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Quantitative Data

Last Updated: Jun 21, 2023

Quantitative data has long been a core component of anthropological research. Since the advent of cultural anthropology during the early 20th century, numerical and statistical methods have been employed to further our understanding of human cultures , behaviors, and societies [1] .

Overview of Quantitative Data in Anthropology

In anthropology , quantitative data involves the collection and analysis of numerical information to reveal patterns, trends, correlations, or generalizations about the human condition. This can be gleaned from various sources such as population censuses, health records, economic indicators, or surveys.

For instance, an anthropologist studying dietary practices across cultures might employ quantitative methods to measure and compare food intake or nutritional indices. A seminal work in this regard is Murdock’s [2] . Ethnographic Atlas, which compiled quantitative data on a variety of cultural traits across hundreds of societies, providing invaluable insights into cross-cultural patterns and diversities.

While quantitative data provides rigorous, generalizable insights, it’s important to recognize that it is often complemented by qualitative data, offering a holistic understanding of human experiences [1] .

Methodology

Research design.

In anthropological research, design is instrumental to the structure and execution of a study. A common research design is the cross-sectional study, where data is collected from a sample population at a specific point in time [1] . This is often employed in cultural anthropology to assess variations across different cultural groups or regions.

Data Collection Techniques

Surveys: Surveys are standard instruments for collecting quantitative data. Anthropologists often use structured interviews or standardized questionnaires to gather information about beliefs, behaviors, and social practices.
Questionnaires: Questionnaires, either self-administered or researcher-administered, are used to gather standardized data. In an anthropological study examining social networks, questionnaire items might include queries about respondents’ relationships and interactions.
Experiments: Experimental methods are used less frequently in anthropology but can offer valuable insights. Laboratory or field experiments, for instance, can help determine causal relationships or test hypotheses about human behavior.
Archival Research: Anthropologists may use archival data, such as historical documents, census data, or institutional records, to gather quantitative information.

Sampling Techniques

There are two types of sampling techniques in research: Probability Sampling Methods and Non-Probability Methods.

Probability sampling methods ensure each member of a population has a known, non-zero chance of being selected , facilitating the collection of unbiased, representative data. These techniques offer more precise and generalizable findings. Here are several types of probability sampling methods:

Simple Random Sampling (SRS): In SRS, each individual in the population has an equal and independent chance of being chosen for the sample. This method ensures a high level of representativeness, and conclusions drawn from the sample can be generalized to the population. However, it requires an exhaustive list of all members in the population, which may not always be feasible.
Systematic Sampling: This involves selecting a random starting point from the population list and then picking every nth element. The key benefit is its simplicity. But, the order of the list may introduce bias if there’s a pattern that coincides with the chosen interval.
Stratified Sampling: The population is divided into homogeneous subgroups, or strata, and random samples are drawn from each stratum. This technique ensures representation of all key groups within the population and can increase the efficiency and precision of the estimate.
Cluster Sampling: In this method, the population is divided into clusters, usually geographically. Random clusters are then chosen and all individuals within these clusters are included in the sample. This method can save time and resources when the population is geographically dispersed but might be less precise than simple random or stratified sampling.

Non-probability sampling techniques don’t give each member of a population a known chance of being included in the sample. These methods are often used when it’s not feasible to conduct probability sampling, and while they can be easier and cheaper to implement, they don’t allow for statistical inference about the entire population.

Convenience Sampling: This is the most straightforward type of non-probability sampling, where participants are selected based on their availability or ease of access. It’s used when a random sample isn’t necessary or when it isn’t feasible to choose a random sample. However, the results may not be representative of the broader population.
Snowball Sampling: Also known as referral sampling, this method is often used when it’s difficult to identify members of a desired population. Researchers start with a small pool of initial informants, then each of those informants provides referrals to other participants, creating a ‘snowball’ effect. This method is particularly useful when studying hidden or hard-to-reach populations.
Quota Sampling: This method is the non-probability counterpart of stratified sampling. Researchers divide the population into specific groups based on relevant characteristics and then non-randomly select individuals from each group until the quotas are filled. The proportion of participants in each quota matches their proportion in the population.
Purposive Sampling: In this method, researchers use their judgment to select participants who are most able to contribute useful data. For example, if conducting research on specialized practices within a culture, anthropologists might use purposive sampling to select individuals who are considered experts in those practices.

Data Analysis

Descriptive statistics.

Descriptive statistics summarize and describe the main features of a dataset. Anthropologists use them to provide a clear and concise summary of their data.

Measures of Central Tendency: These include the mean (average), median (middle value), and mode (most frequent value). For instance, an anthropologist studying age at marriage in a community might use the mean age to describe the central tendency of their data.
Measures of Dispersion: These describe the spread or variability of the data, including range, variance, and standard deviation. For instance, studying income distribution in a society, the range of incomes and the standard deviation can help understand economic inequality.

Inferential Statistics

Inferential statistics allow researchers to draw conclusions about a population based on a sample. They form the backbone of hypothesis testing in anthropological research.

Hypothesis Testing: Hypothesis testing involves proposing a claim about a population parameter and then using sample data to test the claim. For instance, an anthropologist might hypothesize that there is a significant difference in fertility rates between two cultural groups.
Confidence Intervals: These provide a range of values within which the population parameter is likely to fall. An anthropologist estimating the average number of children per woman in a community might report it as a range, giving a confidence interval.
Correlation Analysis: This measures the relationship between two variables. For instance, an anthropologist might analyze the correlation between education level and income within a society.
Regression Analysis: This allows researchers to predict the value of one variable based on the value of another. An anthropologist could use regression analysis to predict a community member’s social status based on variables like age, education, and occupation.

Comparisons with Qualitative Data

Advantages of quantitative data.

Quantitative data provide numeric evidence that can be analyzed statistically to support or refute hypotheses about a population. The results are replicable, allowing for generalizability of findings. For instance, anthropologists might use quantitative data to measure the prevalence of a certain cultural practice in different societies.

Limitations of Quantitative Data

Despite its strengths, quantitative data isn’t well-suited to explore all research questions. It often fails to capture the richness and complexity of human experiences and doesn’t illuminate the meanings or interpretations people assign to their experiences. It also requires a larger sample size to be statistically significant and might oversimplify complex social phenomena.

Complementary Relationship between Quantitative and Qualitative Data

Qualitative and quantitative data serve complementary roles in anthropological research. While quantitative data can illustrate patterns and trends across a population, qualitative data can provide a deep, nuanced understanding of the cultural context and individual perspectives. For instance, an anthropologist might combine a quantitative survey of marriage patterns in a community with qualitative interviews to understand the underlying beliefs and norms influencing these patterns.

Summary of the Article’s Main Points

This article has comprehensively covered the concept, methodologies, analysis, and comparison of quantitative data in anthropological research. Quantitative data provides numerical evidence that facilitates a statistical understanding of phenomena, such as cultural practices or societal trends. Key methodologies discussed include research design, data collection, and sampling techniques, both probability and non-probability. The use of descriptive and inferential statistics in analyzing quantitative data has also been outlined, from measures of central tendency and dispersion to hypothesis testing and regression analysis. The advantages and limitations of quantitative data were considered alongside qualitative data, illuminating their complementary relationship in anthropological research.

Implications for Anthropology and Future Research

Quantitative data plays a crucial role in anthropology, providing a foundation for testing hypotheses and making generalizable conclusions. Yet, the importance of blending these approaches with qualitative methods to obtain a richer, more nuanced understanding of cultural phenomena has been highlighted. For future research, embracing a mixed-methods approach will continue to strengthen the anthropological understanding of human societies and cultures. As we navigate the evolving landscapes of research, the refinement of data collection techniques, alongside advancements in data analysis tools, will further enhance the application and utility of quantitative data in anthropology.

References:

[1] Bernard, H. R. (2011). Research Methods in Anthropology: Qualitative and Quantitative Approaches. AltaMira Press. https://ds.amu.edu.et/xmlui/bitstream/handle/123456789/11396/Russel-Research-Method-in-Anthropology.pdf?sequence=1&isAllowed=y

[2] Murdock, G. P. (1967). Ethnographic Atlas. University of Pittsburgh Press.

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2.6: Quantitative and Qualitative Analysis

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Jennifer Hasty, David G. Lewis, & Marjorie M. Snipes

Learning Objectives

By the end of this section, you will be able to:

Identify differences between quantitative and qualitative information.
Provide an example of how an anthropologist might model research findings.
Describe the steps of the scientific method.

Differences between Quantitative and Qualitative Information

Quantitative information is measurable or countable data that can provide insight into research questions. Quantitative information is one of the most direct ways to understand limited, specific questions, such as how often people in a culture perform a certain action or how many times an art form or motif appears in a cultural artifact. Statistics created from quantitative data help researchers understand trends and changes over time. Counts of cultural remains, such as the number and distribution of animal remains found at a campsite, can show how much the campsite was used and what type of animal was being hunted. Statistical comparisons may be made of several different sites that Indigenous peoples used to process food in order to determine the primary purpose of each site.

In cultural research, qualitative data allows anthropologists to understand culture based on more subjective analyses of language, behavior, ritual, symbolism, and interrelationships of people. Qualitative data has the potential for more in-depth responses via open-ended questions, which can be coded and categorized in order to better identify common themes. Qualitative analysis is less about frequency and the number of things and more about a researcher’s subjective insights and understandings. Anthropology and other fields in the social sciences frequently integrate both types of data by using mixed methods. Through the triangulation of data, anthropologists can use both objective and frequency data (for example, survey results) and subjective data (such as observations) to provide a more holistic understanding.

Many anthropologists create models to help others visualize and understand their research findings. Models help people understand the relationships between various points of data and can include qualitative elements as well. One very familiar model is a map. Maps are constructed from many thousands of data points projected onto a flat surface to help people understand distances and relationships. Maps are typically two-dimensional, but we are of course all familiar with the three-dimensional version of a world map known as a globe. Maps and globes are built on data points, but they also include qualitative information, such as the colors used to represent various features and the human-assigned names of various geographical features. Other familiar types of models include graphs, calendars, timelines, and charts. GPS is also a significant modeling tool today.

GPS, or the Global Position System, is increasingly used in archaeology. A model of a research site can be created using computer programs and a series of GPS coordinates. Any artifacts found or important features identified within the site can mapped to their exact locations within this model. This type of mapping is incredibly helpful if further work is warranted, making it possible for the researcher to return to the exact site where the original artifacts were found. These types of models also provide construction companies with an understanding of where the most sensitive cultural sites are located so that they may avoid destroying them. Government agencies and tribal governments are now constructing GPS maps of important cultural sites that include a variety of layers. Layering types of data within a landscape allows researchers to easily sort the available data and focus on what is most relevant to a particular question or task.

Wild food plants, water sources, roads and trails, and even individual trees can be documented and mapped with precision. Archaeologists can create complex layered maps of traditional Native landscapes, with original habitations, trails, and resource locations marked. GPS has significant applications in the re-creation of historic periods. By comparing the placements of buildings at various points in the past, GPS models can be created showing how neighborhoods or even whole cities have changed over time. In addition, layers can be created that contain cultural and historic information. These types of models are an important part of efforts to preserve remaining cultural and historic sites and features.

The Science of Anthropology

Anthropology is a science, and as such, anthropologists follow the scientific method . First, an anthropologist forms a research question based on some phenomenon they have encountered. They then construct a testable hypothesis based on their question. To test their hypothesis, they gather data and information. Information can come from one or many sources and can be either quantitative or qualitative in nature. Part of the evaluation might include statistical analyses of the data. The anthropologist then draws a conclusion. Conclusions are rarely 100 percent positive or 100 percent negative; generally, the results are somewhere on a continuum. Most conclusions to the positive will be stated as “likely” to be true. Scholars may also develop methods of testing and retesting their conclusions to make sure that what they think is true is proven true through various means. When a hypothesis is rigorously tested and the results conform with empirical observations of the world, then a theory is considered “likely to be accurate.” Hypotheses are always subject to being disproven or modified as more information is collected.

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Chapter 1. Anthropology and the Social Sciences
Chapter 2. The Foundations of Social Research
Chapter 3. Preparing for Research
Chapter 4. Research Design: Experiments and Experimental Thinking
Chapter 5. Sampling I: The Basics
Chapter 6. Sampling II: Theory
Chapter 7. Sampling III: Nonprobability Samples and Choosing Informants
Chapter 8. Interviewing I: Unstructured and Semistructured
Chapter 9. Interviewing II: Questionnaires
Chapter 10. Interviewing III: Cultural Domains
Chapter 11. Scales and Scaling
Chapter 12. Participant Observation
Chapter 13. Field Notes and Database Management
Chapter 14. Direct and Indirect Observation
Chapter 15. Introduction to Qualitative and Quantitative Analysis
Chapter 16. Cognitive Anthropology I: Analyzing Cultural Domains
Chapter 17. Cognitive Anthropology II: Decision Modeling, Taxonomies, and Componential Analysis
Chapter 18. Text Analysis I: Interpretive Analysis, Narrative Analysis, Performance Analysis, and Conversation Analysis
Chapter 19. Text Analysis II: Schema Analysis, Grounded Theory, Content Analysis, and Analytic Induction
Chapter 20. Univariate Analysis
Chapter 21. Bivariate Analysis
Chapter 22. Multivariate Analysis.
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Quantitative and Qualitative Research across Cultures and Languages: Cultural Metrics and their Application

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Published: 09 May 2014
Volume 48 , pages 418–434, ( 2014 )

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Wolfgang Wagner 1 , 3 ,
Karolina Hansen 2 &
Nicole Kronberger 1

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Growing globalisation of the world draws attention to cultural differences between people from different countries or from different cultures within the countries. Notwithstanding the diversity of people’s worldviews, current cross-cultural research still faces the challenge of how to avoid ethnocentrism; comparing Western-driven phenomena with like variables across countries without checking their conceptual equivalence clearly is highly problematic. In the present article we argue that simple comparison of measurements (in the quantitative domain) or of semantic interpretations (in the qualitative domain) across cultures easily leads to inadequate results. Questionnaire items or text produced in interviews or via open-ended questions have culturally laden meanings and cannot be mapped onto the same semantic metric. We call the culture-specific space and relationship between variables or meanings a ’cultural metric’, that is a set of notions that are inter-related and that mutually specify each other’s meaning. We illustrate the problems and their possible solutions with examples from quantitative and qualitative research. The suggested methods allow to respect the semantic space of notions in cultures and language groups and the resulting similarities or differences between cultures can be better understood and interpreted.

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importance of quantitative research across fields in anthropology

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Acknowledgment

Writing of this paper by Karolina Hansen was supported by the post-doctoral internship ‘Fuga’ awarded to her by the Polish National Science Centre (DEC-2013/08/S/HS6/00573).

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Wagner, W., Hansen, K. & Kronberger, N. Quantitative and Qualitative Research across Cultures and Languages: Cultural Metrics and their Application. Integr. psych. behav. 48 , 418–434 (2014). https://doi.org/10.1007/s12124-014-9269-z

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DOI : https://doi.org/10.1007/s12124-014-9269-z

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A Quick Guide to Quantitative Research in the Social Sciences

(11 reviews)

Christine Davies, Carmarthen, Wales

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Reviewed by Tiffany Kindratt, Assistant Professor, University of Texas at Arlington on 3/9/24

Comprehensiveness rating: 3 see less

The text provides a brief overview of quantitative research topics that is geared towards research in the fields of education, sociology, business, and nursing. The author acknowledges that the textbook is not a comprehensive resource but offers references to other resources that can be used to deepen the knowledge. The text does not include a glossary or index. The references in the figures for each chapter are not included in the reference section. It would be helpful to include those.

Content Accuracy rating: 4

Overall, the text is accurate. For example, Figure 1 on page 6 provides a clear overview of the research process. It includes general definitions of primary and secondary research. It would be helpful to include more details to explain some of the examples before they are presented. For instance, the example on page 5 was unclear how it pertains to the literature review section.

Relevance/Longevity rating: 4

In general, the text is relevant and up-to-date. The text includes many inferences of moving from qualitative to quantitative analysis. This was surprising to me as a quantitative researcher. The author mentions that moving from a qualitative to quantitative approach should only be done when needed. As a predominantly quantitative researcher, I would not advice those interested in transitioning to using a qualitative approach that qualitative research would enhance their research—not something that should only be done if you have to.

Clarity rating: 4

The text is written in a clear manner. It would be helpful to the reader if there was a description of the tables and figures in the text before they are presented.

Consistency rating: 4

The framework for each chapter and terminology used are consistent.

Modularity rating: 4

The text is clearly divided into sections within each chapter. Overall, the chapters are a similar brief length except for the chapter on data analysis, which is much more comprehensive than others.

Organization/Structure/Flow rating: 4

The topics in the text are presented in a clear and logical order. The order of the text follows the conventional research methodology in social sciences.

Interface rating: 5

I did not encounter any interface issues when reviewing this text. All links worked and there were no distortions of the images or charts that may confuse the reader.

Grammatical Errors rating: 3

There are some grammatical/typographical errors throughout. Of note, for Section 5 in the table of contents. “The” should be capitalized to start the title. In the title for Table 3, the “t” in typical should be capitalized.

Cultural Relevance rating: 4

The examples are culturally relevant. The text is geared towards learners in the UK, but examples are relevant for use in other countries (i.e., United States). I did not see any examples that may be considered culturally insensitive or offensive in any way.

I teach a course on research methods in a Bachelor of Science in Public Health program. I would consider using some of the text, particularly in the analysis chapter to supplement the current textbook in the future.

Reviewed by Finn Bell, Assistant Professor, University of Michigan, Dearborn on 1/3/24

For it being a quick guide and only 26 pages, it is very comprehensive, but it does not include an index or glossary. read more

For it being a quick guide and only 26 pages, it is very comprehensive, but it does not include an index or glossary.

Content Accuracy rating: 5

As far as I can tell, the text is accurate, error-free and unbiased.

Relevance/Longevity rating: 5

This text is up-to-date, and given the content, unlikely to become obsolete any time soon.

Clarity rating: 5

The text is very clear and accessible.

Consistency rating: 5

The text is internally consistent.

Modularity rating: 5

Given how short the text is, it seems unnecessary to divide it into smaller readings, nonetheless, it is clearly labelled such that an instructor could do so.

Organization/Structure/Flow rating: 5

The text is well-organized and brings readers through basic quantitative methods in a logical, clear fashion.

Easy to navigate. Only one table that is split between pages, but not in a way that is confusing.

Grammatical Errors rating: 5

There were no noticeable grammatical errors.

The examples in this book don't give enough information to rate this effectively.

This text is truly a very quick guide at only 26 double-spaced pages. Nonetheless, Davies packs a lot of information on the basics of quantitative research methods into this text, in an engaging way with many examples of the concepts presented. This guide is more of a brief how-to that takes readers as far as how to select statistical tests. While it would be impossible to fully learn quantitative research from such a short text, of course, this resource provides a great introduction, overview, and refresher for program evaluation courses.

Reviewed by Shari Fedorowicz, Adjunct Professor, Bridgewater State University on 12/16/22

Comprehensiveness rating: 5 see less

The text is indeed a quick guide for utilizing quantitative research. Appropriate and effective examples and diagrams were used throughout the text. The author clearly differentiates between use of quantitative and qualitative research providing the reader with the ability to distinguish two terms that frequently get confused. In addition, links and outside resources are provided to deepen the understanding as an option for the reader. The use of these links, coupled with diagrams and examples make this text comprehensive.

The content is mostly accurate. Given that it is a quick guide, the author chose a good selection of which types of research designs to include. However, some are not provided. For example, correlational or cross-correlational research is omitted and is not discussed in Section 3, but is used as a statistical example in the last section.

Examples utilized were appropriate and associated with terms adding value to the learning. The tables that included differentiation between types of statistical tests along with a parametric/nonparametric table were useful and relevant.

The purpose to the text and how to use this guide book is stated clearly and is established up front. The author is also very clear regarding the skill level of the user. Adding to the clarity are the tables with terms, definitions, and examples to help the reader unpack the concepts. The content related to the terms was succinct, direct, and clear. Many times examples or figures were used to supplement the narrative.

The text is consistent throughout from contents to references. Within each section of the text, the introductory paragraph under each section provides a clear understanding regarding what will be discussed in each section. The layout is consistent for each section and easy to follow.

The contents are visible and address each section of the text. A total of seven sections, including a reference section, is in the contents. Each section is outlined by what will be discussed in the contents. In addition, within each section, a heading is provided to direct the reader to the subtopic under each section.

The text is well-organized and segues appropriately. I would have liked to have seen an introductory section giving a narrative overview of what is in each section. This would provide the reader with the ability to get a preliminary glimpse into each upcoming sections and topics that are covered.

The book was easy to navigate and well-organized. Examples are presented in one color, links in another and last, figures and tables. The visuals supplemented the reading and placed appropriately. This provides an opportunity for the reader to unpack the reading by use of visuals and examples.

No significant grammatical errors.

Cultural Relevance rating: 5

The text is not offensive or culturally insensitive. Examples were inclusive of various races, ethnicities, and backgrounds.

This quick guide is a beneficial text to assist in unpacking the learning related to quantitative statistics. I would use this book to complement my instruction and lessons, or use this book as a main text with supplemental statistical problems and formulas. References to statistical programs were appropriate and were useful. The text did exactly what was stated up front in that it is a direct guide to quantitative statistics. It is well-written and to the point with content areas easy to locate by topic.

Reviewed by Sarah Capello, Assistant Professor, Radford University on 1/18/22

The text claims to provide "quick and simple advice on quantitative aspects of research in social sciences," which it does. There is no index or glossary, although vocabulary words are bolded and defined throughout the text. read more

Comprehensiveness rating: 4 see less

The content is mostly accurate. I would have preferred a few nuances to be hashed out a bit further to avoid potential reader confusion or misunderstanding of the concepts presented.

The content is current; however, some of the references cited in the text are outdated. Newer editions of those texts exist.

The text is very accessible and readable for a variety of audiences. Key terms are well-defined.

There are no content discrepancies within the text. The author even uses similarly shaped graphics for recurring purposes throughout the text (e.g., arrow call outs for further reading, rectangle call outs for examples).

The content is chunked nicely by topics and sections. If it were used for a course, it would be easy to assign different sections of the text for homework, etc. without confusing the reader if the instructor chose to present the content in a different order.

The author follows the structure of the research process. The organization of the text is easy to follow and comprehend.

All of the supplementary images (e.g., tables and figures) were beneficial to the reader and enhanced the text.

There are no significant grammatical errors.

I did not find any culturally offensive or insensitive references in the text.

This text does the difficult job of introducing the complicated concepts and processes of quantitative research in a quick and easy reference guide fairly well. I would not depend solely on this text to teach students about quantitative research, but it could be a good jumping off point for those who have no prior knowledge on this subject or those who need a gentle introduction before diving in to more advanced and complex readings of quantitative research methods.

Reviewed by J. Marlie Henry, Adjunct Faculty, University of Saint Francis on 12/9/21

Considering the length of this guide, this does a good job of addressing major areas that typically need to be addressed. There is a contents section. The guide does seem to be organized accordingly with appropriate alignment and logical flow of thought. There is no glossary but, for a guide of this length, a glossary does not seem like it would enhance the guide significantly.

The content is relatively accurate. Expanding the content a bit more or explaining that the methods and designs presented are not entirely inclusive would help. As there are different schools of thought regarding what should/should not be included in terms of these designs and methods, simply bringing attention to that and explaining a bit more would help.

Relevance/Longevity rating: 3

This content needs to be updated. Most of the sources cited are seven or more years old. Even more, it would be helpful to see more currently relevant examples. Some of the source authors such as Andy Field provide very interesting and dynamic instruction in general, but they have much more current information available.

The language used is clear and appropriate. Unnecessary jargon is not used. The intent is clear- to communicate simply in a straightforward manner.

The guide seems to be internally consistent in terms of terminology and framework. There do not seem to be issues in this area. Terminology is internally consistent.

For a guide of this length, the author structured this logically into sections. This guide could be adopted in whole or by section with limited modifications. Courses with fewer than seven modules could also logically group some of the sections.

This guide does present with logical organization. The topics presented are conceptually sequenced in a manner that helps learners build logically on prior conceptualization. This also provides a simple conceptual framework for instructors to guide learners through the process.

Interface rating: 4

The visuals themselves are simple, but they are clear and understandable without distracting the learner. The purpose is clear- that of learning rather than visuals for the sake of visuals. Likewise, navigation is clear and without issues beyond a broken link (the last source noted in the references).

This guide seems to be free of grammatical errors.

It would be interesting to see more cultural integration in a guide of this nature, but the guide is not culturally insensitive or offensive in any way. The language used seems to be consistent with APA's guidelines for unbiased language.

Reviewed by Heng Yu-Ku, Professor, University of Northern Colorado on 5/13/21

The text covers all areas and ideas appropriately and provides practical tables, charts, and examples throughout the text. I would suggest the author also provides a complete research proposal at the end of Section 3 (page 10) and a comprehensive... read more

For the most part, the content is accurate and unbiased. However, the author only includes four types of research designs used on the social sciences that contain quantitative elements: 1. Mixed method, 2) Case study, 3) Quasi-experiment, and 3) Action research. I wonder why the correlational research is not included as another type of quantitative research design as it has been introduced and emphasized in section 6 by the author.

I believe the content is up-to-date and that necessary updates will be relatively easy and straightforward to implement.

The text is easy to read and provides adequate context for any technical terminology used. However, the author could provide more detailed information about estimating the minimum sample size but not just refer the readers to use the online sample calculators at a different website.

The text is internally consistent in terms of terminology and framework. The author provides the right amount of information with additional information or resources for the readers.

The text includes seven sections. Therefore, it is easier for the instructor to allocate or divide the content into different weeks of instruction within the course.

Yes, the topics in the text are presented in a logical and clear fashion. The author provides clear and precise terminologies, summarizes important content in Table or Figure forms, and offers examples in each section for readers to check their understanding.

The interface of the book is consistent and clear, and all the images and charts provided in the book are appropriate. However, I did encounter some navigation problems as a couple of links are not working or requires permission to access those (pages 10 and 27).

No grammatical errors were found.

No culturally incentive or offensive in its language and the examples provided were found.

As the book title stated, this book provides “A Quick Guide to Quantitative Research in Social Science. It offers easy-to-read information and introduces the readers to the research process, such as research questions, research paradigms, research process, research designs, research methods, data collection, data analysis, and data discussion. However, some links are not working or need permissions to access them (pages 10 and 27).

Reviewed by Hsiao-Chin Kuo, Assistant Professor, Northeastern Illinois University on 4/26/21, updated 4/28/21

As a quick guide, it covers basic concepts related to quantitative research. It starts with WHY quantitative research with regard to asking research questions and considering research paradigms, then provides an overview of research design and process, discusses methods, data collection and analysis, and ends with writing a research report. It also identifies its target readers/users as those begins to explore quantitative research. It would be helpful to include more examples for readers/users who are new to quantitative research.

Its content is mostly accurate and no bias given its nature as a quick guide. Yet, it is also quite simplified, such as its explanations of mixed methods, case study, quasi-experimental research, and action research. It provides resources for extended reading, yet more recent works will be helpful.

The book is relevant given its nature as a quick guide. It would be helpful to provide more recent works in its resources for extended reading, such as the section for Survey Research (p. 12). It would also be helpful to include more information to introduce common tools and software for statistical analysis.

The book is written with clear and understandable language. Important terms and concepts are presented with plain explanations and examples. Figures and tables are also presented to support its clarity. For example, Table 4 (p. 20) gives an easy-to-follow overview of different statistical tests.

The framework is very consistent with key points, further explanations, examples, and resources for extended reading. The sample studies are presented following the layout of the content, such as research questions, design and methods, and analysis. These examples help reinforce readers' understanding of these common research elements.

The book is divided into seven chapters. Each chapter clearly discusses an aspect of quantitative research. It can be easily divided into modules for a class or for a theme in a research method class. Chapters are short and provides additional resources for extended reading.

The topics in the chapters are presented in a logical and clear structure. It is easy to follow to a degree. Though, it would be also helpful to include the chapter number and title in the header next to its page number.

The text is easy to navigate. Most of the figures and tables are displayed clearly. Yet, there are several sections with empty space that is a bit confusing in the beginning. Again, it can be helpful to include the chapter number/title next to its page number.

Grammatical Errors rating: 4

No major grammatical errors were found.

There are no cultural insensitivities noted.

Given the nature and purpose of this book, as a quick guide, it provides readers a quick reference for important concepts and terms related to quantitative research. Because this book is quite short (27 pages), it can be used as an overview/preview about quantitative research. Teacher's facilitation/input and extended readings will be needed for a deeper learning and discussion about aspects of quantitative research.

Reviewed by Yang Cheng, Assistant Professor, North Carolina State University on 1/6/21

It covers the most important topics such as research progress, resources, measurement, and analysis of the data. read more

It covers the most important topics such as research progress, resources, measurement, and analysis of the data.

The book accurately describes the types of research methods such as mixed-method, quasi-experiment, and case study. It talks about the research proposal and key differences between statistical analyses as well.

The book pinpointed the significance of running a quantitative research method and its relevance to the field of social science.

The book clearly tells us the differences between types of quantitative methods and the steps of running quantitative research for students.

The book is consistent in terms of terminologies such as research methods or types of statistical analysis.

It addresses the headlines and subheadlines very well and each subheading should be necessary for readers.

The book was organized very well to illustrate the topic of quantitative methods in the field of social science.

The pictures within the book could be further developed to describe the key concepts vividly.

The textbook contains no grammatical errors.

It is not culturally offensive in any way.

Overall, this is a simple and quick guide for this important topic. It should be valuable for undergraduate students who would like to learn more about research methods.

Reviewed by Pierre Lu, Associate Professor, University of Texas Rio Grande Valley on 11/20/20

As a quick guide to quantitative research in social sciences, the text covers most ideas and areas. read more

As a quick guide to quantitative research in social sciences, the text covers most ideas and areas.

Mostly accurate content.

As a quick guide, content is highly relevant.

Succinct and clear.

Internally, the text is consistent in terms of terminology used.

The text is easily and readily divisible into smaller sections that can be used as assignments.

I like that there are examples throughout the book.

Easy to read. No interface/ navigation problems.

No grammatical errors detected.

I am not aware of the culturally insensitive description. After all, this is a methodology book.

I think the book has potential to be adopted as a foundation for quantitative research courses, or as a review in the first weeks in advanced quantitative course.

Reviewed by Sarah Fischer, Assistant Professor, Marymount University on 7/31/20

It is meant to be an overview, but it incredibly condensed and spends almost no time on key elements of statistics (such as what makes research generalizable, or what leads to research NOT being generalizable). read more

Content Accuracy rating: 1

Contains VERY significant errors, such as saying that one can "accept" a hypothesis. (One of the key aspect of hypothesis testing is that one either rejects or fails to reject a hypothesis, but NEVER accepts a hypothesis.)

Very relevant to those experiencing the research process for the first time. However, it is written by someone working in the natural sciences but is a text for social sciences. This does not explain the errors, but does explain why sometimes the author assumes things about the readers ("hail from more subjectivist territory") that are likely not true.

Clarity rating: 3

Some statistical terminology not explained clearly (or accurately), although the author has made attempts to do both.

Very consistently laid out.

Chapters are very short yet also point readers to outside texts for additional information. Easy to follow.

Generally logically organized.

Easy to navigate, images clear. The additional sources included need to linked to.

Minor grammatical and usage errors throughout the text.

Makes efforts to be inclusive.

The idea of this book is strong--short guides like this are needed. However, this book would likely be strengthened by a revision to reduce inaccuracies and improve the definitions and technical explanations of statistical concepts. Since the book is specifically aimed at the social sciences, it would also improve the text to have more examples that are based in the social sciences (rather than the health sciences or the arts).

Reviewed by Michelle Page, Assistant Professor, Worcester State University on 5/30/20

This text is exactly intended to be what it says: A quick guide. A basic outline of quantitative research processes, akin to cliff notes. The content provides only the essentials of a research process and contains key terms. A student or new researcher would not be able to use this as a stand alone guide for quantitative pursuits without having a supplemental text that explains the steps in the process more comprehensively. The introduction does provide this caveat.

Content Accuracy rating: 3

There are no biases or errors that could be distinguished; however, it’s simplicity in content, although accurate for an outline of process, may lack a conveyance of the deeper meanings behind the specific processes explained about qualitative research.

The content is outlined in traditional format to highlight quantitative considerations for formatting research foundational pieces. The resources/references used to point the reader to literature sources can be easily updated with future editions.

The jargon in the text is simple to follow and provides adequate context for its purpose. It is simplified for its intention as a guide which is appropriate.

Each section of the text follows a consistent flow. Explanation of the research content or concept is defined and then a connection to literature is provided to expand the readers understanding of the section’s content. Terminology is consistent with the qualitative process.

As an “outline” and guide, this text can be used to quickly identify the critical parts of the quantitative process. Although each section does not provide deeper content for meaningful use as a stand alone text, it’s utility would be excellent as a reference for a course and can be used as an content guide for specific research courses.

The text’s outline and content are aligned and are in a logical flow in terms of the research considerations for quantitative research.

The only issue that the format was not able to provide was linkable articles. These would have to be cut and pasted into a browser. Functional clickable links in a text are very successful at leading the reader to the supplemental material.

No grammatical errors were noted.

This is a very good outline “guide” to help a new or student researcher to demystify the quantitative process. A successful outline of any process helps to guide work in a logical and systematic way. I think this simple guide is a great adjunct to more substantial research context.

Section 1: What will this resource do for you?
Section 2: Why are you thinking about numbers? A discussion of the research question and paradigms.
Section 3: An overview of the Research Process and Research Designs
Section 4: Quantitative Research Methods
Section 5: the data obtained from quantitative research
Section 6: Analysis of data
Section 7: Discussing your Results

Ancillary Material

About the book.

This resource is intended as an easy-to-use guide for anyone who needs some quick and simple advice on quantitative aspects of research in social sciences, covering subjects such as education, sociology, business, nursing. If you area qualitative researcher who needs to venture into the world of numbers, or a student instructed to undertake a quantitative research project despite a hatred for maths, then this booklet should be a real help.

The booklet was amended in 2022 to take into account previous review comments.

About the Contributors

Christine Davies , Ph.D

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Quantitative Methods

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Dan T. A. Eisenberg

Darryl Holman

Patricia A. Kramer

Melanie Martin

Ben Marwick

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2 What Is Anthropological Research?

What do anthropologists research.

The short answer is pretty much anything related to being human. We can be more specific by looking at each of the four major fields of anthropology:

Cultural anthropology: this is the cornerstone of anthropology. Cultural, or sociocultural, anthropologists study living peoples. They ask questions about how people behave and why they behave that way.

Biological or physical anthropologists also study living peoples but ask different types of questions that are related to the interaction of biology and culture. Disease, death, evolution, primates are all topic within biological anthropology.

Linguistic anthropologists study how people communicate, both verbally and non-verbally. They look at the structure and evolution of language. They also examine what cultural values are reflected in language.

Archaeologists ask the same types of questions that cultural anthropologists ask, but their dataset is different; they look at the material remains of people in the past.

How do anthropologists decide what to research?

This is a good question, and it has a surprisingly simple answer–they research what they find interesting. It is important that you do the same thing when you are assigned a research project for your classes. Even if your professor assigns a topic, you should be able to come up with an angle to that topic that you find interesting. When choosing a topic, start broadly then through the process of research, begin to narrow the focus. The best way to start is to do some general research to learn about the topic. Wikipedia can be a good source for this first step. The articles on Wikipedia can provide you with keywords to help you focus your research. Once you’ve identified keywords, then you can find academic sources to complete your assignment. Royal Road University has more advice on how to develop a research question. Grand Canyon University also has some advice on this.

Science and Anthropology

There has been a long running debate about the scientific nature of anthropology. Science is a way to gain knowledge about natural phenomena using empirical observation and testing, i.e., experiments (Jurmain, et al 2013). While there are different protocols used in science, it is performed using a set of rules called The Scientific Method (Fancher 2000). The method stresses the need to develop a testable hypothesis, the use of objectivity and rationality, and the circularity of scientific research. This does not mean that science is infallible, but in using the scientific method, particularly with the necessity to have testable hypotheses and tests that are replicable by other researchers, rigorous conclusions are reached.

Within the academy, anthropology is designated as a social science. Social sciences are identified as humanistic, ergo, thought of as “soft sciences” because they focus on

…intangibles and relate to the study of human and animal behaviors, interactions, thoughts, and feelings. Soft sciences apply the scientific method to such intangibles, but because of the nature of living beings, it is almost impossible to recreate a soft science experiment with exactitude…the distinction between the two types of science is a matter of how rigorously a hypothesis can be stated, tested, and then accepted or rejected (Helmenstine 2019).

Helmenstine (2019) further states that the designation is outdated as the “degree of difficulty is less related to the discipline than it is to the specific question at hand.” Difficulty may be not in the performance of experiments but in devising an experiment, which may be harder in the so-called soft sciences.

Peregrine, et al. (2014) claim that the differentiation between science (hard sciences) and the humanities (soft sciences) is a false dichotomy. Within anthropology, both hard science and soft science provide important frameworks for understanding the human experience. Hard science helps us make sense of observable phenomena and soft science provides cultural context for those phenomena. As a holistic discipline it is important for anthropologists to use various methodologies and engage with differing perspectives to understand the human experience.

Anthropology cannot succeed without tolerance for this diversity of approaches…our task…is not to seek definitions of scientific or humanistic approaches but, rather, to implement whatever approach satisfies our interests and helps us to answer our questions (Peregrine et.al. 2012, 597).

Misconceptions About Science

There are some common misconceptions about science that may impede one’s ability to fully understand scientific concepts:

Science proves things. Proofs are final and binary, which occurs in mathematics and logic. Science uses evidence gathered over time to develop theories, which are explanations widely accepted based on the current available data. Good scientists accept that theories may change based on new data (Kanazawa 2008).
The scientific method is the only way to do science and it must be done in a lab. While the scientific method is an important tool, it is not the only tool in the researcher’s toolbox. Part of the scientific method as is generally taught in science classrooms is experimentation. It is important to remember that experimentation is only one way to collect data. There are many other ways to collect data, which does not negate utilizing other steps of the scientific method.
Science is boring . Maybe it is sometimes, but science can be fun and creative. Science employs the imagination and since it explores all observable phenomena, there is usually something that garners one’s interest. Sometimes it is simply a matter of finding that something.
Science is hard. Perhaps. The language of science, including mathematics, can be intimidating; however, learning about how to do science is the same as learning how to do anything—it’s a process that we learn over time. Patience is key here—sometimes we need to look at multiple sources to gain an understanding.
Hypotheses and theories are the same thing. Not so. Hypotheses are tentative explanations for a phenomenon. Theories are an accepted explanation based on the testing of hypotheses and well-supported by facts. It can take a long time for a hypothesis to be elevated to the level of scientific theory.
Scientific knowledge is immutable. Again, not so. Scientific knowledge can and does change as new data are collected and analyzed. If scientific knowledge was immutable or unchanging, then there would never be medical or technological advancements. New or changing knowledge is not bad; the success of humans is reliant on our ability to question and be open to new things. We would not be living in the structures we do today or eat the wide range of foods that we do if not for our ancestors’ openness to new knowledge and change.
There is always a right answer. Since scientific knowledge can change, answers may change, or we may not be able to determine the ‘right’ answer. This can be especially hard for some people to understand. This may be because the data suggest multiple answers or we simply do not have the ability to collect the data needed to approach a definitive answer. The trajectory of human evolution is a good example of this. Based on current data and data collection methods, paleoanthropologists have developed several hypotheses about how humans evolved. Because we cannot say which hypothesis is right some people claim that human evolution is false. This is a misunderstanding as to the nature of science. The ‘gray’ area, the area of uncertainty, is not inherently bad. It drives further research, and we need to be open to the fact that sometimes ‘this is what we know right now’ is okay.
Science is anti-religion. While it is true that many scientists are atheists, it is also true that many scientists follow religious belief systems. Scientists prefer to leave religion outside of the science classroom because the subject matter is out of the purview of what they can research because “supernatural explanations are less likely to generate testable claims” (Brickmore et.al. 2009).

Science and Culture

Science does not operate within a vacuum. It operates within cultural systems, which, unsurprisingly, influences the way science is done. Science, as is taught in most schools in industrial nations, started in Renaissance Europe, which itself was heavily influence by knowledge gained from Islamic societies. However, this empirical approach is not the only way to study nature or observable phenomena.

Cultures from all regions of the world have developed a complex view of nature, rooted in their philosophy, which has led to their understanding and explanation of the natural world. The traditional knowledge of non-European cultures is the expression of specific ways of living in the world, of a specific relationship between society and culture, and of a specific approach to the acquisition and construction of knowledge (Iaccarino 2003, 223).

It is beyond the scope of this class to explore all the various ways cultures understand the interaction of culture and environment or to investigate the myriad of ways knowledge is constructed. Because anthropology is embedded within the Western practice of science, that approach is the focus of this chapter.

Society’s Influence on Science

As cultures and societies change to meet new needs, so does science. For instance, national interests influence the trajectory of science. World War II influenced research into atoms, leading to the development of nuclear weapons and nuclear medicine. More recently, the COVID-19 pandemic led researchers to investigate not only a vaccine for the virus, but its origins and epidemiology.

Funding to practice science, e.g., laboratory equipment, salaries, etc., most often comes from social organizations. Both government and private organizations support scientific research. In the United States, the National Science Foundation, National Institute of Health, and the Centers for Disease Control are a few governmental organizations that provide grants for scientific research. The Wenner-Gren Foundation, Tinker Foundation, and Alfred P. Sloan Foundation are examples of private organizations that provide science research grants. Sometimes the organizations direct the focus of research by providing grants for specific types or areas of research, such as The Whitehall Foundation, which provides grants for research in the life sciences, or The Leakey Foundation, which awards grants for research about human origins. While funding agencies can influence what scientists research, it cannot determine the conclusions of the research.

Scientists are people raised within cultures that “…shape our expectations, values, beliefs, and goals” (“Shaping Scientists” 2022). For example, a scientist with a family member who died from cancer may focus on cancer research. On the flip side, beliefs or ethical concerns may turn scientists away from certain avenues of research, as in the case of Joseph Rotblat, a Polish-born scientist who stepped away from the Manhattan Project because he was concerned about the cost to human life (“Shaping Scientists” 2022).

Clearly, these influences could cause bias within scientific research; however, science has a self-checking system that helps to mitigate bias. This system is called the scientific method.

The Scientific Method

Why do students of anthropology need to understand the scientific method? Because it is a key methodology in biological anthropology. It is one of the tools used to help researchers find answers to questions about the human experience.

Steps of the Scientific Meth od

graphic illustrating the scientific method

1. Define the problem

This is based on observation — either something you’ve observed from nature or from something that’s already been written. How many times have you seen something or read about something and thought up a question about it? If you have, then you’ve done the first step of the Scientific Method!

2. Develop Hypothesis

Propose an explanation for the observed phenomenon. For a hypothesis to be a good hypothesis, it must be testable. In other words, anyone must be able to test it to see if it’s supported or unsupported. Even if a hypothesis is unsupported, it is still ‘good’ if it is testable. There are lots of questions out there that are not testable, such as, “Somewhere on the planet, a pink elephant is dancing the can-can in a tutu.” Because it’s impossible to explore every inch of the world looking for our dancing elephant, this would not be feasibly testable. Along the same lines, a testable hypothesis would be, “Student group A, who have read the material on The Scientific Method, will have a higher average score on their quiz than will Student group B, who have not read the material on The Scientific Method.”

3. Collect Data

The experiment should be specifically designed to test the hypothesis. The experiment will provide data as to whether the hypothesis is supported or not. Experiments must be replicable by other researchers. For our example above with ever-so-fortunate quiz-takers, you’d have Group A read the material (Group B would not) and both groups would take the quiz. This experimental design is replicable by other researchers because they too could find two groups of students and follow the same protocol (students are randomly assigned to a group; one group reads the material, one not, and both take the quiz).

4. Organize/analyze data:

Once the experiment is completed, you must organize the data and analyze the results. For our example, you would grade the quizzes, calculate the average score per group, and analyze the results.

5. Conclusion

You develop a statement that sums up what the data (collected during the experimental phase) says about the hypothesis. For our example, our concluding statement would be something like, “Group A, who read material on The Scientific Method, performed better on a Scientific Method quiz than did Group B, who did not read the material.” If your hypothesis is supported, then you would move on to the next step in the process. If your hypothesis was unsupported, you would rework your hypothesis and start the process over.

6. Share the knowledge

It is important to share the results of your work even (and maybe even especially ) if your hypothesis was not supported. Remember — information is only good if it is communicated to others!

Things to keep in mind:

Science does not “prove” anything. Hypotheses are falsified/unsupported or not falsified/supported. For a hypothesis to be accepted as a theory, it undergoes rigorous testing (Larsen 2008: 16). It can take decades for a hypothesis to become a theory. For this reason, it is important to differentiate between theory within the sciences and theory in colloquial use. In the latter, theories are equated to guesses that may or may not be based on any data and are often said to be ‘just a theory.’ It is inaccurate (and we might even say it is insulting) to think a theory in science is “just a theory.” That manner of thought trivializes all the research that went into the development of the scientific theory.

Unfortunately, there is a practice both within and outside the academy, to use the term theory interchangeably with hypothesis. It is up to you to determine if the “theory” referred to is in fact a scientific theory or a hypothesis. While we discuss various topics in anthropology it is important to keep these distinctions in mind. We will encounter situations where there are multiple hypotheses or theories proposed to explain a phenomenon. Contradictory supported hypotheses are possible; some refer to this as equifinality . This is not necessarily a bad thing. It simply means that the data collected supports two (or more) hypotheses — in most cases, there is not sufficient data available to support one more than the other (especially when we get to the information within human evolution). Some who do not fully understand the scientific process, think this is a bad thing, but equifinality drives further research. So, for the moment we must open our minds to accept two or more potential conclusions.

The Scientific Method in Action!!! Watch the following clip and identify the steps of The Scientific Method. Was their hypothesis testable? Was their experiment replicable? What was the conclusion of their “scientific endeavor”? Clip: The Scientific Method in Action .

Data Collection Methods

Like other scientists, anthropologists are looking for patterns, associations, and repetitions (Park 2014, 32). There are five basic categories of data collection methods that anthropologists employ: 1) material observation, 2) biological observation, 3) behavioral observation, 4) direct communication, and 5) participant observation. Which method is used varies depending on the anthropological field and to some extent the research question asked.

From the previous discussion, it should be clear that experimentation is one data collection method employed by anthropologists, which can fall within several of the basic categories mentioned above, including material observation. Data collection through material observation means that the researcher is collecting data about objects and settings. This method is used across all the fields of anthropology. Cultural anthropologists, archaeologists, and primatologists might look at the tools used by the subjects. Biological anthropologists might observe the local environment. Written records could be observed by linguistic anthropologists, cultural anthropologists, and archaeologists (Park 2014).

Biological observation includes observations of human and primate anatomy, including genetics and physiology. Paleoanthropologists and primatologists look at fossils. Archaeologists and cultural anthropologists examine the food that people consumed, and along with biological anthropologists may investigate pathogens that impact people (Park 2014).

Collecting data on how people and non-human primates adapt to their cultural and natural environments is the purview of behavioral observation. One might be tempted to say that behavioral observation is the end-all-be-all for anthropologists, but it is important to remember that it is simply one method of data collection. Most anthropologists employ multiple data collection techniques (Park 2014).

Direct communication is a data collection method that is common in the social sciences. Anthropologists in the field prefer to work with informants , people who supply information about the study group. During conversations with informants, anthropologists learn about emic , or insider, perspectives. Data collected in this manner can help the anthropologist to narrow their research and even develop new research questions. Surveys, questionnaires, interviews, and focus groups are other types of direct communication data collection techniques (Park 2014).

Branislaw Malinowski and Trobriand Islanders

The final type of data collection methods is a cornerstone of anthropology— participant observation . Pioneered by Branislaw Malinowski during his research among the Trobriand Islanders in the early to mid-20 th -century, participant observation requires the researcher to live with the research group to not only observe the group but to participate in activities. Through this participation, the goal is to gather data on the emic perspective to better understand why behaviors are practiced. This method does not preclude the use of other types of data collection. Indeed, using multiple data collection techniques can provide a richer interpretation of human behavior. No matter which data collection technique is employed, it is vital to obtain informed consent from the study participants.

Ethics and Anthropological Research

Informed consent is not only an ethical but legal concern when conducting human subject research. Potential participants must:

be provided with sufficient information to make an informed choice about their participation,
give consent voluntarily, and
be competent to make a decision about involvement in the research (Informed Consent 2018, 1146).

This includes ensuring potential subjects understand any risks and benefits that might be involved through their participation. The researcher needs to make it explicit how the data is going to be used and how privacy is going to be maintained. The subjects also need to be informed that they can withdraw from the study at any time. In other words, researchers need to make full disclosure in order to protect the rights and dignity of their research subjects. For anthropologists, this often entails engaging interpreters to help with either verbally conveying the information or transcribing the details into local languages. Anthropologists often record informed consent so that there are no problems later.

What constitutes “good research”?

Clarity and ethics are two things that comprise good research. The purpose and question(s) need to be clearly defined so that the audience understands. The research process also needs to be planned and explained well. Bouchrika on Research.com lays out the “ Top 10 Qualities of Good Academic Research .” It’s worth a look. George Washington University provides a guide for “ 15 Steps to Good Research .” Much of the information on these two sites relates to original research, but the basic ideas apply to research at the undergraduate level even though the research you conduct will be primarily literature research (also called a literature review or lit search). Most, but not all, undergraduate research projects require you to develop a question and then research what answers other people have developed. This is not a bad thing. In fact, all original research starts with a lit search because we want to see if and what other people have done to address the question. This helps to prevent plagiarism. The Purdue Online Writing Lab has a good overview about writing a literature review . WikiHow has advice on how to do a lit search. If you read nothing else, read that.

What does it mean to conduct ethical research?

Nothing can ruin an academic or research career faster than unethical practices. But what does this mean? While there are discipline-specific ethical standards, there are some general qualities to ethical research (Reznick 2017):

Strive for honesty in all scientific communications. Honestly report data, results, methods and procedures, and publication status. Do not fabricate, falsify, or misrepresent data. Do not deceive colleagues, research sponsors, or the public.

Objectivity

Strive to avoid bias in experimental design, data analysis, data interpretation, peer review, personnel decisions, grant writing, expert testimony, and other aspects of research where objectivity is expected or required. Avoid or minimize bias or self-deception. Disclose personal or financial interests that may affect research.

Keep your promises and agreements; act with sincerity; strive for consistency of thought and action.

Carefulness

Avoid careless errors and negligence; carefully and critically examine your own work and the work of your peers. Keep good records of research activities, such as data collection, research design, and correspondence with agencies or journals.

Share data, results, ideas, tools, resources. Be open to criticism and new ideas.

Respect for Intellectual Property

Honor patents, copyrights, and other forms of intellectual property. Do not use unpublished data, methods, or results without permission. Give proper acknowledgement or credit for all contributions to research. Never plagiarize.

Confidentiality

Protect confidential communications, such as papers or grants submitted for publication, personnel records, trade or military secrets, and patient records.

Responsible Publication

Publish in order to advance research and scholarship, not to just advance your own career. Avoid wasteful and duplicative publication.

Responsible Mentoring

Help to educate, mentor, and advise students. Promote their welfare and allow them to make their own decisions.

Respect for colleagues

Respect your colleagues and treat them fairly.

Social Responsibility

Strive to promote social good and prevent or mitigate social harms through research, public education, and advocacy.

Non-Discrimination

Avoid discrimination against colleagues or students on the basis of sex, race, ethnicity, or other factors not related to scientific competence and integrity.

Maintain and improve your own professional competence and expertise through lifelong education and learning; take steps to promote competence in science as a whole.

Know and obey relevant laws and institutional and governmental policies.

Animal Care

Show proper respect and care for animals when using them in research. Do not conduct unnecessary or poorly designed animal experiments.

Human Subjects Protection

When conducting research on human subjects, minimize harms and risks and maximize benefits; respect human dignity, privacy, and autonomy; take special precautions with vulnerable populations; and strive to distribute the benefits and burdens of research fairly.

Anthropology has its own ethical charter. You can read the complete statement by the American Anthropological Association, but in sum it states that anthropologists must be cognizant of power differentials among all of the players in research, funding agencies, government officials, academic officials, as well as the researcher and subjects. We must carefully weigh the consequences of our decisions. Specifically, anthropologists must:

be open and honest about our work
obtain informed consent and necessary permissions
consider competing ethical considerations and all affected parties
make our results accessible
protect and preserve our records
be respectful and ethical in all professional relationships.

Brickmore, Barry, Amy Ellwein, Dara Dorsey, and Sharon Browning. 2009. “Addressing Science and Religion.” Accessed March 1, 2022. https://serc.carleton.edu/sp/process_of_science/science_religion.html .

Egger, Anne E. 2009. “Misconceptions and Missing Conceptions About the Process of Science.” Accessed March 1, 2022. https://serc.carleton.edu/sp/process_of_science/misconceptions.html .

Fancher, L. J. 2000. The Great “SM.” Accessed May 7, 2015. http://www.cod.edu/people/faculty/fancher/scimeth.htm .

Helmenstine, Anne Marie. 2019. “What Is the Difference Between Hard and Soft Science?” Accessed March 1, 2022. https://www.thoughtco.com/hard-vs-soft-science-3975989 .

Iaccarino, Maurizio. 2003. “Science and Culture.” EMBO Reports 4(3): 220-223. Accessed March 7, 2022. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1315909/ .

“Informed Consent.” 2018. In The SAGE Encyclopedia of Lifespan Human Development , edited by Marc H. Bornstein, 1145-1148. Vol. 3. Thousand Oaks, CA: SAGE Reference. Gale eBooks Accessed March 2, 2022. https://link.gale.com/apps/doc/CX7423500417/GVRL?u=wash_main&sid=bookmark-GVRL&xid=4aeb4d91 .

Jurmain, Robert, Lynn Kilgore, and Wenda Trevathan. 2013. Essentials of P hysical A nthropology , 4th edition. Belmont (CA): Wadsworth, Cengage Learning.

Kanazawa, Satoshi. 2008. “Common Misconceptions About Science I: “Scientific Proof.”” Accessed March 1, 2022. https://www.psychologytoday.com/us/blog/the-scientific-fundamentalist/200811/common-misconceptions-about-science-i-scientific-proof .

Kaziek, C. J. and David Pearson. 2014. “Ask a biologist: using the scientific method to solve mysteries.” Accessed May 7, 2015. http://askabiologist.asu.edu/explore/scientific-method .

Larsen, Clark Spencer. 2008. Our O rigins: D iscovering P hysical A nthropology . New York: W.W Norton & Company, Inc.

“Observation, Participant.” 2008. In International Encyclopedia of the Social Sciences , 2nd ed., edited by William A. Darity, Jr., 14-17. Vol. 1. Detroit, MI: Macmillan Reference USA. Gale eBooks . Accessed March 2, 2022. https://link.gale.com/apps/doc/CX3045301796/GVRL?u=wash_main&sid=bookmark-GVRL&xid=96a38b9d .

Park, Michael Alan. 2014. Introducing Anthropology: An Integrated Approach , 6 th edition.

Peregrine, Peter, Yolanda T. Moses, Alan Goodman, Louise Lamphere, and James Lowe Peacock. 2012. “What Is Science in Anthropology?” American Anthropologist 114(4): 593-597. Accessed February 28, 2022. https://www2.lawrence.edu/fast/PEREGRIP/Publications/Science%20in%20Anth.pdf .

Resnik, David B. 2015. “What Is Ethics in Research & Why Is It Important?” National Institute of Environmental Health Sciences. Accessed November 6, 2017. https://www.niehs.nih.gov/research/resources/bioethics/whatis/index.cfm .

Saneda, Tori. 2015. “The Scientific Method.” Accessed March 1, 2022. https://wikieducator.org/Biological_Anthropology/Unit_1:_Evolutionary_Theory/Scientific_Method .

“Shaping Scientists.” 2022. Understanding Science. University of California Museum of Paleontology. Accessed March 7, 2022. https://undsci.berkeley.edu/article/%3C?%20echo%20$baseURL;%20?%3E_0_0/scienceandsociety_04 .

Welsch, Robert L., Luis A. Vivanco and Agustin Fuentes. 2017. Anthropology: Asking Questions about Human Origins, Diversity, and Culture . New York: Oxford University Press. New York: McGraw-Hill Education.

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Importance of Quantitative Research Across Fields

First of all, research is necessary and valuable in society because, among other things, 1) it is an important tool for building knowledge and facilitating learning; 2) it serves as a means in understanding social and political issues and in increasing public awareness; 3) it helps people succeed in business; 4) it enables us to disprove lies and support truths; and 5) it serves as a means to find, gauge, and seize opportunities, as well as helps in finding solutions to social and health problems (in fact, the discovery of COVID-19 vaccines is a product of research).

Now, quantitative research, as a type of research that explains phenomena according to numerical data which are analyzed by means of mathematically based methods, especially statistics, is very important because it relies on hard facts and numerical data to gain as objective a picture of people’s opinion as possible or an objective understanding of reality. Hence, quantitative research enables us to map out and understand the world in which we live.

In addition, quantitative research is important because it enables us to conduct research on a large scale; it can reveal insights about broader groups of people or the population as a whole; it enables researchers to compare different groups to understand similarities and differences; and it helps businesses understand the size of a new opportunity. As we can see, quantitative research is important across fields and disciplines.

Let me now briefly discuss the importance of quantitative research across fields and disciplines. But for brevity’ sake, the discussion that follows will only focus on the importance of quantitative research in psychology, economics, education, environmental science and sustainability, and business.

First, on the importance of quantitative research in psychology .

We know for a fact that one of the major goals of psychology is to understand all the elements that propel human (as well as animal) behavior. Here, one of the most frequent tasks of psychologists is to represent a series of observations or measurements by a concise and suitable formula. Such a formula may either express a physical hypothesis, or on the other hand be merely empirical, that is, it may enable researchers in the field of psychology to represent by a few well selected constants a wide range of experimental or observational data. In the latter case it serves not only for purposes of interpolation, but frequently suggests new physical concepts or statistical constants. Indeed, quantitative research is very important for this purpose.

It is also important to note that in psychology research, researchers would normally discern cause-effect relationships, such as the study that determines the effect of drugs on teenagers. But cause-effect relationships cannot be elucidated without hard statistical data gathered through observations and empirical research. Hence, again, quantitative research is very important in the field of psychology because it allows researchers to accumulate facts and eventually create theories that allow researchers in psychology to understand human condition and perhaps diminish suffering and allow human race to flourish.

Second, on the importance of quantitative research in economics .

In general perspective, the economists have long used quantitative methods to provide us with theories and explanations on why certain things happen in the market. Through quantitative research too, economists were able to explain why a given economic system behaves the way it does. It is also important to note that the application of quantitative methods, models and the corresponding algorithms helps to make more accurate and efficient research of complex economic phenomena and issues, as well as their interdependence with the aim of making decisions and forecasting future trends of economic aspects and processes.

Third, on the importance of quantitative research in education .

Again, quantitative research deals with the collection of numerical data for some type of analysis. Whether a teacher is trying to assess the average scores on a classroom test, determine a teaching standard that was most commonly missed on the classroom assessment, or if a principal wants to assess the ways the attendance rates correlate with students’ performance on government assessments, quantitative research is more useful and appropriate.

In many cases too, school districts use quantitative data to evaluate teacher effectiveness from a number of measures, including stakeholder perception surveys, students’ performance and growth on standardized government assessments, and percentages on their levels of professionalism. Quantitative research is also good for informing instructional decisions, measuring the effectiveness of the school climate based on survey data issued to teachers and school personnel, and discovering students’ learning preferences.

Fourth, on the importance of quantitative research in Environmental Science and Sustainability.

Addressing environmental problems requires solid evidence to persuade decision makers of the necessity of change. This makes quantitative literacy essential for sustainability professionals to interpret scientific data and implement management procedures. Indeed, with our world facing increasingly complex environmental issues, quantitative techniques reduce the numerous uncertainties by providing a reliable representation of reality, enabling policy makers to proceed toward potential solutions with greater confidence. For this purpose, a wide range of statistical tools and approaches are now available for sustainability scientists to measure environmental indicators and inform responsible policymaking. As we can see, quantitative research is very important in environmental science and sustainability.

But how does quantitative research provide the context for environmental science and sustainability?

Environmental science brings a transdisciplinary systems approach to analyzing sustainability concerns. As the intrinsic concept of sustainability can be interpreted according to diverse values and definitions, quantitative methods based on rigorous scientific research are crucial for establishing an evidence-based consensus on pertinent issues that provide a foundation for meaningful policy implementation.

And fifth, on the importance of quantitative research in business .

As is well known, market research plays a key role in determining the factors that lead to business success. Whether one wants to estimate the size of a potential market or understand the competition for a particular product, it is very important to apply methods that will yield measurable results in conducting a market research assignment. Quantitative research can make this happen by employing data capture methods and statistical analysis. Quantitative market research is used for estimating consumer attitudes and behaviors, market sizing, segmentation and identifying drivers for brand recall and product purchase decisions.

Indeed, quantitative data open a lot of doors for businesses. Regression analysis, simulations, and hypothesis testing are examples of tools that might reveal trends that business leaders might not have noticed otherwise. Business leaders can use this data to identify areas where their company could improve its performance.

20.2 Why Anthropology Matters

Learning outcomes.

By the end of this section, you will be able to:

Explain the characteristics of anthropology that make it uniquely relevant today.
Describe and give an example of anthropological values.
Analyze the importance of anthropological skills.

A Uniquely Relevant Discipline

As you learned in What is Anthropology? , anthropology is a unique discipline. Not only does it study all aspects of what it means to be human across time, with a focus on evolution and how changes occur in our bodies and cultures, but it also examines the ways in which we adapt to different social and physical environments. This process of adaptation is a primary source of cultural and biological diversity. Anthropology is also holistic, examining the context of and interconnections between many parts of our lives and weaving together our biology, our traditions, and the diverse social and physical environments in which we live. The anthropological approach views humans as part of a wider system of meaning, as actors and change-makers within a dynamic environment populated by others. Across cultures, those others can include other species (plant and animal) and spirits as well as other human beings. It is the human ability to imagine and construct the universe in which we live that most interests anthropologists.

In most four-field introductory classes, students are surprised at the breadth of anthropology, but this wide lens is the cornerstone of the discipline. Today, anthropologists increasingly approach the study of humans as a dynamic construct. We see humans as agents in motion, undergoing change as a normal state of being, rather than as objects in a petri dish, preserved and inert. This means that anthropological studies are by necessity messy and in flux, as our subject matter makes change. Because holism, adaptation, and adjustment are critical to anthropological studies, we bring an especially powerful lens to attempts to understand complex, large-scale global problems.

Few of our challenges today are simple. Solving the climate crisis requires changes not just to our use of fossil fuels but also to the ways in which we produce food, bathe, heat and cool our houses, and travel. Each culture and each community must be aware of its power and potential to enact positive change. Both a scientific and a humanistic approach are needed to solve our current global challenges.

Anthropological Values

The anthropological perspective is grounded by principles and standards of behavior considered important to understanding other people and their ways of life. These include the value of all cultures; the value of diversities, biological and cultural; the importance of change over time; and the importance of cultural relativism and acknowledging of the dignity of all human beings. These anthropological values undergird our discipline.

The study of culture intersects with each of the four subfields and highlights the importance of diversity. From the beginning, humans have used ingenuity to tackle problems and provide solutions to challenging circumstances. Anthropologists study and value this extraordinary process of human creativity, documenting it in living and past cultures, in our languages and symbol systems, and even in our bones, through cultural procedures such as elongating women’s necks (as is practiced by the Kayan people of Myanmar) or flattening/elongating people’s heads (practiced by the Chinookan peoples of North America). Even our diets, which are cultural artifacts of adaptation, are written on our bones. The consumption of corn, for example, is measurable as carbon isotopes in human bone. Anthropology celebrates this human uniqueness and diversity, understanding that different ways of being are humanity’s greatest legacy—a foundation embodied in the concept of the ethnosphere.

Anthropological studies produce documentation of immeasurable worth. Through anthropological research, we collect, preserve, and share the stories of living humans as well as human artifacts, sites, and bodies. Together, these documents form a valuable database. Field notes and artifacts from the earliest anthropologists document diversity that has since disappeared. Franz Boas taught his students how to make life masks of the people they were studying to document the physical diversity of different groups of people (A. Singer 1986). This vast collection of some 2,000 life masks is now preserved at the Smithsonian Institution as an archival resource for understanding environment, culture, and biological adaptations. Many masks document ethnic groups that are now extinct. Anthropology collections are of inestimable value for future research.

The Council for the Preservation of Anthropological Records , or CoPAR , works with anthropologists, librarians, and archivists to obtain and preserve anthropological records and make them available both for the study of human diversity and as a record of the history of the discipline. The organization has two primary goals. The first is to educate anthropologists on the value and urgency of saving documents. The second is to help train archivists and information specialists in best practices for handling the sometimes very sensitive information within these documents while also facilitating them in making sure that the information is available to scholars anywhere (Silverman and Parezo 1995).

Diversity is a product of adaptation and change over time. As cultural groups encountered different challenges in their environments, they used ingenuity and innovation to address these challenges, sometimes borrowing other cultures’ solutions when applicable. In the high Andes of South America, the steep mountainous inclines mean that there is little flat ground for growing food. In response to this challenge, Inca farmers used terrace farming , building steplike terraces into the hillside to create areas of flatter surfaces for growing crops (see Figure 20.6 ). Forms of terrace farming are found all over Asia and in parts of Africa, with cultures in each area adapting the use of terraces to meet specific climatic conditions and crop requirements (e.g., paddy rice cultivation requires small earthwork borders to allow for flooding). In short, there is no one way to do something; every solution is calibrated to particular needs. Today, with increasing urgency to minimize our carbon footprints, architects are designing homes to meet clients’ demands for net-positive houses —that is, houses that produce more energy than they consume through solar power and lower-energy appliances (Stamp 2020). As we work toward reducing our dependence on fossil fuels, the architectural and construction industries are beginning to adapt to these changing needs and demands.

Besides culture and diversity, anthropology is also about the human power to change. Through adaption, evolution, and even acclimatization (short-term adaptation to environmental change), the human body has evolved alongside human cultures to make us a species uniquely capable of adapting to almost any environmental or social conditions. Humans can survive even in such inhospitable environments as outer space (thanks to the human-designed technology that makes up the International Space Station) and the polar regions (where human-built structures and protective gear make habitation possible at McMurdo Station in Antarctica). And humans have survived health crises such as the COVID-19 pandemic and historical tragedies such as slavery and warfare. The ability to change, redirect, reassess, reimagine, and innovate has sustained our species across time.

Diversity matters more today than ever. Where diversity is valued, there is greater potential for innovation and collaboration. A central value of anthropology, evident in both research and applied work across communities, is anthropologists’ focus not only on understanding other cultures and different ways of living but also on translating them—that is, communicating what is learned across cultures in order to share it more broadly.

The most important anthropological value, however, is cultural relativism , or suspending judgment about other cultures until one gains a clear understanding of the meaning and significance of what those cultures do and believe. Cultural relativism requires us to understand the rationale, purpose, and meaning of cultural traditions and knowledge before we decide on their validity. And it provides significant advantages in better understanding others:

It allows us to see the worth, dignity, and respect of all persons, allowing for initial exchange and collaboration between “us” and “them.”
It reminds us to approach the study of other cultures without automatically judging them as inferior, thus minimizing ethnocentrism.
It helps us keep an open mind about the potentials and possibilities inherent in our species.

First formally introduced by Franz Boas , cultural relativism laid the groundwork for the discipline of anthropology, a science that would study what it means to be human in all its diverse forms. Boas and his students worked to apply cultural relativism across racial, ethnic, linguistic, and socioeconomic boundaries, documenting the rich cultural traditions of Indigenous peoples, minority communities, and immigrants. The concept, though, has undergone a great deal of debate since the 1948 Universal Declaration of Human Rights by the United Nations. Is anything okay if a culture decides it is? Are there any boundaries to cultural relativism? Do we have to accept everything that a group does, or can an anthropologist ultimately judge that a practice is damaging, harmful, and not deserving of being respected and upheld?

While these debates remain, anthropologists still value cultural relativism (and the worthiness of other peoples and cultures), although perhaps in a modified form that anthropologist Michael Brown calls cultural relativism 2.0 . As Brown states, cultural relativism 2.0 is “a call to pause before judging, to listen before speaking, and to widen one’s views before narrowing them” (2008, 380). In other words, first give people a chance .

Anthropology is important today, perhaps even more than when it formally began some 150 years ago. As French anthropologist Maurice Godelier says:

Anthropology—together with history—is one of the social science disciplines best able to help us understand the complexity of our now globalized world and the nature of the conflicts and crisis we are experiencing. In such a world, it would be irresponsible and indecent for anthropologists [to] stop trying to understand others. (2016, 75–76)

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Importance of Quantitative Research Across Fields
Importance of Quantitative Research Across Different Fields
Importance of Quantitative Research Across Fields
Importance of Quantitative Research across Fields
Importance of Quantitative Research Across Fields

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The Importance of Quantitative Research Across Fields || Practical Research 2 || Quarter 1/3 Week 2
Importance of Quantitative Research Across Fields
Anthropology- Engagement Project
Branches of Biology || Anthropology/Physiology/Ornithology/Physiology/Ophthalmology
Why Do We Need Anthropology To Study Humans? (1/2) || Abdul Haque Chang, Ph.D
LESSON: THE IMPORTANCE OF QUALITATIVE RESEARCH ACCROSS FIELDS OF INQUIRY

COMMENTS

Quantitative Data in Anthropology
In anthropology, quantitative data involves the collection and analysis of numerical information to reveal patterns, trends, correlations, or generalizations about the human condition. This can be gleaned from various sources such as population censuses, health records, economic indicators, or surveys. For instance, an anthropologist studying ...
Importance of Quantitative Research Across Fields
M. Quantitative research uses statistical and mathematical techniques to systematically investigate observable phenomena in natural and social sciences. The document discusses how quantitative research has contributed across various fields like anthropology, communication, medicine, behavioral science, education/psychology, and social science.
2.5 Quantitative and Qualitative Analysis
Anthropology and other fields in the social sciences frequently integrate both types of data by using mixed methods. Through the triangulation of data, anthropologists can use both objective and frequency data (for example, survey results) and subjective data (such as observations) to provide a more holistic understanding.
On Quantification in Social Anthropology
The result has been an increase in quantitative material and statistical analysis in sociology and the other social sciences but a rudimentary development of these methods in social anthropology. The collection of quantitative material in anthropological fieldwork has increased considerably during the past twenty years but its origins go back ...
Anthropology by Data Science
Data science as a field has cultivated abductive, bottom-up forms of quantitative research, which provide useful quantitative parallels to similarly abductive, bottom-up qualitative techniques in ethnographic research. ... Anthropologists should adopt an anthropology by data science perspective through incorporating machine-learning and other ...
2.6: Quantitative and Qualitative Analysis
Qualitative analysis is less about frequency and the number of things and more about a researcher's subjective insights and understandings. Anthropology and other fields in the social sciences frequently integrate both types of data by using mixed methods. Through the triangulation of data, anthropologists can use both objective and frequency ...
The Use of Quantitative Observational Techniques in Anthropology'
Yet quantitative observational techniques have been used in psychology, and especially developmental psychology, since. the '30s. Their revitalization over the last three decades (partly under the influence of ethology) has included recognition of some of the special problems raised by human subjects. 2.
Research methods in anthropology : qualitative and quantitative
Publisher's summary. Research Methods in Anthropology is the standard textbook for methods classes in anthropology. Written in Russ BernardOs unmistakable conversational style, his guide has launched tens of thousands of students into the fieldwork enterprise with a combination of rigorous methodology, wry humor, and commonsense advice.
Chapter 4
As a result of these foci, field research became an integral part of anthropological genetics and provided considerable insight into the understanding of human variation and disease processes. This chapter summarizes the following aspects of field investigations: (1) The reasons for conducting field research in anthropological genetics.
The Use of Quantitative Observational Techniques in Anthropology [and
The measurement of human activity is discussed in relation to anthropological methods and to observational techniques borrowed from the field of animal behavior. When observational techniques are used, a number of sources of bias emerge: choice of subjects, time of day, seasonal, demographic, and spatial effects, and the presence of the observer. Such biases can be circumvented. Use of a ...
20.3 What Anthropologists Can Do
Many of the chapters in this text feature stories about anthropological research and its importance in understanding what it means to be human. Each of the subfields engages in distinct types of field research as ways to test theories and advance our knowledge of human beings. Theoretical research is the backbone of academic anthropology.
Quantitative and Qualitative Research across Cultures and ...
Growing globalisation of the world draws attention to cultural differences between people from different countries or from different cultures within the countries. Notwithstanding the diversity of people's worldviews, current cross-cultural research still faces the challenge of how to avoid ethnocentrism; comparing Western-driven phenomena with like variables across countries without ...
A Quick Guide to Quantitative Research in the Social Sciences
This resource is intended as an easy-to-use guide for anyone who needs some quick and simple advice on quantitative aspects of research in social sciences, covering subjects such as education, sociology, business, nursing. If you area qualitative researcher who needs to venture into the world of numbers, or a student instructed to undertake a quantitative research project despite a hatred for ...
Quantitative Methods
Field Research Methods. Film/Cinema. Food. Geoarchaeology. Geographic Information Systems. Geography. Global Health. ... Quantitative Methods. Queer Studies. Race and Ethnicity. Religion. Reproduction. Research Methods. ... Department of Anthropology University of Washington 314 Denny Hall Box 353100 Seattle, WA 98195-3100.
What Is Anthropological Research?
Data collection through material observation means that the researcher is collecting data about objects and settings. This method is used across all the fields of anthropology. Cultural anthropologists, archaeologists, and primatologists might look at the tools used by the subjects. Biological anthropologists might observe the local environment.
Chapter 3
1. QUANTITATIVE RESEARCH and ANTHROPOLOGY Anthropology is a research method of combining qualitative and quantitative research data. It is concerned with exploring. The Importance of Quantitative Research across Fields. null. What communicative behaviors are used to respond to co-workers displaying emotional stress? (Allen, Titsworth, Hunt ...
Importance of Quantitative Research Across Fields
Importance of Quantitative Research Across Fields. First of all, research is necessary and valuable in society because, among other things, 1) it is an important tool for building knowledge and facilitating learning; 2) it serves as a means in understanding social and political issues and in increasing public awareness; 3) it helps people ...
20.2 Why Anthropology Matters
Introduction; 1.1 The Study of Humanity, or "Anthropology Is Vast"; 1.2 The Four-Field Approach: Four Approaches within the Guiding Narrative; 1.3 Overcoming Ethnocentrism; 1.4 Western Bias in Our Assumptions about Humanity; 1.5 Holism, Anthropology's Distinctive Approach; 1.6 Cross-Cultural Comparison and Cultural Relativism; 1.7 Reaching for an Insider's Point of View
Why Is Quantitative Research Important?
Advantages of Quantitative Research. Quantitative researchers aim to create a general understanding of behavior and other phenomena across different settings and populations. Quantitative studies are often fast, focused, scientific and relatable. 4. The speed and efficiency of the quantitative method are attractive to many researchers.
RES104-B Importance of Quantitative Research across Fields
Study with Quizlet and memorize flashcards containing terms like Anthropology, Communication, Medicine and more. ... Study sets, textbooks, questions. Log in. Sign up. Upgrade to remove ads. Only $35.99/year. RES104-B Importance of Quantitative Research across Fields. Flashcards. Learn. Test. Match. ... Introduction to Quantitative Research. 12 ...
Module 2-Importance of Quantitative Research across
Welcome to the Practical Research 2 Alternative Delivery Mode (ADM) Module on Importance of Quantitative Research Across the Fields. The hand is one of the most symbolized part of the human body. It is often used to depict skill, action and purpose. Through our hands we may learn, create and accomplish.
Importance of Quantitative Research Across Fields
IMPORTANCE OF QUANTITATIVE RESEARCH ACROSS FIELDS - Free download as Powerpoint Presentation (.ppt / .pptx), PDF File (.pdf), Text File (.txt) or view presentation slides online. PR2-Module 2

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Quantitative Data

Overview of Quantitative Data in Anthropology

Methodology

Data Collection Techniques

Sampling Techniques

Data Analysis

Inferential Statistics

Comparisons with Qualitative Data

Limitations of Quantitative Data

Complementary Relationship between Quantitative and Qualitative Data

Summary of the Article’s Main Points

Implications for Anthropology and Future Research

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2.6: Quantitative and Qualitative Analysis

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Quantitative and Qualitative Research across Cultures and Languages: Cultural Metrics and their Application

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A Quick Guide to Quantitative Research in the Social Sciences

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Quantitative Methods

Dan T. A. Eisenberg

Darryl Holman

Patricia A. Kramer

Melanie Martin

Ben Marwick

Related Research

2 What Is Anthropological Research?

How do anthropologists decide what to research?

Science and Anthropology

Misconceptions About Science

Science and Culture

Society’s Influence on Science

The Scientific Method

Steps of the Scientific Meth od

Data Collection Methods

Ethics and Anthropological Research

What constitutes “good research”?

What does it mean to conduct ethical research?

Objectivity

Carefulness

Respect for Intellectual Property

Confidentiality

Responsible Publication

Responsible Mentoring

Respect for colleagues

Social Responsibility

Non-Discrimination

Animal Care

Human Subjects Protection

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Importance of Quantitative Research Across Fields

20.2 Why Anthropology Matters

A Uniquely Relevant Discipline

Anthropological Values