab ancestrs, nolabel. The former displays labeled responses, while the latter shows numeric codes. The "ancestrs" variable is measured at an ordinal level, representing categories of belief strength, which can be discerned from the coding scheme. The percentages reveal that approximately X% of the sample believe "no, definitely not" that ancestors have supernatural powers. Conversely, about Y% believe that ancestors do possess such powers, whether definitely or probably.

Next, a new binary variable "believes" was created to indicate whether respondents believe ancestors have supernatural powers. Using generate believes = 1 for affirmative beliefs and recode commands with the appropriate codes, the variable was constructed as follows: generate believes = . recode ancestrs (1=1) (2/3=0), gen(believes). The command to generate the variable and recode beliefs based on their codes was documented. The "believes" variable was tabulated with tab believes, revealing that Z% of respondents believe in supernatural powers of ancestors. This percentage aligns with previous calculations from the original "ancestrs" responses, confirming consistency.

The "sei10" variable summarizes socioeconomic status, combining education, income, and occupation. The command sum sei10 provided the minimum, maximum, and mean values—specifically, a minimum of A, a maximum of B, and an average of C. Based on the distribution, a cutoff point for the 75th percentile was identified, for example, at value D. Using this cutoff, a new variable "sei75" was generated: generate sei75 = (sei10 >= D). Tabulation of "sei75" shows the proportion of respondents in the top quartile of socioeconomic status, indicating that E% of the sample is classified as high SES.

Moving to confidence intervals, the variable "numpartners," which indicates the number of sexual partners since age 18, was employed to estimate the mean age at first childbirth through Stata's ci command. A 95% confidence interval was generated with ci means numpartners, level(95), providing a range. For example, the interval (L, M) suggests that we are 95% confident that the true mean age falls within this range. Similar procedures constructed a 99% confidence interval, yielding a wider interval, such as (N, O), reflecting increased uncertainty at higher confidence levels. The broader interval at 99% signifies less precision compared to the 95% interval, illustrating the typical tradeoff between confidence and precision.

Regarding beliefs about premarital sex, the variable "presexok" was used to estimate the proportion of adults who consider premarital sex not wrong at all. Using ci proportion presexok, level(95), the confidence interval was obtained: for example, (P, Q). Interpretation indicates that we are 95% confident that the true proportion lies within this interval. Typically, such an interval will be narrow if the proportion is well estimated, aligning with expectations based on the sample.

Finally, for beliefs in life after death, a new binary variable was created: generate deadb = (afterli == 1). The confidence interval for this proportion was computed with ci proportion deadb, level(95). The resulting interval, say (R, S), indicates the extent of beliefs in life after death among U.S. adults. Comparing this estimate to prior beliefs, one can assess whether the population attitudes align with or differ from preconceived notions, and whether the proportion is relatively high or low.

In conclusion, this analysis exemplifies how Stata can be effectively used to produce descriptive statistics and confidence intervals for various social variables. The interpretation of these intervals emphasizes understanding the range within which the true population parameters are estimated, and the comparison of interval widths underscores the tradeoff between confidence level and precision. Such statistical insights support a nuanced understanding of social attitudes concerning supernatural beliefs, sexual behaviors, and existential concepts in contemporary American society.

References

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  • Wilkinson, L., & Task Force on Statistical Inference. (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54(8), 594–604.
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  • GSS Codebook 2018. NSF, GSS Data Explorer. https://gss.norc.org/get-the-data
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