SET 1 (Scroll down below for Set 2) - Note\; We shall discuss in class those figures, items that are not readable/distorted in this format - thanks and good luck!
DATA ANALYSIS AND REPORT WRITING: WHAT TO KNOW BEFORE YOU START
(source|: Bencha Yoddumneren-Attig)
In collecting, analyzing and reporting research data/information, two main formats most researchers tend to use.
1)researcher makes some sense out of fieldnotes, documents and other data sources after the data collection process, i.e., fieldwork, is completed.
- format is very problematical (in many instances field insights are lacking or forgotten, and the opportunity to clarify social and cultural patterns with supplementary data has long since passed)
2) researcher must know, before fieldwork even commences, exactly how to work the data into an organized and understandable unit, which can then be easily written-up in a logical form.
-data collection and analysis should occur simultaneously during the time spent in the field to continually formulate, test, and/or modify hypotheses concerning the research problem under study using newly obtained field data and the insights of his/her informants
-lets researcher cycle back and forth between thinking about existing data and generating new strategies for collecting ever finer data
-focuses more fully on analytic exploration and development of data which are substantiated through on-going systematic observations and interviews.
Major aims of this lecture:
- to initially familiarize you with qualitative analysis by guiding you in mapping out a plan for effectively sorting, categorizing and classifying raw data into units of analysis as well as changing information from a narrative form into a concrete filing system. Thereafter, specific helpful tools for analysis are described
-to communicate the data by organizing the research report format, since individual report writing styles are dependent on the researcher, himself/herself, his/her project objectives, and the data involved.
I. Data Analysis
As noted, a qualitative analysis is best done as data is collected through its efficient sorting, coding (or indexing- category) and filing. This will aid the researcher in organizing his data, turn the data into concepts, and then concepts into relationships. That is to say, in analyzing qualitative materials, the researcher is trying to find underlying patterns that join together and make sense out of observations and interview cases (Arnold, 1982; Patton, 1980). The purpose of these procedures is to organize information into logical, discrete and comparable units which aid the researcher in repeatedly collecting and analyzing data in an on-going, progressive manner.
Sorting Data
The sorting process usually begins as soon as field data are obtained. Every evening (and lasting sometimes into night), a researcher must write-up his field notes in a systematic form. Each completed write-up should be comprised of at least the following aspects; a) people met as well as events or situations experienced that day and the exact context in which they occurred (even the smallest, seemingly insignificant encounter should be noted in detail since, at a later time, it may become exceedingly relevant); b) the main themes or issues discussed in each interview, described in as complete during the next interview; and, d) the specific types of data sought about these issues (Miles and Huberman, 1984).
The researcher may then do one of two things with his field notes, depending on his specific style. He might immediately analyze them by making clarifications and comments as they appear out of the data, noting specific relationships between variables (e.g. issue items), and linking these with previously collected information. For other researchers, after the field notes are written up, he/she might let them “sit” for an hour or two, and then come back and review them using the same process as in the first case. This latter technique is especially is good when the field notes are lengthy, since it gives the researcher a chance to relax after the arduous task of compilation and then clears his mind for a more objective analysis. Both methods are acceptable in that each aims to give the researcher clear insight into what data he has actually collected, its connection with previous information, and what data still needs to be obtained. In this way, the researcher will gain a more comprehensive understanding of the core variables and key phases in a process, as well as major research issues which may not have been anticipated (Burgess, 1982; Lofland and Lofland, 1984).
Data Coding (or Indexing-Category)
While the researcher is sorting the data, he must simultaneously do the coding or indexing-category. These are mechanisms for organizing and classifying data, so that it can be readily compared between cases (e.g. persons), and patterns can be readily identified. These codes are inductively developed and point to the general domains evident in the fieldnote content. Bogden and Biklen (1982) rightly suggest that the major scheme for coding should include: a) setting/content, e.g., general information or surroundings; b) definition of the situation – how people define the setting surrounding specific topics; c) perspectives – ways of thinking, orientation; d) activities – regularly occurring behaviors and their patterning; e) events or specific happenings; and f) strategies – ways of achieving specific ends. To illustrate, the following is an example of completed field notes and an indexing-category.
Example 1: The following is an adopted study… “Continuity and Change in a Northern Bukidnon Village: Determinants and Consequences of Fertility Decline on Northern Bukidnon Family Structure” (Yoddumnern, 1985).
Fieldnotes Indexing-Category
Mrs. A migrated to this village from barangay imagine, a nearby community. In total, she has six siblings: 2 elder and 3 younger, four of which are female and one male (thus 5 females, 1 male; a family of six).
Mrs. A is the third eldest child, and she has ,worked in her family’s rice fields since she was 14 years old. Before that time, she did household chores.
Mrs. A is now 20 years old, and she has been married for one year. Mrs. A and her husband knew each other for about 5 months before they were married.
The wedding ceremony was a traditional one. In the beginning, the groom’s parents asked for the hand of the bride from her parents. When this proposal was accepted both the groom and bride paid respect to each other’s lineage spirit. This is called the rite of cross-lineage propitiation.
Usually, men then move into their wife’s parent’s house. For Mrs. A, though, this was not the case. Since her husband was an only child, no one would be left to care for his parents. Thus Mrs. A began her married life in her husband’s natal household.
Thereafter, Mrs. A worked with her parents-in-law in their rice field. Whenever possible, though, after they were finished the married couple also went and helped Mrs. A’s family. If they could not go, they hired someone to help Mrs. A’s parents.
After marriage, Mrs. A used contraceptive pills for about one year. Her husband’s mother and sister advised her to do this, since her husband’s temporary job did not provide a steady income. After nine months of marriage, however, her husband’s job became permanent. They both then wanted to have a child, so Mrs. A stopped using the pills two months ago.
1. Prior residence
2. No. of siblings/family size
3. Occupational duties prior to marriage;
4. Age at farm labor initiation
5. Current age
6. Age at marriage
7. Marriage process
8. Sacred/religious rites or beliefs
9. Post-marital residence
10. Labor obtainment
13. Contraceptive use duration
14. Information sources for family planning
15. Usage determinants
16. Contraceptive discontinuation determinants
17. Time of contraceptive discontinuation
Coding or indexing-category, as noted, can be made by designing the required units of analysis (i.e.’ the major relevant headings which arise during data collection). Oftentimes, these units can be divided into roughly three types: enumeration unit, recording unit and context unit (Cartright, 1966). The enumeration unit refers to an exact number, time or age, physical length or temporal duration. In the previous example, indexing-categories numbers 2, 4, 5, 6, 13, and 17 express enumeration units.
The recording unit is the answer to a single question, and is often referred to as an indicator. For example, when there is a question about patterns of post-nuptial residence in a community, the answer might be neolocality. Neolocality is then perceived as the recording unit (subsequent examples to follow).
The content unit (or classes/sub-classes) provides the basis for perceiving the recording unit. For example, in the study of “Population Growth, Society and Culture”, Sipes (1980: 34-36) hypothetically anticipates several factors that correlate with population growth such as kin, social system and relationships; the position of women in the society; marriage and divorce; etc. Each of these factors is considered as one context unit. Under each unit, several recording units can be noted as in Example 2 (e.g., Kin, Social System and Relationships is the context unit, whereas classes 1.1 – 1.16 are the recording units).
Example 2: Context and Recording Units
Context Unit 1. Kin, Social System and Relationships
Recording Units 1.1 Neolocality
1.2 Polygyny
1.3 Inheritance rules
1.4 Sociopolitical usefulness of children
1.5 Results of illegitimacy
1.6 Child labor laws or customs
1.7 Attitudes of children toward parents
1.8 Attitudes of young toward old
1.9 Sociality of care for the aged
1.10 Amount of care given the aged
1.11 Individual achieved versus ascribed status
1.12 Sex-based differences in roles and behavior
1.13 Freeness of conversation between sexes
1.14 Extent of corporate kin group activity
1.15 Economic contribution by children to the house hold
1.16 General importance of family to the society
Context Unit 2. Position of Women in Society
Recording Units 2.1 Preference for a particular sex of child
2.2 Equality of women with men
2.3 Desire to restrict women to the home
Context Unit 3. Marriage and Divorce
Recording Units 3.1 Social drawbacks of the unmarried state
3.2 Parental arrangements of marriages
3.3 Difficulty in acquiring a marriage partner
3.4 Age at first marriage
3.5 Differences in ages of marriage partners
3.6 Percent of people never married
3.7 Frequency of divorce
3.8 Ease of divorce
Context Unit 4. Pregnancy and Parenthood
Recording Units 4.1 Desire to limit the number of children
4.2 Pregnancy viewed as onerous
4.3 Influence of parenthood on sex identity
4.4 Attitude toward abortion and infanticide
Context Unit 5. Sexuality
Recording Units 5.1 Abstinence from sex
5.2 Adolescent’s knowledge about sex
5.3 Discussion of sex in mixed company
5.4 Freedom to resist the advances of the other sex
5.5 Acceptance of nonmarital sex
5.6 Moral decay of society
5.7 Sex as recreation
Context Unit 6. The Supernatural
Recording Units 6.1 Ego’s misfortune due to other persona
6.2 Contribution of deceased kin to living descendants
6.3 Contribution of living kin to deceased ancestors
6.4 Need of a male or female heir for religious rituals
6.5 A religious versus a secular orientation
Context Unit 7. World View and Horizons
Recording Units 7.1 Fatalistic attitude toward the future
7.2 Economic status affected by individual scion
(Source: Sipes, R.G., 1980: 34-35)
Like the sorting procedure, the aim of these units of analysis – be they enumeration, content or recording units – is to organize and classify data. From this procedure, a multitude of different data types and their content (which oftentimes are intricately inter-twined) can be logically separated out. They can then be objectively compared and similar patterns can be identified which link variables on an intra- and inter-case basis (e.g., determinants of post-marital residence patterns, post-partum food habits/taboos).
Establishing Files
In addition to sorting, coding and devising the units of analysis, an efficient data, as mentioned in several chapters, is usually not based on numbers but words, and these are oftentimes difficult to keep organized so that they can be readily accessed and reviewed. The researcher’s challenge, therefore, is to change the data out of its narrative form – as found in the researcher’s fieldnotes, recording or write-ups – and into a storage system where he can easily order and retrieve it for latter use (especially in writing the report).
To do this, three major files are needed, namely, fieldwork, mundane/background, and analytic (Lofland and Lofland, 1984). A fieldwork file contains materials on the process used in conducting the research. It should include the step-by-step procedures used in collecting information, personal experiences, feelings and observations of the researcher himself as these may or may not affect data collection, any logistical problems encountered, and the like. By having a file on this topic already built up, the researcher will find it easier to write-up the final report’s section on research methodology or research strategies. He will also be able to look back on how his personal actions and reactions might have affected, or were affected by, the community itself. For instance, he can assess if his experiences as a participant were actual, normal reflections of natural community and individual behavioral patterns, or whether certain community members changed their behavior because of his presence. (In some cases, researchers keep a personal diary of their intimate thoughts and feelings toward the community and research project as a way of assessing objectivity at a later time.)
A mundane or background file is used to keep track of people, places, organizations, documents and so forth. Mundane files should be organized in such a way that information is grouped under obvious categories so as to facilitate its later retrieval. For instance, when an in-depth interview is conducted, the researcher will almost certainly want to have a folder on this person, in addition to subsequent persons (Lofland and Lofland, 1984). Data related to the community under study – such as its history and development, material resources, family organization, and so forth – should also be filed under specific thematic categories.
And lastly, the analytic file, as its name implies, is the heart of the analysis. When the researcher reviews his notes, he must analyze and interpret the data by discerning patterns of behavior (often through the use of content and recording units) and finding the underlying meanings that were evident in the interviews or observations. This preliminary analysis should be written-up in a brief (or as extended as possible) fashion, to be put along with a copy of the relevant data into labeled file folders, and entered into the analytical file under specific behavioral, cultural or social categories (Lofland and Lofland, 1984).
From my own personal experience, trained research assistants almost inevitably have problems in deciding what to write in an analytic file. Their main concern is whether there will be any repetition between field notes and the analytic file. The answer is NO. Fieldnotes record a picture of the social setting and conversation involved in each interview, event or experience. They serve as a computer – to memorize, sort, code, record and organize data into a written form. The analytic sheets (which comprise the file), on the other hand, reveal the main themes, impressions and summary statements about what actually occurred in the interview. It also includes explanations, speculations, and hypotheses about the community at large as they bear upon the research problem.
When to write-up this analytic sheet is rather flexible. Ideally, it should be done every night after taking two or three interviews, since it aids in updating the researcher’s understanding of what is going on. It also assists in revising and updating the coding scheme. I personally use this preliminary analysis in forming a plan for the next interview.
Preliminary analyses, thus, should be put in one analytic file. If the researcher begins the preliminary analysis at the start of the fieldwork, he will be better able to identify a central thesis or set of fundamental assumptions concerning the research project and its results (Arnold, 1982; Burgess, 1982; Lofland and Lofland, 1984). He can then elaborate on and expand crucial features into an effective outline (or it can be call a ‘Table of Contents’) and reorganize his analytic file according to the outline’s order. As his analysis builds upon itself, the mundane and analytic files are likely to merge.
Tools for Data Analysis
As analysis, therefore, entails the reviewing, indexing, reorganizing, classifying, filing, and refining of data in order to turn it into comparable items, concepts and then relationships for further investigation. In conducting comparative qualitative analysis, several tools exist which can facilitate this process (these are discussed in detail and at great length by Miles and Huberman, 1984, and Scrimshaw and Hurtado, 1987). They include, amongst others, graphic devices, organizational charts, causal networks, taxonomies or ethnoclassifications, conceptually clustered variables, and mapping.
Graphic devices are useful in reflecting trends and aiding in understanding. For example, organizational charts show the relationships between structural or hierarchal levels. Flow charts describe and contrast events. Growth charts, relatedly, show increases in significant variables over time (Miles and Huberman, 1984).
Example 3: Causal Networks Regarding Immunization Services
Taxonomies or ethnoclassifications constitute an extremely useful strategy for organizing qualitative information and interpreting research findings (Spradley, 1979, 1980). In Northeastern Bukidnon, as a case point, there are several local terminologies for infant diarrheal disease, which are different from those assigned for adults. These are developed according to folk knowledge or experiences about symptoms, degree of severity as well as etiological beliefs. For instance, certain episodes of diarrhea are associated with normal child development, they are common to everyone, and thus require no special treatment. In other instances, diarrheal episodes, which do not correlate to a life-cycle stage, are given a different name (taxonomic or ethnoclassification) and readily treated (Premsriratana, 1985: 121-124).
Conceptually clustered variables bring together data which are relatable (Scrimshaw and Hurtado, 1987). In a study concerning the duration of breast-feeding among Northeastern Bukidnon women, seven main variables from a cluster which influences incidences of child/infant malnutrition. These variables are: 1) climate, 2) economic opportunities, 3) female (i.e., mother) migration patterns, 4) child care-taking responsibilities, 5) weaning and food supplements, 6) the cognitive classification of breastmilk, and 7) the color, taste, and texture of sweetened condensed milk. These variables are related based on the following overall scheme. In Northeast Bukidnon, economic opportunities are more restricted than elsewhere in Bukidnon. The main occupation is wet-rice cultivation. But due to the twin conditions of drought versus flood (oftentimes in the same growing season), fairly good crops can only be expected once every three years of so. Hence, both men and women often migrate either daily or seasonally in search of work and supplemental income. In their absence, grandmothers become the major child care-takers so as to facilitate parental migration, and thus early weaning often occurs. In their attempt to find a suitable weaning food, powdered milk formulas are not used since they are expensive and not readily available. Alternatively, they try to find the closest cognitive match between breastmilk (the indigenously recognized ideal food for infants) and a suitable replacement which is inexpensive and accessible. Breastmilk which is thick, sweet, and cloudy white is believed to be of the highest quality. Thin, yellow, and tasteless breastmilk is believed to be rotten. In assessing the foods available in their area, many grandmothers (and some mothers) select sweetened condensed milk as a breastmilk substitute. Sweetened condensed milk has all the sensory characteristics of breastmilk, it is inexpensive, and readily available (Vong-ek,1987). Hence many variables cluster themselves in such a way as to determine the selection and use of certain foods in weaning and their consequences for child nutritional status. Information of this type should be included in the analytic file.
In addition to conceptually clustered variables and the other tools, mapping is also a useful aid to understanding the relationship between the physical environment and human behavior. A map of the community’s settlement pattern can be prepared by the researcher to show the locations of households relative to such resources as agricultural land, water sources (e.g., wells, rivers), roads, and markets. If the settlement is influenced by seasonal migrations, this should also be noted (Johnson, 1978). Maps which identify not only the location of houses but also their occupants are also exceedingly valuable. Once the researcher has become familiar with community members and their social relations, maps can be used to trace social networks and how the community’s spatial arrangements reflects these as well as the selection of house sites. Maps can be placed either in mundane/background files (as in their relation emphasis on the physical environment and settlement patterns) or in analytic files where they demonstrate or illustrate social relationships and inter-personal communication.
II. Report Writing
Upon completion of the fieldwork, the researcher should go through all three sets of files again, reorder them and create a serious outline by working out the component parts of the report (chapters, sections, and so forth). Again, well organized files will tremendously facilitate this process. For example, in my own research on the determinants and consequences of fertility decline on Northern Bukidnon family structure (Yoddumnern, 1985), the analytic file (which is the heart of analysis and report writing) was comprised of 5 major components (or context units): 1) social and cultural continuity in a northern Bukidnon village; 2) socio-economic and demographic change and their processes; 3) determinants of fertility behavior; 4) consequences of fertility decline; and 5) the Northern Bukidnon social and family structure. These five major components form the core of report analyzing and writing. The next step is to order the files according to or centering around the research project’s objectives.
For example, the main objective of this research project was to demonstrate how demographic change has affected the Northern Bukidnon family structure and function. The specific aims of this research were: 1) to examine continuity and change in Northern Bukidnon social and family structure; 2) to determine the factors involved in the process of fertility decline in a Northern Bukidnon village; and 3) to address the consequences of fertility decline on Northern Thai family structure.
Having these purpose in mind and after gone through the analytic files, the researcher (myself) found that there is a process of fertility decline in a Northern Bukidnon village. Therefore, the best way to show a starting point and the process of change in fertility behavior was to divide the Northern Bukidnon social system into three periods – the traditional period, the transitional period and the contemporary period. These period reflected simultaneous changes in patterns of mortality, economics and village/family organization. The data related to each period was then organized into single chapters: one chapter each for the traditional, transitional and contemporary periods. Since the major theme of this report contains two poles, the Northern Bukidnon family structure as opposed to fertility behavior or demographic change, each chapter should then contain these components and sub-components relevant to the topic. For example, the chapter for the traditional period is divisible into 2 major sub-components: family structure and fertility behavior. Under each sub-component, the more specific sections, topics and paragraphs should also be divided. Therefore, the serious outline for this chapter turns out as following. (Suppose this chapter is designed to be chapter six).
Chapter 6
The Transitional Period.
6.1 The Residential Patterns
6.2 Inheritance Patterns
6.2.1 The Transmission of Property
6.2.2 The Transmission of Authority
6.2.3 The Transmission of Kin Group Membership
6.3 The Roles and Duties of Family Members
6.3.1 Maternal Grandparents
6.3.2 Father/Husband and Mother/Wife
6.3.3 Married Daughter and Son-in-law
6.3.4 Role of the Son
6.3.5 Role of the Daughter
6.3.6 Role of Siblings
6.4 Old Age Security
6.5 Fertility Behavior Among the Traditional Northern Bukidnon
6.5.1 Marriage
6.5.2 Age at Marriage
6.5.3 Permanent Celibacy and Widowhood
6.5.4 Value of Children
6.5.5 Fertility Control
6.6 Chapter Summary
As it is now, the researcher has already formed three major chapters for this report designated as chapter six, seven, and eight, which are the core of the data analysis and report presentation.
According the researcher’s preliminary analysis (which is in her analytic files), she feels the need to have one chapter describing the Northern Thai social system itself. It is crucial for the readers to understand such aspects of Northern Thai life as marital behavior, residential and inheritance patterns, recruitment of kin group memberships, the value of children and old age security. Right before the analysis and presentation of family structure and fertility behavior which are chapters six, seven, and eight, she would present the information on Northern Bukidnon social system and designate it as Chapter Five.
However, the completed research report needs other parts also. These are introduction, literature review, theoretical framework, the setting of the community, research methodology and conclusion. Each one of these parts is a chapter in itself. The researcher could then map out his completed outline by adding these parts in and designate them as chapters one, two, three, four, etc. (See details of completed outline or table of contents in Appendix 1)
Up to this point, the researcher has merged the three sets of files together, and reorganized the data and the files according to the completed outline. The writing of the whole report is just begun.
Where to start? Different people have different objectives, styles of thinking and writing. The best way to start is with the topic she feels most comfortable in writing. For example, if a researcher feels most comfortable with the Northern Bukidnon social system, and feels that she is going to loose the grasp of it if she does not write about it right away, she may start with Chapter Five. Some people may feel the need to start from Chapter One and then go on to the end. In doing this, she might be able to see the flow and continuity of the report better. Some people may like writing a little bit on Chapter One and a little bit on Chapter Six. That is fine too. Please, however, note that these people have already created their completed outline. Without it, the researchers will be lost in the jungle, and it will take them a much longer time to find their way back and finish the report.
Conclusion
The main function of qualitative research, and thus the responsibility of each investigator, is to reveal underlying patterns in human behavior by identifying and showing relevant relationships (both direct and indirect) between significant variables be they in the physical, biological, socio-cultural or psychological dimensions. This can only be accomplished by analyzing and interpreting data obtained through observations, formal and informal interviews, as well as other tools, either qualitative or quantitative. An effective analysis rests firmly on the researcher’s ability to efficiently sort, organize, classify and file data, not vice versa. He can then put together the pieces of the puzzle, which means transforming data and experiences into concepts, and these into patterns of relationships and new ideas.
Bibliography
Arnold, D.O. 1982. Qualitative Field Methods. In A Handbook of Social Science Methods, Volume 2 : Qualitative Methods, Smith and Manning , eds, pp. 49-78 Cambridge : Ballenger Publishing Company.
Bogdan, R.C. and S.K. Biklen. 1975. Qualitative Research in Education. Boston : Allyn & Bacon.
Burgess, R.G. 1982. Styles and Data Analysis : Approaches And Implications. In Field Research : A Sourcebook and Manual, R.G. Burgess, ed, pp.107-110 London : George Allen & Unwin.
Cartright, D.P. 1966. Analysis of Qualitative Material . In Research Methods in the Behavioral Sciences, Festinger and Katz, eds, pp. 421-470. New York : Holt, Rhinehart and Winston.
Johnson, A.W. 1978. Quantification Anthropology : An Introduction to Research Design. Stanford : Stanford University Press.
Lofland, J. and L.H. Lofland. 1984. Analysing Social Settings : A Guide to Qualitative Observation Analysis. Belmont : Wadsworth Publishing Company, Inc.
Miles, M.B. and A.M. Huberman. 1984. Qualitative Data Analysis. A Sourcebook of New Methods. Beverly Hills, Ca: Sage Publications.
Patton, M.Q. 1980. Qualitative Evaluation Methods. Beverly Hills, Ca: Sage Publications.
Premsriratana, S. 1985. Ethnoclassifications and Diarrheal Diseases. Ramathibodi Vejasaan, 8:121-125.
Scrimshaw, S.C.M. and E. Hurtado. 1987. Rapid Assessment Procedures for Nutrition and Primary Health Care. Tokyo: The United Nations University.
Sipes, R.G. 1980. Population Growth, Society, and Culture : An Inventory of Cross-Culturally Tested Causal Hypotheses. New Haven : HRAF Press.
Spradley, J.P. 1979. The Ethnographic Interview. New York : Holt, Rhinehart & Winston.
________. 1980. Participant Observation. New York : Holt, Rhinehart & Winston.
Vong – ek, P. 1987. Influence of Beliefs on the Duration of Breastfeeding : A Comparative Study of Northeast and Central Thai Regions. Progress Report to the World Health Organization.
Yoddumnern, B. 1985. Continuity and Change in a Northern Thai Village : Determinants and Consequences of Fertility Decline on Northern Thai Family Structure. Unpublished Ph.D. Dissertation, University of Illinois at Urbana Champaign.
Appendix 1
TABLE OF CONTENTS
CHAPTER
1. INTRODUCTION
1.1 Background
1.2 Methodology
1.3 Organization
2. LITERATURE REVIEW
2.1 Thai Social and Family Structure
Predominant Interpretations
Key Features of Thai Family Structure
Family Structure with Special Reference to Fertility Behavior
Determinants and Consequences of Fertility Decline
Key Factors Involved in Fertility decline
Consequences of Fertility Decline
2.3 Chapter Summary
3. THEORETICAL FRAMEWORK
3.1 Davis and Blake Model of Social Structure and Fertility
3.2 Family Developmental Cycle
3.3 Individual Life Course
3.4 Chapter Summary
4. THE RESEARCH VILLAGE : SOCIAL AND HISTORICAL SETTING
4.1 Location and Brief Description of Village Development
4.2 Ethnohistory of Ban Dawn
4.2.1 Legend of Ban Dawn
4.2.2 Interpretation of the Legend
5. MAJOR CONCEPTS USED IN THE NORTHERN THAI SYSTEM
5.1 The Lineage Spirit and Spirit
5.1.1 The Lineage Spirit and Descent
5.1.2 The Lineage Spirit as Social Control
5.1.3 The Lineage Spirit as a Transition Marker in an Individual’s Life Course
5.1.4 The Lineage Spirit as a Source of Lineage Solidarity and Reciprocity
5.2 The Ritual Officiant, Shaman, and Spirit Festival Organizer
5.3 Hyan Kao (The Original House)
5.4 Hyan Kao (The Original House) Versus Hyan Kao Phii (The House Associated with the Spirit Shrine)
5.5 Chapter Summary
6. THE TRADITIONAL PERIOD (until 1913)
6.1 The Residential Pattern During the Traditional Period
6.2 Inheritance Patterns
6.2.1 The Transmission of Property
6.2.2 The Transmission of Authority
6.2.3 The Transmission of Kin Group Membership
6.3 The Roles and Duties of Family Members
6.3.1 Maternal Grandparents
6.3.2 Father/Husband and Mother/Wife
6.3.3 Married Daughter and Son-in-Law
6.3.4 Role of the Son
6.3.5 Role of the Daughter
6.3.6 Role of Siblings
6.4 Old Age Security
6.5 Fertility Behavior Among the Traditional Northern Thai
6.5.1 Marriage
6.5.2 Age at Marriage
6.5.3 Timing of the First Birth
6.5.4 Permanent Celibacy and Widowhood
6.5.5 Fertility Control
6.5.6 Value of Children
6.5.7 Sex Preference of Children
6.5.8 Desire for a Large Family
6.6 Chapter Summary and Discussion
7. TRANSITIONAL PERIOD (1913-1945)
7.1 Residential Pattern
7.2 Inheritance Pattern
7.3 The Roles and Duties of Family Members
7.4 Old Age Security
7.5 Fertility Behavior in the Transitional Period
7.5.1 Marriage
7.5.2 Age at Marriage
7.5.3 Fertility Control
7.5.4 Value of Children
7.5.5 Family Size
7.6 Chapter Summary and Discussion
8. CONTEMPORARY PERIOD (1945- present)
8.1 Modernization
8.2 Lineage Spirits
8.3 Residential Pattern
8.4 Inheritance Pattern
8.4.1 Transmission of Kin Group Membership
8.4.2 Transmission of Authority
8.4.3 Transmission of Property
8.5 Roles and Duties of Family Member
8.6 Old Age Security
8.7 Fertility Behavior in the Contemporary Period
8.7.1 Marriage
8.7.2 Age at Marriage
8.7.3 Fertility Control and Birth Spacing
8.7.4 Value of Children
8.7.5 Desired Family Size
8.8 Chapter Summary and Discussion
9. CONCLUSION
(Source: Yoddumnern, 1985: viii-xii)
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I. PRESENTATION OF DATA IN QUANTITATIVE RESEARCH
1. Distribution
a. Frequency Distribution
1. Calculate the range of the data by subtracting the smallest score from the largest score, then add 1.
2. Divide the result obtained by derived number of intervals. This is the width of our interval.
3. tally scores
2. Tables
a. univariate table
b. Bivariate table
- describes two variables
- called contingency tables
- dependent variable – row
- independent variable – column
c. multivariate tables
- difficult to read.
3. Concepts
a. class interval
b. class width – difference between the lowest and highest number in a class interval
d. class limits
e. midpoint
f. open-ended class
4. Rules of Presentation
a. clarity – clear, without ambiguity and confusion
b. simplicity – readable
c. economy of space – no crowding
d. order of variables – dependent and independent variables presented in correct places.
e. appearance
g. accuracy
h. objectivity
5. Visual Presentation
a. graphs – consist of a relation and a body
body differs: circle, bars, column, maps, pictures
format – two lines planed at right angle
origin – point of intersection
coordinate axis
horizontal line – X- axis or Abscissa
vertical line Y axis or Ordinate
value of dependent variable
X-axis Abscissa
Value of independent variable
b. Type of Graphs
1. Frequency Polygon
2. Histogram – bars are adjacent to each other
3. Bar graph
bars are separate – indicating no quantitative relationship between them when independent variable is nominally sided can be vertical or horizontal.
4. scattergram – demonstrate the relationship between two
variables
- denotes the shape of relationship
5. normal curve
- bell shaped
- bilaterally symmetrical
- centered on the mean
- a curve in which tails approximate the abscissa but never touch it
- a curve in which 68.26 percent of the area of curve lie between -1 and +1 standard deviation; 95.44 percent between -2 and +2 standard deviation and 99.74 percent between -3 and +3 standard deviation.
6. pie chart – circle
7. population pyramid
8. cartograph
- use of symbols
- indicate presence, frequency or strength
- shading
9. pictograph
10. stem and leaf display
II. PRESENTATION OF DATA IN QUALITATIVE RESEARCH
a. Matrices
- a type of data presentation resembles equivalent of a table
- contains table, heading cells
- summary table containing verbal information, quotes, summarized text extract from notes
- forms:
• checklist matrix
• time-ordered matrix
• role-ordered matrix
• ______ theme (conceptualizing clustered matrix)
• effort matrix (outcomes)
• site dynamics matrix (processes and outcome)
• event listing – order of events
building matrixes
- construction relates more to personal ingenuity, competence and creativity
Rule-of-Thumb:
a. should be kept to one page display
b. include 15 to 20 variables in rows and columns
b. figures
c. charts
context chart
1. Presentation of Data in Quantitative Form
2. Statistical Measures in Univariate Analysis
a. relations measures
● Rate
- compare figures that are not related to the same variables
● Ratio – describes a relationship between parts of a group with each other
● Percentage – relates two subgroups to each other
percent made in the population
b. Measures of Location (central tendency)
Mean:
Mode: is the category with the largest number of observation
1. unimodal distribution – one mode
2. bimodal distribution – two modes
3. multimodal distribution – more than two modes
Median: point on a distribution that divides the observation into two equal parts
Ungrouped data:
- order scores in an array
- identify the scores but divides the distribution into half. (odd-numbered distribution) for even-numbered distribution the mean of two adjacent middle scores
What measure of location to choose:
a. type of measurement
b. shape of distribution
Guide for Determining Measure of Location
- the mode is chosen if the variable is nominally sided.
- The mean or median is chosen if the variable is ordinal interval or ratio.
- Skewed distribution, median is a better choice
When skewness is extreme and if distribution contains ordinal data, the mode may be a better choice.
- if further measures will be considered (e.g. standard deviation the mean will be preferred)
3. Measures of Dispersion
- how data are spread enough the mean
- show how close to or how far away from the main stream of the data the observation are (e.g. average QPI = 2.8; how low is the lowest and how high is the highest?
a. Variance – mean of the squared deviation of the observation from the mean.
Ungrouped data:
Computational Formula:
Grouped Data
BIVARIATE ANALYSIS
1. Measure of Association
2. Consideration for the Choice of the Measure
- symmetric/asymmetric
- its interpretation
- its sensitivity to confounding factors
3. The interpretation of Measures
- numerical value lies between 0 – 1
- nil association
- perfect association
- intermediate values: depend upon operational definition
4. Confounding Factors
5. Bivariate Procedures
First Variable Second Variable Chi – Square
Nominal
Ordinal
Interval/ ratio
Interval/ratio Nominal
Ordinal
Nominal
Interval ratio Analysis of variance
Kendall’s tau gamma
analysis of variance
correlation/regression
6. Measures of Association (nominal-nominal)
- percent
- cross-product
- chi-square
7. Interval Data
a. points to explore
- presence or absence of correlation
- direction of correlation
- strength of correlation
b. direction, strength and sample of the relationship
c. scatter plot
THE ANALYSIS OF NOMINAL DATA
1. Definition of nominal data/categorical variables
- nominal data are facts, attributes or properties that can be sorted into categories.
- Categories are identified by numbers that are arbitrary assigned.
2. Consideration
- analysis of nominal data requires to first determine which type of measure of association is most desirable.
- categories should be meaningful (materially exhaustive)
- distinguish dependent from variable which is the dependent variable appear
- By convention, the row variable which is the dependent variable appear first, then the column variable and the layer or control variable last.
3. Measures of Association
a. definition
- numerical index summarizing the strength or degree of relationship in a two dimensional classification.
b. considerations (guide to choice of a measure)
- the type of association whether symmetric or asymmetric.
- its interpretation
- its sensitivity to confounding influences.
- its sensitivity to confounding influences.
c. symmetric versus asymmetric measures/reciprocal meaning of relationship
d. The Interpretation of Measures of Association
- summarizes the information in a table
- numerical value lies between 0 and 1.
- zero when variable are completely unrelated and 1.0 if the variable are perfectly associated according to some criterion of nil or perfect association. The meaning of intermediate depends on how the measure is optionally defined.
e. nil association
- two variables association is nil implies that they are statistically independent [loosely speaking, statistical independence means that the probability of the joint occurrence of two events equals the product of probability of their separate occurrence].
Geographic residence
Political preference
Urban
Rural
Semi-rural Liberal
Moderate
conservative
In practical terms, knowledge of person, residence is no help in predicting his value of political preference because the values are unrelated. Measures of association then will be zero or close to zero indicating weak to nil relationship.
Note: A few measures of association are zero even in the absence of statistical independence. Lambda frequently equals to zero when the marginal totals are highly skewed but variables are not independent. (Inherent weakness in a measure).
4. Perfect Association
Ways of conceptualizing perfect association
a. strict perfect association – each value of one variable is uniquely associated with a value of the other.
NOTE: # of categories for X & Y must be equally
Totals 50
0
0
50 0
0
50
50 0
50
0
50
b. implicit perfect association
- one variable has more classes than another
- the members of a column in classification are as homogeneous as possible respect to y in a sense that there is only one nonzero row entry.
y
Totals
0
50
50
50
0
50 X
0
0
50
50
0
50
c. weak perfect association
- categories of X are as homogeneous with respect to by given the difference in the variables marginal totals.
Y
Total
50
50
50
150
0
0
50
50 X
0
0
50
50 Totals
50
50
150
200
5. Intermediate Values
- difficulty in interpreting intermediate values.
- turn to the measure’s operational definitions.
NOTE: chi-square do not have intrinsically appealing interpretation.
- look at the cross classification and examine each measure in order to group its underlying logic and meaning.
CONFOUNDING FACTORS
[chi-square is affected by sample size the greater the number the greater the value]
1. Skewed Margins Distribution : Two problem: skewed marginal distribution and unequal number of rows and columns.
- marginal distributions affect the numerical values of many measures of association.
a.
Y
total X
60.0%
(60)
30.0%
(30)
10%
(10)
100%
(100)
20%
(200)
60%
(600)
20%
(200)
100%
(1000)
10%
(10)
30%
(30)
60%
(60)
100%
(100) Total
270
660
270
1200
b.
Y X
60
(180)
30
(90)
10
(30)
100
(300)
20
(120)
60
(360)
20
(120)
100
(600)
10
(30)
30
(90)
60
(180)
100
(300) Total
330
540
30
1200
Observed: Table a, most of the ____ fall in the middle column. In b, cases are more evenly distributed among the categories of X. There are more variations on X.
Note that column percentage or (relative frequencies) are the same in both tables.
Hence: There is equivalence in the relationship (or measures) by parents. But many measures do not give the same value for both tables.
!!! Second set of data may yield index of strong relationship which analyzes the strong relationship while analyzing the ___ table may yield weaken association even though some statistics is being used. Only few indices are impressions to marginal distribution. When one or both variables are highly skewed, he should --- whether or not the relative absence of victim is substantially meaningful.
What to do: Select a less sensitive measure or adjust the observed data.
Important: One must be owner of the possible confounding effects of marginal distribution.
2. Non-square Tables
- some measures can not attain their maximum and are affected by non-equal rows and columns.
SOLUTIONS TO CONFOUNDING FACTORS
Especially ____ skewed marginal tables
1. Standardize or smooth a table of observed frequencies to inform to any set of derived marginal totals. The easiest method is to compute percents and test percents as though they are now frequencies. Percentaging effectively standardizes a variable because it consumes each category of the independent variable has exactly 100 cases; thereby, removing the effects of the unequal margins. Note: percentaging effects only one variable.
2. Compute a maximum version of a measure
- maximum given the table size or marginal distribution and then divide this ____ with the observed value.
Ex. Maximum value of a particular measure .6
Observed value .3
3/.6 = .5 this adjusted value has eliminated the constraints improved by extraneous factors.
Measures of Associations (2 x 2 tables)
Note: It is not advisable to collapse or reduce larger array into 2 x 2 tables. As collapsing introduce distortion and procedure misleading results.
1. Percents
- one of the easiest ways to measure relationship especially if one is clearly a dependent variable.
- If the distribution of responses changes from one category to another, there is evidence for a relationship.
Political Preferences Urban Semi-urban Rural Total
Liberal
Moderate
Conservative 33
(193)
41
(241)
26
(153)
100 30
(161)
37
(199)
34
(182)
100 11
(461)
33
(134)
54
(229)
100
400
547
567
Total 587 562 409 1538
- A difference in percents can be interpreted as a regression coefficients between two dichotomous variables ( a regression coefficients given the magnitude of a change in y, for a unit change in x).
- Example:
x difference
y .9
(45)
.1
(5) .4
(20)
.6
(30) .5
1.0
(50) 1.0
(50)
A change in one unit of X (from 0 to 1) produces a change of 5. This result indicating a substantial relationship.
- gives a clear interpretation and not sensitive to imbalances in the marginal distribution of X.
MEASURES OF ASSOCIATION
1. What is a measure of association
- numerical index summarizing the strength or degree of relationship in two-dimensional cross classification.
2. Consideration which guide the choice of a measure
a. symmetric/asymmetric
b. its interpretation
c. its sensitivity to confounding influence
3. The interpretation of Measures of Association
- The numerical value of most measures lies between 0 and 1.
- Zero if variables are completely unrelated.
- one if the variables are perfectly associated.
a. nil association
- implies statistical independent (statistical independence means that the probability of the joint occurrence of two events equals the product of the probability of their separate occurrence)
Thus: Knowledge of ____ score X is no help in predicting his value on y because the variables are unrelated to work after.
- values close to zero typically indicate a weak to nil relationship
- few measures of association are zero even in the absence of statistics independence Lambda frequently equals zero when the margins totals are highly skewed – that is – most cases in one category but the variables are not independent.
b. perfect association
- classified variables often represent measurement errors where individuals are ____ together into categories out of convenience or necessity.
WAYS TO CONCEPTUALIZE PERFECT ASSOCIATION
1. Strict perfect association
- each value of one variables is uniquely associated with a value of the other.
x
Y 50
0
0 0
0
50 0
50
0
Totals 50 50 50
Knowledge of a person X category implies perfect prediction of his score or y measure of association equal 1.0
2. Implicit Perfect Association
- one variable frequently has more classes than another (the rows and columns are not equal)
Example:
X
Y
Total 0
50
0
50 50
0
0
50 0
0
50
50 50
0
0
50
The numbers of a column classification are as homogeneous as possible with respect to y in the sense that there is only one non-zero row entry per column. Different X categories are generally associated with different y categories but since the classes on X outnumber those on y, the association is not unique.
3. Weak Perfect Association
- categories of X are as homogeneous as possible with respect to y, given the differences in the variable marginal totals.
Example:
X
Y
Total 50
50
50
150 0
0
50
50 0
0
50
50
4. Intermediate Values
How would one make sense of a values being between 0 and 1.0. Suppose the value of an index is .45, what will be the conclusion about the strength and form of relationship?
Answers lies on measures operational definition. Chi-square in this context do not have intuitively appealing interpretation. Thus each measure has to be examined separately in order to group its underlying logic and meaning.
5. Confounding Factors
- extraneous factors frequently confuse one’s interpretation. It is well known that number of cases affects the magnitude of chi-square statistics: The greater the sample size, the larger the value of chi-square statistics.
Almost all measures of association eliminate the effect of sample sizes but similar types of factors can influence their numerical values. Two most common problems are:
1. Skewed marginal distribution
2. Unequal numbers of rows and columns
a. Skewed Marginal Distribution
Example A
Example B
X X
y 60
30
10 20
60
20 10
30
60 60
30
10 20
60
20 10
30
60
A B
Number
Total 60
30
10
100 200
600
200
1000 10
30
60
100 270
660
270
1200 180
90
30
300 120
360
120
600 30
90
180
300 330
540
330
1200
In table A most of the cases fall in middle column, in table B, they are more evenly distributed among the categories of X. But the column percentages are the same in both tables.
!!! A researcher who computes on index for the second set of data might find a strong relationship while someone analyzing the first table might report a much weaker association, although both use the same statistics.
!! Pay particular attention to marginal table. If one or both variables are highly skewed decide whether or not the relative absence of variation is substantially meaningful.
!!!! Adjust observed data or select a less sensitive measure or the lack of variation may itself be theoretically important.
6. Solutions to confounding factors
a. skewed marginal total
- standardize or smooth a table of observed frequencies to conform to a desired marginal totals. Easiest method is to compute percent and ____ percent as though they are now frequencies. Percentaging effectively standardizes a variable ____ it assumes each category of the independent variable has exactly 100 cases thereby removing the effects of unequal marginals.
b. Compute a “maximum” version of a measure
- maximum given the table size or marginal distribution and divide this version into the observed values.
Ex: for a given set of marginal totals, the maximum value of a particular measure is 6.6. Divide this quantity .6 into the observed value, say .3 to obtain in adjusted value of .5 – the adjusted measure partially eliminate the constraints improved by the extraneous factors.
7. Measures of Association for 2 x 2 tables
- best known and most extensively studied type of cross-classification
- little advantage in collapsing/reducing a larger among into a 2 x 2 table.
Collapsed data frequently introduced distortions. Weak relationship in table larger than 2 x 2 could turn out to be a large association if the variables are dichotomized. Produce misleading results.
a. Percent
- easiest way to measure relationship between two variables especially if one is clearly dependent variable. Compare how people in different categories behave with respect to the classes of another. If the distribution of responses changes from one category to another, there is evidence of a relationship.
Example: Table: Relationship between Ethnicity and Political Analysis
Christian M T
Liberal 193
33 161
30 46
21 400
Moderate 241
41 199
37 134
33 547
Conservative 153
26 182
34 229
56 564
587 542 409 1 538
Percents are particularly useful in 2 x 2 tables. A difference in percents or proportions can be interpreted a regression coefficient between two dichotomous variables. (A regression coefficient gives the magnitude of a change in y for a ___ change in the independent variable.
Consider:
X
y .9
(45)
.1
(5)
1.0
(50) .4
(20)
.6
(30)
1.0
(50)
Difference in proportions with respect to fist row is .5. The same quantity should be obtained if the categories of x and y are coded 0 and 1 and the data substituted into familiar regression formula. A change in one wink of x (from 0 to 1) produces a change 7.5 in y. Given the range of possible values (0 to 1), this result indicates a substandard relationship.
b. cross-product ratio
- called odds ratio
- underlies two popular measure of _____ and has several useful properties
- provides an understanding of log linen analysis; a categories multi-variate technique
Simplified Version for Convenience purpose
Christian Muslim Total
Liberal
Conservative 193
153 46
229 239
382
Total 346 275 621
Obviously the variables are related. But how strongly? Comparing the odds of being liberal.
For Christian, these odds are
193/153 = 1.26
or 1.3 to 1
odds of being liberal among Muslim
46/229 = .20
If ethnicity is unrelated to ideology, the odds of being liberal should be the same for our ethnic groups. The odd of being liberal among Muslim is considerably less than one.
Calculate this ratio:
The ratio of the odds (denoted by ) has a simple interpretation. If they are the same in both categories of ethnicity, their ratio will equal to 1.0
If a hypothetical data:
y x
45 90
15 30
Is
Hence, no relationship. Departure in either direction from 1.0 suggests association. The greater the departure, the stronger the relationship.
c. Properties of odds ratio
The odds ratio range from 0 to as with 1.0 indicating statistical independence. Values less than 1.0 imply a “negative” association while values greater than 1.0 mean a positive relationship.
Examine the following:
A B
100
25 50
200 25
200 100
50
125 250 225 150
The odds ratio for A = 16.0 in B = .0625
The B is simply similar with A. Frequencies are related thus maintaining the same underlying strength of association.
In this since the two tables reflect similarity in the magnitude but not in the direction of the relationship.
The lack of symmetry is truly easily removed by calculating the natural log of .
Properties:
1. Odds ratio are ____ under row and column multiplication
Example:
Y A B
75
10 15
100 750
100 15
100
85 115 200 850 115 965
This insensitivity to marginal distribution is quite useful because the inherent relationship appear essentially equivalent.
2. ____ under ____ of rows and columns
Estimate Variance of Odds Ratio
Measure Based on the Chi-Square
Phi Squared
One reason for not using as a measure of association is that its numerical magnitude depends partly on the size of the sample.
Dividing Chi Square by n corrects for n and leads to a popular measure of association phi squared
varies between 0 and 1. zero when the variable are statistically independent sensitive to marginal totals.
5. Correlation Coefficient
r2 = .16 16% of the variance in ideology is accounted by ethnicity.
- sensitive to skewed marginal distribution equivalence to
Cosines of Associations of I x J tables
1. The odds Ratio in I x J tables
- involve looking at several individual odds ratio
- permit one to examine various subhypothesis
- to locate the precise source of association
- east (the bottom right hand) all of the table is the reference point:
Urban Semi-urban Rural
Liberal
Moderate
Conservative 193
241
153 161
199
182 46
134
229 400
547
564
Total 587 542 409 1 538
Where I = row and J = column
t = (I – 1)(J = 1) 2 x 2 table
t = (3 – 1)(3) 2 x 2 table
= (2) (2) = 4 2 x 2 table
- hence, calculate 4 .
Disadvantage: to ____ for larger table
Corresponding odds ratios:
11 = (193/153) / (46/229) = 6.28
21 = (241/153) / (134/229) = 2.69
12 = (161/153) / (134/229) = 4.42
22 = (199/182) / (134/229) = 1.86
Phi Squared
- does not have an upper ___ except in 2 x 2 tables (where it vanishes between 0 and 1)
- difficult to interpret
2. Contingency Coefficient
- lies between 0 and 1
- sample estimate given by
- does not reach 1.0 even when variables seem completely associated
3. Proportional Reduction in Error Measures (PRE)
- rationale
- properties
1. skewed distribution of y may yield PRE measures on zero even if the variables are not statistically independent.
2. limit is 0 and 1.0 zero when x and y are independent 1.0 when they are completely related.
- intermediate values have clear interpretation.
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