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Fundamentals of Research and Report Preparation  Context As a guiding principle and
chief operational philosophy, we see research methods and statistical analysis as an integrated process reflective of the
ancient Kmt principle of "making the complex simple and digestible to the common man and woman" as to place research
and statistics within the reach of every university student. Our purpose at this time is merely to outline our system
which we will present in finished book form in the near future. |
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With modern advances in computerized systems, IT, and smart software, we believe that systems
of research can now interactively bridge the gap between what is and what can be ascertained through scientific study and
thus ultimately result in full correspondence of knowledge to reality, and an exhaustive knowledge of the specific object
under study, separating and synthesizing the elements, tendencies, connections, and processes as to arrive a resolution to
an initial problem statement. This is essentially a new moment in history and we intend to seize it. In a systematic process, we isolate and define: (1) research
problem/conceptualization, (2) data type, (3) specific research question(s), (4) data matrix, (5) analysis/analyses type,
(6) formula/equations, (7) selection of appropriate procedure, (8) application of the procedure (number-crunching), (9) replication
in SPSS computerized applications, and finally (10) the interpretation of output. While the emphasis is not on mathematical
calculations, students must be exposes to the mathematical foundations of the statistical concepts covered and will be required
to complete some calculations with and without the aid of a computer. Generally, students should exit the text with a fundamental
knowledge of the integrative use of research methods and statistics, their underlying mathematical properties and their related
computer applications. Equally important is their knowing how and when to use statistics to solve sociological research problems.
This report, then stresses both the conceptual and procedural knowledge (computational algorithms) of statistics and the link
between them and computerized applications such as SPSS 16.0.
This process will be detailed using diagrams, figures, process charts, templates, tables, and computerized flow charts and
algorithms. The careful, meticulous, attention to detail at the theoretical stage of statistical analysis provides the foundation
for proper us of SPSS 16.0 computerized procedures. In sum, this theme of presenting the statistical research methodology
as an unfolding process is centered on lab assignments and a thoroughly prepared lab journal. In sum, there will be
a fundamental assessment of lab assignments with special consideration given to the final lab journal Uses observation, Uses data available to all observers,
Establishes facts, Seeks relationships, draws inferences, establishes laws of development for a process. Promotes understanding,
prediction, control, and change. Answers fundamental questions with certainty. Clarifies the question, States the problem, isolates the hypothesis to
be tested, Collects, analyzes, and interprets data, Forms tentative conclusion, Answers the question or retains or rejects
the hypothesis, findings, report, final policy document. The Process, Identify concern and clarify problem, Formulate
research questions or hypotheses, methods for testing hypothesis, Collect and analyze data, State findings and draw
conclusions, write report Rules of legality, Rules of ethics, Rules of philosophy, Rules of procedure, (appropriation, inquiry, presentation) Basic, Applied, Advanced theoretical Qualitative, Quantitative, Experimental, Nonexperimental, logical method, Ka method Ethnographic, Historical,
Descriptive, Correlational, Action, Evaluative, Causal-comparative, Experimental, Sociological, Political, Economic, Technological
, Cultural, Moral Observational, Internet, Questionnaire, Interview, Participants, Procedures, Settings, Objects, Records, Documents,
Informants Video, Audio, Verbal description, Notation, Recording, Analysis, Questioning, Testing, Measurement Data/
Reliability/Validity Primary/Participant/Sample population/Discrete Secondary/Continuous/Independent Authenticity/Confounding/Intervening Accuracy/Organism/Extraneous Labor,
technology, race, class, culture, generation, psychology, education, health care, housing, transportation, recreation, government,
language, religion, mass media, military, finance, etc./ Professional, Mass movement, Historical context, Personal interest, Importance, Newness, Time
required, Difficulty, Cost, Ethics Sizing , Clarifying, Questioning, Stating hypotheses Statement of the problem, Significance of the problem, Questions and/or
hypotheses, Definitions, Assumptions, Limitations, Delimitations, Survey of literature, Procedures, Calendar, Budget Secondary Sources, such
as Journals, Encyclopedia, Yearbooks, Reviews of research, Handbooks
of research, Scholarly books, Magazine articles, Newspaper articles ERIC Descriptors, CIJE, Dissertation Abstracts, Education Index, Resources
in Education, Psychological Abstracts, Books in Print Journal articles, Conference papers, Dissertations, Monographs, Scholarly books, Technical
reports, Digest of Educational Statistics ERIC databases, UnCover, Periodical abstracts, Newspaper abstracts, Other databases Status Group comparison, Relationship Status Mean, median, mode, Standard deviation, Norms, Percentiles Chi-square, Difference between means, Analysis of variance, Statistical significance Coefficient of correlation, Statistical
significance Topic and Summary, Complete bibliographical citation Place information on the summary card to facilitate regrouping, for example Author(s) names(s) and date, Topic addressed in the report Task 1. State topic, problem, and questions or hypotheses Task 2. Outline the library search for related information Task 3. Identify needed data and their probable sources Task 4. List
steps to be carried out in the study Task 5. Specify procedures and tools for
collecting data Task 6. Forsee how data can best be analyzed and interpreted Task 7. Anticipate the appropriate report format for you research The outline for our systematic approach for each stage is as follows: Introduction Background of the problem Statement of the problem situation Purpose of the study - include practical outcomes of products Questions to be answered or objectives to be investigated Conceptual
or substantive assumptions Rationale and theoretical framework Delineation of the research problem (explain relationship between variables Statement
of hypothesis Importance of the study Definition
of terms (conceptual - operational definitions appear in methodology phase) Scope
and delimitation's of the study Outline of the remainder of the thesis or
proposal The type of problem to be researched helps determine which research design is the best to use.
For example, if the problem deals with something historical, documentary or historiography research designs would be appropriate;
if one wants to study the behavior of children in a classroom setting, a field research design would be used. In some instances there is not one particular research design
that addresses all the dimensions of the research problem. In such cases research designs can be combined to better
address the problem. Purpose Process Quantitative Dimension Qualitative Dimension Problem
Statement Scientific inquiry Question Hypothesis Literature Review Theoretical Framework Level of
Theory Conceptual/Operational Definitions Methodological Design Sampling Measurement Instruments Primary
Data (Data Collection) Validity and Reliability Data Analysis Making Conclusions Strengths Limitations/Weaknesses Presentation Ethical Issues Computer Application
Steps to Follow When Considering Procedures for Collecting DataIdentify the focus of researchSocial behavior, Correlationship/prediction, Present status or events, Innovation, Past status or events, Evaluation , Causation Decide what data are needed for the selected focusDescriptions, Statements , Scores, Analyses, Measurements, Rankings, Opinions, Categorizations Select appropriate data sourcesParticipants, Records, Procedures, Documents, Settings , Informants, Objects Decide whether a sample is needed, and if so, what type of sampleRandom, Stratified, Cluster, Systemic, Convenience Select appropriate procedures for data collectionMaking notations, Questioning, Describing, Measuring, Analyzing, Testing Select tools appropriate for collecting the needed dataMarking devices Tests, Cameras, Surveys, Recorders, Scales, Guides, Measuring devices, Criteria Foresee the formats into which data will accrueDetailed notes, Categories, Summary notations, Rankings, Scores, Measurements, Tallies, Analyses, Data, Analysis, Purposes Types of Data Quantitative: Mostly numerical, Statistical, Describe and test
Qualitative Research: Mostly verbal, Logico-inductive, Discover patterns
Data Analysis/Synthesis/Interpretation
Qualitative Analysis(Logic-inductive process: verbal), identify topics, Cluster topics into categories, Form categories into patterns, Make explanations from patterns, Use explanations to answer, Research question Quantitative Analysis(Statistical process: mathematical), Descriptive Statistics, Central tendency, Mean, Median, Mode, Variability, Range, Variance, Standard deviation, Correlation, Inferential Statistics: Standard error, Confidence limits, Test of significance , Of correlation, Of difference, between means Measurement Model Nominal: Different values indicate a difference in the characteristic being measured. Ordinal: different values indicate a difference in relative amount of the characteristic being measured. Interval: Equal intervals indicate equal differences in amount of the characteristic being measured. Ratio: Ratios of variable values indicate proportional amounts of the characteristic being measured Mathematical Model Continuous: No boundaries between adjoining values. Includes most interval and ratio variables that do not involve counting, and ordinal variables that are not rank orders. Discrete: Clear boundaries between values. Includes nominal variables, counting variables, and rank orders. Measures of Frequencyfrequency: the frequency of occurrence for each variable value proportion, percent: the relative frequency of occurrence for each variable value Measures of Central Tendencymode: the most commonly occurring value mean: an indicator of central tendency sensitive to the exact position of each score in the distribution median: the middle score in a distribution Measures of Variabilityrange: the difference between the lowest and highest scores variance: the average squared deviation from the mean standard deviation: an indicator of average deviation from mean in the same units of measure as the original scores Measures of Linear RelationshipPearson product-moment correlation coefficient: the strength and direction of the relationship between two variables phi coefficient: a variant of the Pearson statistic for two dichotomous variables point-biserial correlation coefficient; a variant of the Pearson statistic where one variable is dichotomous Spearman rank-order correlation coefficient: a variant of the Pearson statistic for two rank-ordered Variables Simple regression: indicates the best estimate of one variable from the values of another Parametric Tests of pattern Hypothesesone-group z test: whether a population µ differs from some predefined value where σ is known one-group t test: whether a population µ differs from some predefined value; knowing σ is Unnecessary Nonparametric Tests of pattern HypothesesBinomial test: whether a population probability for a dichotomous variable differs from some predefined value χ² goodness of fit test: whether a set of population probabilities differ from a set of predefined values Parametric Tests of Relationship Hypothesesdependent groups t test: whether a dichotomous variable is related to a quantitative variable in the population when groups defined by the dichotomous variable are dependent independent groups t test: whether a dichotomous variable is related to a quantitative variable in the population when groups defined by the dichotomous variable are independent t test for a correlation coefficient: whether the population ρxy between two variables differs from 0 one-way independent groups ANOVA: whether a discrete variable is related to a quantitative variable in the population when groups defined by the discrete variable are independent one-way dependent groups ANOVA: whether a discrete variable is related to a quantitative variable in the population when groups defined by the discrete variable are dependent two-way independent groups ANOVA: whether the combination of two discrete variables is related to an interval or ratio variable in the population when groups defined by the discrete variables are independent Nonparametric Tests of Relationship Hypothesesχ² test of independence: whether two variables demonstrate dependent outcomes in the population Wilcoxon T test: whether a dichotomous variable is related to an ordinal, interval, or ratio variable in the population when groups defined by the dichotomous variable are dependent Mann-Whitney U test: whether a dichotomous variable is related to an ordinal, interval, or ratio variable in the population when groups defined by the dichotomous variable are independent Kruskal-Wallis H test: whether a discrete variable is related to an ordinal, interval, or ratio variable in the population when groups defined by the discrete variable are independent
Research findings are the summarize results of data analysisThe thesis format usually containsFront Material Title , Acknowledgements, Abstract BodyIntroduction, Review of literature, Procedures (or method), Findings (or results), Conclusions (or discussion) Back MaterialBibliography, Appendixes Research report conventions generally includeFormatIntroduction, Relevant citations, Procedures followed, Results found, Interpretations Writing StyleDescriptive style, Clear, plain, unemotional language, Third person, passive voice, Tentative rather than adamant, Consistency in format and meaning
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Principles: Ancient Kmt Science Foundations of Modern Science
RESEARCH PROCESS TEMPLATE 1 Objective Conditions, Science, Research Objective Conditions (C,H,L) Scientific/Social Dimensions Social Political Economic Cultural Q1-Q2 Dimensions Measurement
Apparatus that Guarantees Objectivity, Self Criticism and Self Correction 2. Purpose,
Motivation, Driving Force Behind Research Project Objective Conditions Motivation for Research Policy Motivations Academic
Motivations Personal Motivations Problem Solving Explanation Description Process Orientation Scientific Inquiry Other 3. Scientific Reasoning Scientific Reasoning Objective Conditions Q1-Q2 Dimensions
Two Ways of Finding Answers (Induction and Deduction) Types of Research
Questions Description: What is the Militia Movement? Exploration:
Who are the Militia members? Evaluation: Who are the Targets of the Militia Movement Strengths and Limitations of Research The Role of Theory Validity:
The Goal of Research Measurement Validity Causal Validity Generalizability Conclusions 4 Process Orientation
and Problems of Research Objective Conditions (Q1-Q2) Summation
of Conditions Chronologically, Historically, Logically Research Questions Formulated Through Worldview
(Q1-Q-2) Criteria for Precise Research Questions Procedures for Formulating
Research Questions Precise, Concise, Decisive Problem Statement 5.
Literature Review Searching the Literature (C, H, L) Purging
Literature of Eurocentricism Critical Assessment of Race, Class, Gender, Generation, and Cultural
Elements Analysis Synthesis Summation
Conducting the Search Checking the Results 6. Three Fundamental Forms of Research (Deductive Descriptive, and Inductive) Deductive Research: Family History and Domestic Violence Inductive
Research At what cognitive stage does a change in one attitude translate into a change in one's
behaviors in regards to AID? Descriptive Research How did socialist
society collapse in the former Soviet Union? What were the lessons? Guidelines for Researchers
Scientific Guidelines Ethical Guidelines Honesty
and Openness 7. Theoretical Framework Literature
Summary in Journals Critique Critical Assessment of Classes, Races,
Genders, Generations, Cultures Time dimensions Space Dimensions Mass Dimension Direction of Process Historical, Context Maintain (C, H, L,) Summary Conclusions Synthesis Inference 8 Conceptualization and Measurement (Conceptual Modeling on
Computers) Concepts Types of Concepts Measurement Operation Using Available Data Constructing
Questions Sense Data and Empirical Data Multiple Questions Scales
and Indexes Making Observations Combining
Measurement Operations Units of Analysis Levels of Measurement Nominal Level of Measurement Ordinal Level of Measurement Interval Level of Measurement Ratio Level of Measurement Dichotomies
and Special Case Comparison of Levels of Measurement Evaluating
Measures Validity Reliability 9
Causality (Computer Modeling; Internet; Advanced Software) Types of Causal Explanation
Nomothetic Causes Idiographic Causes Combined
Causal Explanations Criteria for Identifying a Nomothetic Cause Causes
of Racism and Genocide: Experimental and Nonexperimental Approaches The Experimental Approach:
Prejudice The Nonexperimental Approach: Prejudice Transformed into Racism Time Order The Experimental Approach: Domestic Violence The
Nonexperimental Approach: White Collar Criminality Nonspuriousness The
Experimental Approach: Hate Crimes The Nonexperimental Approach: Urban Violence Mechanism The Experimental Approach: Domestic Violence, Child Abandonment The Nonexperimental Approach: Affirmative Action Comparison of Experimental and Nonexperimental Approaches The Idiographic Approach to Causality Journalistic
Explanation of Hate Crimes Event-Structure Analysis of the Oklahoma Bombing Field Researchers' Explanations A Combined Explanation of Rwandan killing Rwandan
Research Designs to Determine Time Order Cross-sectional Designs
Longitudinal Designs Repeated Cross-sectional Design Fixed-sample Panel Designs Event-based Designs Units of
Analysis and Causal Conclusions 10 Sampling (Computer Advances; Internet Technology;
Desktop Publishing) An Overview of Sampling in Research Deciding
When to Sample Population: Diverse or Identical? Generalization:
Representative; Nonrepresentative? Resources: Limited or Adequate Evaluating
Samples Sample Example 1: Nazi Supporters? Sample Example 2: Do Public
Employees Favor Gun Control? Prisons for Profit? Curfews? Sample Example 3: What Do US Adults
know about the Militia Movement? Lessons about Sample Quality Sampling
Methods Nonprobability Sampling Methods Availability Sampling Quota sampling Purposive Sampling Snowball Sampling Probability Sampling Methods Simple Random Sampling Systematic
Random Sampling Stratified Random Sampling Cluster Sampling Sampling Distributions Estimating Sampling Error Determining
Sample Size 11 Validity in Three Dimensions: Integration and Review (Computers)
Analysis of Research Design Two Studies of Differential
Treatment Case Study: Homelessness The Research Design Case Study: Discrimination in the Legal System Case Study: Alcohol Use, DWI, and Automobile
Accidents in Detroit The Research Design Analysis of the Design Two Studies of AIDS Awareness Case Study: AIDS Education among the Homeless
The Research Design Analysis of Design Case
Study: AIDS Education among College Students The Research Design Analysis
of Design Lessons about Researcher Design 12 Experiments
(Computer Modeling; Internet; Advances in Computer Automated Machine Technology) The
Experimental Method Components of True Experiments Experimental
and Comparison Group Randomization Distinguishing Random Assignment
from Random Sampling Comparing Matching to Random Assignment Pre testing Post test Measures Control over Conditions Identification
of the Causal Mechanism True Experiments Search Program True Experimental Designs Types of Quasi-Experiments Nonequivalent
Control Group Designs Ex Post Facto Control Group Designs Before-and-After
Designs Evaluation of Experiments: Sources of Invalidity Causal
(Internal) Validity Selection Bias Endogenous Change External Events Contamination Treatment Misidentification
Generalizability Sample Generalizability External
Validity Interaction of Testing and Treatment Issues Ethical Practice
Deception Distribution of Benefits 13
Survey Research (Phone, Computer, E-Mail, Postal) Appeal of Surveys Survey Research in the Social Sciences Reasons for the Appeal of Surveys Versatility Efficiency Generalizability Questionnaire Development and Assessment Guiding Framework What
Are the Study Objectives? What Substantive Variables Should Be Measured? What Questions Will Help interpret Yet Answers? What Do Other People Think? Guidelines for Writing Individual Questions Minimize the Risk of Bias
Make Questions and Responses Choices Clear Avoid Making Disagreement
Disagreeable Do Not Reinvent the Wheel Focus on One Issue at a Time
Choose Response Format Carefully Minimize Fence-sitting and Floating
Questionnaire Organization The Introduction or Cover Letter Pretesting and Revision Survey Designs Mailed Self-administered
Surveys Group-administered Surveys by Telephone In-Person Interviews
A Comparison of Survey Designs Issues Ethical Practice 14 Field Research (Laptop, Sound and Visual Technology and Journals) The
Fieldwork Approach Case Study: European and Asian Scientific and Spiritual Legacy: Lessons for
Modern Medicine Field Research Techniques Participant Observation
Complete Observation Complete Participation Participation
and Observations Personal Dimensions of Participant Observation Intensive
Interviewing Focus Groups Stages Field Research Entering the Field Case Study: Modern Education Without Science Case Study: Modern Education Without Spirituality Case Study; Modern Education With
Both Science and Spirituality Developing and Maintaining Relations Sampling
People and Events Recording and Analyzing Data Taking Notes Analyzing
Notes Evaluating Conclusions Ethical and Moral Practices in the
Field Journal Keeping Analysis of Findings Synthesis
of Findings Summation of Findings Deductions/Induction Generalization to Larger Issues of Education, Science and Spirituality. 15
Multiple Methods in Context (Advances Based on Computer Software; Internet; Computer Modeling): Integration and Review Comparing Methods of Data Collection Supplementing
Single-Method Research Adding Qualitative Data Q1-Q2 Integrating Quality with Quantity; Theory with Practice Case Study: Abandonment of Family
Responsibility Case Study: Drug Peddling Case Study: Drug Abuse Conducting Factorial Surveys Case Study: Health Care for the Elderly Case Study: The Impact of AIDS on Youth Studying Variation across Social Contexts
Case Study: Domestic Violence in Detroit Performing Meta-analyses
Case Study: The Birth, Death and Rebirth of Civilizations Recognizing
Natural Processes Case Study: Courage and Fear in the Face of Death
Lessons about Research Design Individual Data Collection Methods
Supplementing Data Collection Methods Scientific Context Objective Conditions Process Orientation Unity and Struggle
of Opposites Content Synthesis/Summation Preparing Data for Analysis
Displaying Univariate Distributions Graphs Frequency
Distributions Ungrouped Data Grouped Data Summarizing Univariate Distributions Measures Central Tendency Mode
Median Mean Measures of Variation Range Inter quartile Range Variance Standard Deviation Analyzing Data Ethically: How Not to Lie with Statistics Cross-tabulating Variables Describing Association Evaluating
Association 16 Statistics (Advances Based on Computer Software)--- SPSS-16 Descriptive Statistics: breakdowns and exploratory data Correlation Quick Basic Statistical Systems and Block Statistics Interactive Probability Calculator Systems T-Tests (and other tests of group differences)
Frequency Tables. Cross tab Tables, Banner Tables, multiple Analysis Multiple Regression Methods Anova Stepwise Discriminate Function Analysis Non parametric Statistics Distribution
Fitting Factor Analyze and Principle Components Multidimensional
Rating Reliability/True Analysis Cluster analysis techniques
Log-Linear Analysis Central Nonlinear Estimation Canonical Correlation Analyze Survival/ Failure Time Analysis Time Series Analysis Forecasting Transformations, modeling, plots, Auto-correlation
ARIMA and interrupted time series intervention analysis Seasonal
and non-seasonal exponential smoothing Classical seasonal decomposition Census Method I Monthly and quarterly seasonal decomposition and Adjustment
(Census Method II) Polynomial distributed lag models Spectrum (Fouler) and cross-spectrum analysis Regression-based forecasting
techniques Structural Equation Modeling/Path Analysis SEPATH) SEPATH
Method Industrial Statistics Quality Control Charts
Chart Options and Statistics Other Plots and Scroll sheets Auto-updating charts Real Time QC Systems Process Analysis Process
Capability analysis Q1-Q2 plans Screening Designs
Fixed-level factorial designs Central composite response surface)
designs Latin squares Robust design experiments
Designs for mixtures and triangular graphs Designs
for constrained surfaces and mixtures D and A-optimal designs 17 Reporting Research Results (Computer-Based; Internet Driven; LCD) Research
Proposals Scientific Content Spiritual Content Justice Content Research Report Goals Advance Scientific
Knowledge Shape Social Policy Organize Social Action Dialogue with Research Subjects Research Report Types |
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