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Exploratory and Descriptive Statistics

• By: Julie Scott Jones & John Goldring
• Publisher: SAGE Publications Ltd
• Series: The SAGE Quantitative Research Kit
• Publication year: 2021
• Online pub date: January 16, 2024
• Discipline: Sociology , Criminology and Criminal Justice , Business and Management , Communication and Media Studies , Education , Psychology , Health , Social Work , Political Science and International Relations
• Methods: Descriptive statistics , Range , Standard deviations
• DOI: https:// doi. org/10.4135/9781529682786
• Keywords: child poverty , religion Show all Show less
• Print ISBN: 9781526424716
• Online ISBN: 9781529682786
• Buy the book icon link

Subject index

Nervous about statistics? This guide offers a clear, straight to the point break down of exploratory and descriptive statistics and its potential. Anchored by lots of examples and exercises to enhance your learning, it offers gudience on how to:  • Identify and access different types of variables and data  • Select the best method for measuring your chosen variables and data  • Use data viusalization techniques to tell stories with your data  • Appropriately clean and manage your data Part of The SAGE Quantitative Research Kit, this book will give you the know-how and confidence needed to succeed on your quantitative research journey.

Front Matter

• The SAGE Quantitative Research Kit
• List of Figures, Tables and Boxes
• About the Authors
• Chapter 1: Introducing Descriptive and Exploratory Statistics
• Chapter 2: Finding Data to Describe
• Chapter 3: Measure Everything – Learn Something – Answer Nothing: An Exploration Into Variables and Types of Measurement
• Chapter 4: I Am Not a Number, I Am a Categorical Variable
• Chapter 5: I Like Being Average, I Am an Interval Variable
• Chapter 6: Visualising Our Data
• Chapter 7: The Story Waiting to Be Told

Back Matter

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14 Quantitative analysis: Descriptive statistics

Numeric data collected in a research project can be analysed quantitatively using statistical tools in two different ways. Descriptive analysis refers to statistically describing, aggregating, and presenting the constructs of interest or associations between these constructs. Inferential analysis refers to the statistical testing of hypotheses (theory testing). In this chapter, we will examine statistical techniques used for descriptive analysis, and the next chapter will examine statistical techniques for inferential analysis. Much of today’s quantitative data analysis is conducted using software programs such as SPSS or SAS. Readers are advised to familiarise themselves with one of these programs for understanding the concepts described in this chapter.

Data preparation

In research projects, data may be collected from a variety of sources: postal surveys, interviews, pretest or posttest experimental data, observational data, and so forth. This data must be converted into a machine-readable, numeric format, such as in a spreadsheet or a text file, so that they can be analysed by computer programs like SPSS or SAS. Data preparation usually follows the following steps:

Data coding. Coding is the process of converting data into numeric format. A codebook should be created to guide the coding process. A codebook is a comprehensive document containing a detailed description of each variable in a research study, items or measures for that variable, the format of each item (numeric, text, etc.), the response scale for each item (i.e., whether it is measured on a nominal, ordinal, interval, or ratio scale, and whether this scale is a five-point, seven-point scale, etc.), and how to code each value into a numeric format. For instance, if we have a measurement item on a seven-point Likert scale with anchors ranging from ‘strongly disagree’ to ‘strongly agree’, we may code that item as 1 for strongly disagree, 4 for neutral, and 7 for strongly agree, with the intermediate anchors in between. Nominal data such as industry type can be coded in numeric form using a coding scheme such as: 1 for manufacturing, 2 for retailing, 3 for financial, 4 for healthcare, and so forth (of course, nominal data cannot be analysed statistically). Ratio scale data such as age, income, or test scores can be coded as entered by the respondent. Sometimes, data may need to be aggregated into a different form than the format used for data collection. For instance, if a survey measuring a construct such as ‘benefits of computers’ provided respondents with a checklist of benefits that they could select from, and respondents were encouraged to choose as many of those benefits as they wanted, then the total number of checked items could be used as an aggregate measure of benefits. Note that many other forms of data—such as interview transcripts—cannot be converted into a numeric format for statistical analysis. Codebooks are especially important for large complex studies involving many variables and measurement items, where the coding process is conducted by different people, to help the coding team code data in a consistent manner, and also to help others understand and interpret the coded data.

Data entry. Coded data can be entered into a spreadsheet, database, text file, or directly into a statistical program like SPSS. Most statistical programs provide a data editor for entering data. However, these programs store data in their own native format—e.g., SPSS stores data as .sav files—which makes it difficult to share that data with other statistical programs. Hence, it is often better to enter data into a spreadsheet or database where it can be reorganised as needed, shared across programs, and subsets of data can be extracted for analysis. Smaller data sets with less than 65,000 observations and 256 items can be stored in a spreadsheet created using a program such as Microsoft Excel, while larger datasets with millions of observations will require a database. Each observation can be entered as one row in the spreadsheet, and each measurement item can be represented as one column. Data should be checked for accuracy during and after entry via occasional spot checks on a set of items or observations. Furthermore, while entering data, the coder should watch out for obvious evidence of bad data, such as the respondent selecting the ‘strongly agree’ response to all items irrespective of content, including reverse-coded items. If so, such data can be entered but should be excluded from subsequent analysis.

Data transformation. Sometimes, it is necessary to transform data values before they can be meaningfully interpreted. For instance, reverse coded items—where items convey the opposite meaning of that of their underlying construct—should be reversed (e.g., in a 1-7 interval scale, 8 minus the observed value will reverse the value) before they can be compared or combined with items that are not reverse coded. Other kinds of transformations may include creating scale measures by adding individual scale items, creating a weighted index from a set of observed measures, and collapsing multiple values into fewer categories (e.g., collapsing incomes into income ranges).

Univariate analysis

Univariate analysis—or analysis of a single variable—refers to a set of statistical techniques that can describe the general properties of one variable. Univariate statistics include: frequency distribution, central tendency, and dispersion. The frequency distribution of a variable is a summary of the frequency—or percentages—of individual values or ranges of values for that variable. For instance, we can measure how many times a sample of respondents attend religious services—as a gauge of their ‘religiosity’—using a categorical scale: never, once per year, several times per year, about once a month, several times per month, several times per week, and an optional category for ‘did not answer’. If we count the number or percentage of observations within each category—except ‘did not answer’ which is really a missing value rather than a category—and display it in the form of a table, as shown in Figure 14.1, what we have is a frequency distribution. This distribution can also be depicted in the form of a bar chart, as shown on the right panel of Figure 14.1, with the horizontal axis representing each category of that variable and the vertical axis representing the frequency or percentage of observations within each category.

With very large samples, where observations are independent and random, the frequency distribution tends to follow a plot that looks like a bell-shaped curve—a smoothed bar chart of the frequency distribution—similar to that shown in Figure 14.2. Here most observations are clustered toward the centre of the range of values, with fewer and fewer observations clustered toward the extreme ends of the range. Such a curve is called a normal distribution .

Lastly, the mode is the most frequently occurring value in a distribution of values. In the previous example, the most frequently occurring value is 15, which is the mode of the above set of test scores. Note that any value that is estimated from a sample, such as mean, median, mode, or any of the later estimates are called a statistic .

Bivariate analysis

Bivariate analysis examines how two variables are related to one another. The most common bivariate statistic is the bivariate correlation —often, simply called ‘correlation’—which is a number between -1 and +1 denoting the strength of the relationship between two variables. Say that we wish to study how age is related to self-esteem in a sample of 20 respondents—i.e., as age increases, does self-esteem increase, decrease, or remain unchanged?. If self-esteem increases, then we have a positive correlation between the two variables, if self-esteem decreases, then we have a negative correlation, and if it remains the same, we have a zero correlation. To calculate the value of this correlation, consider the hypothetical dataset shown in Table 14.1.

After computing bivariate correlation, researchers are often interested in knowing whether the correlation is significant (i.e., a real one) or caused by mere chance. Answering such a question would require testing the following hypothesis:

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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18 Descriptive Research Examples

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Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.

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Descriptive research involves gathering data to provide a detailed account or depiction of a phenomenon without manipulating variables or conducting experiments.

A scholarly definition is:

“Descriptive research is defined as a research approach that describes the characteristics of the population, sample or phenomenon studied. This method focuses more on the “what” rather than the “why” of the research subject.” (Matanda, 2022, p. 63)

The key feature of descriptive research is that it merely describes phenomena and does not attempt to manipulate variables nor determine cause and effect .

To determine cause and effect , a researcher would need to use an alternate methodology, such as experimental research design .

Common approaches to descriptive research include:

• Cross-sectional research : A cross-sectional study gathers data on a population at a specific time to get descriptive data that could include categories (e.g. age or income brackets) to get a better understanding of the makeup of a population.
• Longitudinal research : Longitudinal studies return to a population to collect data at several different points in time, allowing for description of changes in categories over time. However, as it’s descriptive, it cannot infer cause and effect (Erickson, 2017).

Methods that could be used include:

• Surveys: For example, sending out a census survey to be completed at the exact same date and time by everyone in a population.
• Case Study : For example, an in-depth description of a specific person or group of people to gain in-depth qualitative information that can describe a phenomenon but cannot be generalized to other cases.
• Observational Method : For example, a researcher taking field notes in an ethnographic study. (Siedlecki, 2020)

Descriptive Research Examples

1. Understanding Autism Spectrum Disorder (Psychology): Researchers analyze various behavior patterns, cognitive skills, and social interaction abilities specific to children with Autism Spectrum Disorder to comprehensively describe the disorder’s symptom spectrum. This detailed description classifies it as descriptive research, rather than analytical or experimental, as it merely records what is observed without altering any variables or trying to establish causality.

2. Consumer Purchase Decision Process in E-commerce Marketplaces (Marketing): By documenting and describing all the factors that influence consumer decisions on online marketplaces, researchers don’t attempt to predict future behavior or establish causes—just describe observed behavior—making it descriptive research.

3. Impacts of Climate Change on Agricultural Practices (Environmental Studies): Descriptive research is seen as scientists outline how climate changes influence various agricultural practices by observing and then meticulously categorizing the impacts on crop variability, farming seasons, and pest infestations without manipulating any variables in real-time.

4. Work Environment and Employee Performance (Human Resources Management): A study of this nature, describing the correlation between various workplace elements and employee performance, falls under descriptive research as it merely narrates the observed patterns without altering any conditions or testing hypotheses.

5. Factors Influencing Student Performance (Education): Researchers describe various factors affecting students’ academic performance, such as studying techniques, parental involvement, and peer influence. The study is categorized as descriptive research because its principal aim is to depict facts as they stand without trying to infer causal relationships.

6. Technological Advances in Healthcare (Healthcare): This research describes and categorizes different technological advances (such as telemedicine, AI-enabled tools, digital collaboration) in healthcare without testing or modifying any parameters, making it an example of descriptive research.

7. Urbanization and Biodiversity Loss (Ecology): By describing the impact of rapid urban expansion on biodiversity loss, this study serves as a descriptive research example. It observes the ongoing situation without manipulating it, offering a comprehensive depiction of the existing scenario rather than investigating the cause-effect relationship.

8. Architectural Styles across Centuries (Art History): A study documenting and describing various architectural styles throughout centuries essentially represents descriptive research. It aims to narrate and categorize facts without exploring the underlying reasons or predicting future trends.

9. Media Usage Patterns among Teenagers (Sociology): When researchers document and describe the media consumption habits among teenagers, they are performing a descriptive research study. Their main intention is to observe and report the prevailing trends rather than establish causes or predict future behaviors.

10. Dietary Habits and Lifestyle Diseases (Nutrition Science): By describing the dietary patterns of different population groups and correlating them with the prevalence of lifestyle diseases, researchers perform descriptive research. They merely describe observed connections without altering any diet plans or lifestyles.

11. Shifts in Global Energy Consumption (Environmental Economics): When researchers describe the global patterns of energy consumption and how they’ve shifted over the years, they conduct descriptive research. The focus is on recording and portraying the current state without attempting to infer causes or predict the future.

12. Literacy and Employment Rates in Rural Areas (Sociology): A study aims at describing the literacy rates in rural areas and correlating it with employment levels. It falls under descriptive research because it maps the scenario without manipulating parameters or proving a hypothesis.

13. Women Representation in Tech Industry (Gender Studies): A detailed description of the presence and roles of women across various sectors of the tech industry is a typical case of descriptive research. It merely observes and records the status quo without establishing causality or making predictions.

14. Impact of Urban Green Spaces on Mental Health (Environmental Psychology): When researchers document and describe the influence of green urban spaces on residents’ mental health, they are undertaking descriptive research. They seek purely to understand the current state rather than exploring cause-effect relationships.

15. Trends in Smartphone usage among Elderly (Gerontology): Research describing how the elderly population utilizes smartphones, including popular features and challenges encountered, serves as descriptive research. Researcher’s aim is merely to capture what is happening without manipulating variables or posing predictions.

16. Shifts in Voter Preferences (Political Science): A study describing the shift in voter preferences during a particular electoral cycle is descriptive research. It simply records the preferences revealed without drawing causal inferences or suggesting future voting patterns.

17. Understanding Trust in Autonomous Vehicles (Transportation Psychology): This comprises research describing public attitudes and trust levels when it comes to autonomous vehicles. By merely depicting observed sentiments, without engineering any situations or offering predictions, it’s considered descriptive research.

18. The Impact of Social Media on Body Image (Psychology): Descriptive research to outline the experiences and perceptions of individuals relating to body image in the era of social media. Observing these elements without altering any variables qualifies it as descriptive research.

Descriptive vs Experimental Research

Descriptive research merely observes, records, and presents the actual state of affairs without manipulating any variables, while experimental research involves deliberately changing one or more variables to determine their effect on a particular outcome.

De Vaus (2001) succinctly explains that descriptive studies find out what is going on , but experimental research finds out why it’s going on /

Simple definitions are below:

• Descriptive research is primarily about describing the characteristics or behaviors in a population, often through surveys or observational methods. It provides rich detail about a specific phenomenon but does not allow for conclusive causal statements; however, it can offer essential leads or ideas for further experimental research (Ivey, 2016).
• Experimental research , often conducted in controlled environments, aims to establish causal relationships by manipulating one or more independent variables and observing the effects on dependent variables (Devi, 2017; Mukherjee, 2019).

Experimental designs often involve a control group and random assignment . While it can provide compelling evidence for cause and effect, its artificial setting might not perfectly mirror real-worldly conditions, potentially affecting the generalizability of its findings.

These two types of research are complementary, with descriptive studies often leading to hypotheses that are then tested experimentally (Devi, 2017; Zhao et al., 2021).

Benefits and Limitations of Descriptive Research

Descriptive research offers several benefits: it allows researchers to gather a vast amount of data and present a complete picture of the situation or phenomenon under study, even within large groups or over long time periods.

It’s also flexible in terms of the variety of methods used, such as surveys, observations, and case studies, and it can be instrumental in identifying patterns or trends and generating hypotheses (Erickson, 2017).

However, it also has its limitations.

The primary drawback is that it can’t establish cause-effect relationships, as no variables are manipulated. This lack of control over variables also opens up possibilities for bias, as researchers might inadvertently influence responses during data collection (De Vaus, 2001).

Additionally, the findings of descriptive research are often not generalizable since they are heavily reliant on the chosen sample’s characteristics.

See More Types of Research Design Here

De Vaus, D. A. (2001). Research Design in Social Research . SAGE Publications.

Devi, P. S. (2017). Research Methodology: A Handbook for Beginners . Notion Press.

Erickson, G. S. (2017). Descriptive research design. In  New Methods of Market Research and Analysis  (pp. 51-77). Edward Elgar Publishing.

Gresham, B. B. (2016). Concepts of Evidence-based Practice for the Physical Therapist Assistant . F.A. Davis Company.

Ivey, J. (2016). Is descriptive research worth doing?.  Pediatric nursing ,  42 (4), 189. ( Source )

Krishnaswamy, K. N., Sivakumar, A. I., & Mathirajan, M. (2009). Management Research Methodology: Integration of Principles, Methods and Techniques . Pearson Education.

Matanda, E. (2022). Research Methods and Statistics for Cross-Cutting Research: Handbook for Multidisciplinary Research . Langaa RPCIG.

Monsen, E. R., & Van Horn, L. (2007). Research: Successful Approaches . American Dietetic Association.

Mukherjee, S. P. (2019). A Guide to Research Methodology: An Overview of Research Problems, Tasks and Methods . CRC Press.

Siedlecki, S. L. (2020). Understanding descriptive research designs and methods.  Clinical Nurse Specialist ,  34 (1), 8-12. ( Source )

Zhao, P., Ross, K., Li, P., & Dennis, B. (2021). Making Sense of Social Research Methodology: A Student and Practitioner Centered Approach . SAGE Publications.

• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 23 Achieved Status Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 25 Defense Mechanisms Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 15 Theory of Planned Behavior Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 18 Adaptive Behavior Examples

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 23 Achieved Status Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 15 Ableism Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 25 Defense Mechanisms Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 15 Theory of Planned Behavior Examples

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Quantitative Research

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Quantitative research methods are concerned with the planning, design, and implementation of strategies to collect and analyze data. Descartes, the seventeenth-century philosopher, suggested that how the results are achieved is often more important than the results themselves, as the journey taken along the research path is a journey of discovery. High-quality quantitative research is characterized by the attention given to the methods and the reliability of the tools used to collect the data. The ability to critique research in a systematic way is an essential component of a health professional’s role in order to deliver high quality, evidence-based healthcare. This chapter is intended to provide a simple overview of the way new researchers and health practitioners can understand and employ quantitative methods. The chapter offers practical, realistic guidance in a learner-friendly way and uses a logical sequence to understand the process of hypothesis development, study design, data collection and handling, and finally data analysis and interpretation.

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Wilson, L.A. (2019). Quantitative Research. In: Liamputtong, P. (eds) Handbook of Research Methods in Health Social Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-10-5251-4_54

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3 Descriptive Research Design

• Published: March 2018
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This chapter presents two of the most prominent approaches to the design of descriptive research in music education. Simply creating depictions of music teaching and learning experiences that are organized and illustrative of the variation that can exist in any given setting is a worthwhile scientific endeavor in and of itself. Descriptive research is most typically an exploration of what is, what exists, and/or the status of any given topic of interest. The first section deals with basic steps in observational research designs, and the second section outlines critical features of survey designs. These fundamental research design options are excellent entry points for emerging scholars and when employed imaginatively can yield many benefits for the profession.

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Background | Publications | Courses | Vitae | Contact

I received my Ph.D. (2005) and M.A. (2001) in Sociology from the University of Massachusetts Amherst . Previously, I earned a B.A. in Sociology from the State Academy of Management in Moscow, Russia. I am an Associate Professor in the Sociology Department of Boston College since 2005. I am also affiliated with the Sloan Center on Aging and Work/Workplace Flexibility . My research interests include race, gender, and class, family sociology, aging and the life course, and quantitative methods.

Sarkisian, Natalia , and Naomi Gerstel. 2012. Nuclear Family Values, Extended Family Lives: The Importance of Gender, Race, and Class . New York: Routledge.

Selected Journal Articles

McNamara, Tay, Marcie Pitt-Catsouphes, Natalia Sarkisian , Elyssa Besen, and Miwako Kidahashi. Forthcoming. "Age Bias in the Workplace: Cultural Stereotypes and In-Group Favoritism." The International Journal of Aging and Human Development . Sarkisian, Natalia , and Naomi Gerstel. 2016. "Does Singlehood Isolate or Integrate? Examining the Link between Marital Status and Ties to Kin, Friends, and Neighbors." Journal of Social and Personal Relationships , 33, 3, 361-384. ***Article selected to be highlighted in "Relationship Matters" podcast (#49)*** Sweet, Stephen, Natalia Sarkisian , Christina Matz-Costa, and Marcie Pitt-Catsouphes. 2015. "Are Women Less Career Centric Than Men? Structure, Culture, and Identity Investments." Community, Work, and Family , 19, 4. Karamnov, Sergey, Natalia Sarkisian , Rebecca Grammer, Wendy L. Gross, and Richard D. Urman. 2015. " Analysis of Adverse Events Associated with Adult Moderate Procedural Sedation Outside the Operating Room. " Journal of Patient Safety . Calvo, Rocio, and Natalia Sarkisian . 2015. " Racial/Ethnic Differences in Postmigration Education among Adult Immigrants in the USA ." Ethnic and Racial Studies , 38, 7, 1029-1049. Calvo, Esteban, Christine A. Mair, and Natalia Sarkisian . 2015. " Individual Troubles, Shared Troubles: The Multiplicative Effect of Individual and Country-level Unemployment on Life Satisfaction in 95 Nations (1981-2009) ." Social Forces , 93, 4 (June), 1625-1653. Johnson, Jessica, Natalia Sarkisian , and John Williamson. 2015. " Using a Micro-Level Model to Generate a Macro-Level Model of Productive Successful Aging ." The Gerontologist , 55, 1 (February), 107-119. Calvo, Esteban, Natalia Sarkisian , and Christopher R. Tamborini. 2013. " Causal Effects of Retirement Timing on Subjective Well-being: The Role of Cultural Norms and Institutional Policies ." Journal of Gerontology: Social Sciences , 68, 1 (January), 73-84. *** Winner of the 2013 James G. Zimmer New Investigator Research Award of the APHA Aging and Public Health Section *** Garroutte, Eva Marie, Natalia Sarkisian , and Sergey Karamnov. 2012. " Affective Interactions in Medical Visits: Ethnic Differences among American Indian Older Adults ." Journal of Aging and Health , 24, 7 (October), 1223-1251. Oh, Seil, and Natalia Sarkisian . 2012. " Spiritual Individualism or Engaged Spirituality? Social Implications of Holistic Spirituality among Mind-Body-Spirit Practitioners ." Sociology of Religion , 73, 3 (Autumn), 299-322. Collins, Amy L., Natalia Sarkisian , and Ellen Winner. 2009. " Flow and Happiness in Later Life: An Investigation into the Role of Daily and Weekly Flow Experiences ." Journal of Happiness Studies , 10, 6 (December), 703-709. Garroutte, Eva, Natalia Sarkisian , Dedra Buchwald, Jack Goldberg, and Jan Beals. 2008. " Perceptions of Medical Interaction between Healthcare Providers and American Indian Older Adults ." Social Science and Medicine , 67, 4 (August), 546-556. Shen, Ce, Natalia Sarkisian , and Thanh Tran. 2008. " Child Mortality, Economic Development, and Social Inequality in Less Developed Countries: A Cross-National Analysis ." China Journal of Social Work, 1 , 2 (July), 172-188. Sarkisian, Natalia , and Naomi Gerstel. 2008. " Till Marriage Do Us Part: Adult Children's Relationships with Parents ." Journal of Marriage and Family, 70 , 2 (May), 360-376. Sarkisian, Natalia . 2007. " Street Men, Family Men: Race and Men's Extended Family Involvement ." Social Forces, 86 , 2 (December), 763-794. *** Winner of the 2008 Oliver Cromwell Cox Article Award from the Section on Racial and Ethnic Minorities of the American Sociological Association *** Sarkisian, Natalia , Mariana Gerena, and Naomi Gerstel. 2007. " Extended Family Integration among Euro and Mexican Americans: Ethnicity, Gender, and Class ." Journal of Marriage and Family, 69 , 1 (February), 40-54. *** Winner of the 2008 Distinguished Contribution to Scholarship Award from the Race, Gender, Class Section of the American Sociological Association *** Sarkisian, Natalia . 2006. " 'Doing Family Ambivalence': Nuclear and Extended Families in Single Mothers' Lives ." Journal of Marriage and Family, 68 , 4 (November), 804-811. Gerstel, Naomi, and Natalia Sarkisian . 2006. " Marriage: The Good, the Bad, and the Greedy ." Contexts, 5 , 4 (November), 16-21. Sarkisian, Natalia , Mariana Gerena, and Naomi Gerstel. 2006. " Extended Family Ties among Mexicans, Puerto Ricans, and Whites: Superintegration or Disintegration? " Family Relations, 55 , 3 (July), 331-344. Garroutte, Eva, Natalia Sarkisian , Lester Arguellos, Jack Goldberg, and Dedra Buchwald. 2006. " Cultural Identity and Perceptions of Health Status among American Indian Older Adults and Their Healthcare Providers ." Journal of General Internal Medicine, 21 , 2 (February), 111-116. Sarkisian, Natalia , and Naomi Gerstel. 2004. " Kin Support Among Blacks and Whites: Race and Family Organization ." American Sociological Review, 69 , 4 (December), 812-837. Sarkisian, Natalia , and Naomi Gerstel. 2004. " Explaining the Gender Gap in Help to Parents: The Importance of Employment ." Journal of Marriage and Family, 66 , 2 (May), 431-451. *** Winner of the 2005 Rosabeth Moss Kanter International Award for Research Excellence in Families and Work ***

Selected Book Chapters

Courses taught at bc.

SOCY7704: Regression Models for Categorical Data The major topics of the course include OLS regression diagnostics, binary, ordered, and multinomial logistic regression, models for the analysis of count data (e.g., Poisson and negative binomial regression), treatment of missing data, and the analysis of clustered and stratified samples. All analyses in the course are conducted using Stata, but no previous Stata experience is necessary. SOCY7705: Advanced Statistics This course introduces students to both hierarchical linear modeling (HLM) and structural equation modeling (SEM). The analyses in the course are conducted using HLM and LISREL software. SOCY7706: Longitudinal Data Analysis This course will focus on panel data management and analysis, with topics including change models, fixed and random effects models, GEE models, and mixed models. All analyses in the course are conducted using Stata, but no previous Stata experience is necessary. SOCY7708: Hierarchical Linear Modeling The major topics of this applied course will include two-level models for continuous, categorical, and count outcomes, three-level models, growth curve models, models for dyadic data, and cross-nested models. The analyses in this course are conducted using HLM software. SOCY7709: Quantitative Data Management This course will provide a comprehensive introduction to quantitative data management using Stata; the focus will be on working with complex datasets (both cross-sectional and longitudinal) and preparing them for analysis. This course is intended for students who need to manage data for academic or non-academic projects. SOCY7781: Dissertation Seminar This is a continuing research workshop which covers all stages of the research process, from conceptualization and theory development through data analysis and writing. The workshop is intended primarily for sociology graduate students working on dissertation proposals and dissertations. Others will be welcomed on a case-by-case basis. The group meets bi-weekly, with individual meetings with the professor as necessary. All students who are writing dissertations are strongly recommended to enroll in this workshop, at least for one semester. SOCY3361: Family and Work This course explores the relationship between family and work from a sociological perspective. The course is designed to provide the information and critical skills necessary to address the modern conundrum of work and family life. We shatter common myths about our past and our present and examine how social forces shape the paid and unpaid work that families do. We focus on contemporary families but also attempt to situate them in a broader historical context. Throughout the course, we stay tuned to the issues of race, gender, class, and sexuality. We also explore the relationship of work and family to social policies and social change, investigating ways in which policies create and address inequities and thinking critically about mechanisms for change.

Contact Information

Mailing Address: Natalia Sarkisian Department of Sociology Boston College 140 Commonwealth Ave Chestnut Hill, MA 02467 Phone : (617) 552-0495 Email : [email protected]

Updated : September 1, 2016

FROM SELMA TO MOSCOW:

How Human Rights Activists Transformed U.S. Foreign Policy

The 1960s marked a transformation of human rights activism in the United States. At a time of increased concern for the rights of their fellow citizens–civil and political rights, as well as the social and economic rights that Great Society programs sought to secure–many Americans saw inconsistencies between domestic and foreign policy and advocated for a new approach. The activism that arose from the upheavals of the 1960s fundamentally altered U.S. foreign policy–yet previous accounts have often overlooked its crucial role.

In  From Selma to Moscow,  Sarah B. Snyder traces the influence of human rights activists and advances a new interpretation of U.S. foreign policy in the “long 1960s.” She shows how transnational connections and social movements spurred American activism that achieved legislation that curbed military and economic assistance to repressive governments, created institutions to monitor human rights around the world, and enshrined human rights in U.S. foreign policy making for years to come. Snyder analyzes how Americans responded to repression in the Soviet Union, racial discrimination in Southern Rhodesia, authoritarianism in South Korea, and coups in Greece and Chile. By highlighting the importance of nonstate and lower-level actors, Snyder shows how this activism established the networks and tactics critical to the institutionalization of human rights. A major work of international and transnational history,  From Selma to Moscow  reshapes our understanding of the role of human rights activism in transforming U.S. foreign policy in the 1960s and 1970s and highlights timely lessons for those seeking to promote a policy agenda resisted by the White House.

Praise for From Selma to Moscow

“Based on deep and thorough archival research, as well as an innovative and creative use of quantitative measures, Snyder’s book demonstrates that issues of human rights emerged as a significant priority for many Americans, both political leaders and activists, well before the Carter Administration. From Selma to Moscow is an extremely important contribution to what remains one of the most important challenges in American foreign policy.”

–Thomas Schwartz, Vanderbilt University

“In this illuminating book Sarah Snyder explains the origins of the human rights movement in the 1960s and chronicles its evolution until the inauguration of Jimmy Carter. Linking the evolution of human rights to other social movements, she probes the motives, highlights the transnational connections, and analyzes the successes and failures of activists regarding human rights violations inside the Soviet Union, Southern Rhodesia, Greece, South Korea, and Chile. This book is an important contribution to the literature on human rights.”

–Melvyn Leffler, Edward Stettinius Professor of American History, University of Virginia

“Human rights is emerging as one of the central concerns of modern humanities and social science scholarship. From Selma to Moscow illuminates the missing links between histories of the 1940s and the 1970s, the focus of previous studies. Sarah Snyder’s globe-spanning tale of activists and policy makers reveals the significance of the 1960s for bringing human rights to the forefront of U.S. foreign relations. An important book from an excellent historian.”

–Tim Borstelmann, University of Nebraska–Lincoln

“In this impressive and deeply researched work, Sarah Snyder reveals the way global struggles over human rights became a feature of American politics and foreign policy in the 1960s and 70s as activists, journalists, and Congress members made the case that the United States was complicit if the country ignored brutal repression. An important contribution.”

–Mary L. Dudziak, Emory University School of Law

of From Selma to Moscow

Click on the journal title to read the full review.

Federal History

Reviewed by Paul Adler, Theresa Keeley, Robert Rakove, Matthew K. Shannon

American Historical Review

Reviewed by  Kelly J. Shannon

Diplomatic History

Reviewed by Michael Cotey Morgan

Journal of American History

Reviewed by  Jennifer Delton

Cold War History

Reviewed by  Joe Renouard

New Books Network

Reviewed by Zeb Larson

Reviewed by several, with response

Journal of Cold War Studies

Reviewed by William Michael Schmidli

Human Rights Quarterly

Reviewed by Debbie Sharnak

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The Moscow-Tartu semiotics school: A bibliography of works and comments in English (Journal for descriptive poetics and theory of literature) Unknown Binding – January 1, 1978

• Language English
• Publisher North-Holland Pub. Co
• Publication date January 1, 1978
• See all details

Product details

• ASIN ‏ : ‎ B0007C8UTY
• Publisher ‏ : ‎ North-Holland Pub. Co (January 1, 1978)
• Language ‏ : ‎ English

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• 5 star 4 star 3 star 2 star 1 star 4 star 0% 0% 0% 0% 0% 0%
• 5 star 4 star 3 star 2 star 1 star 3 star 0% 0% 0% 0% 0% 0%
• 5 star 4 star 3 star 2 star 1 star 2 star 0% 0% 0% 0% 0% 0%
• 5 star 4 star 3 star 2 star 1 star 1 star 0% 0% 0% 0% 0% 0%

Customer Reviews, including Product Star Ratings help customers to learn more about the product and decide whether it is the right product for them.

To calculate the overall star rating and percentage breakdown by star, we don’t use a simple average. Instead, our system considers things like how recent a review is and if the reviewer bought the item on Amazon. It also analyzed reviews to verify trustworthiness.

No customer reviews

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