Basics of research ab22

ResearchMethods
4/6/2022
By:
Abren Gelaw Mekonnen (PhD)
Assistant Professor of Geography & Environmental Studies
Arba Minch University
2019

Slogans of Confident Researchers:
 I can do it (At the beginning)
 I know what I am doing (at the middle of the research);
 I have better knowledge on this issue (After completing the
research);
(Prof. Sara Hewie).
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As a Beginning:

1. Introduction
1.1 What is a research? Definitions:
 It is “a careful investigation or inquiry for new facts in any
branch of knowledge.”
 Research is an original contribution to the existing stock of
knowledge making for its advancement.
 It is the pursuit of truth with the help of study, observation,
comparison and experiment.
 In short, it is the search for knowledge through objective &
systematic method of finding solutions to a problem.
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 Research is a systematic process that involves:
a) defining the problem,
b) formulating a hypothesis,
c) collecting required facts or data,
d) analyzing the facts; and
e) reaching at conclusions either in the form of:
- solution(s) on a problem or
- generalizations on design &/or improvement of theories &
models;
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1.2 Purpose of a Research
 The purpose of research is to discover answers for a question
or solutions for a problem via the use of scientific procedures.
 Purpose (target) of a scientific research are:
 To gain familiarity with a phenomenon or to achieve new
insights into it – Exploratory or formulative research.
 To portray/describe accurately characteristics of a particular
individual, a group or situation– Descriptive research.
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1.2 Purpose of a Research ….
 Purpose/Objectives ……
 To determine the frequency with which something occurs or
with which it is associated with something else – Diagnostic
research.
 To test a hypothesis of a causal relationship between variables
– Hypothesis-Testing Research.
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1.3 Characteristics of a Scientific Research
Scientific research:
1) Is largely problem-oriented – search a solution to a problem;
2) Is conducted based on facts & empirical evidences;
3) Often requires designing representative sample from which
data are gathered;
4) Often involves generalization about the whole study
population upon analysis of data collected from sample;
5) Requires expertise i.e., skill needed to carryout investigation;
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1.3 Characteristics of a Scientific Research….
 Scientific research…..
 6) Is targeted to generate new knowledge or improve existing
ones;
 7) Is aimed to develop new theories & models, and improve or
disprove existing ones;
 8) Involves rigorous procedures of data collection & analyses;
 9) Presupposes ethical neutrality, i.e., it should be free of bias
in sampling, data collection, analysis, presentation, etc.;
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1.3 Characteristics of a Scientific Research….
 Scientific research…..
 10) Is committed to only objective considerations; i.e. it should
stick to the truth;
 11) It should be valid & reliable;
 Validity – is the accuracy & adequacy of data used for a study;
 Reliability – is the repeatability of results of a study made by
d/t researchers on the same issue/problem;
 12) Requires rationality/justification from researchers’ side;
 13) Requires carefully designed procedures;
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1.4 Ways/Approaches to knowledge Acquisition
 (I) Authoritarian knowledge: seeking kge by referring to
individuals who are socially/politically considered as qualified
producers of knowledge;
 E.g. kings, archbishops, tribal leaders, etc.
 (II) Mystical mode: truth seekers obtain knowledge from
supernatural or magical power;
 This depends on the ritualistic and ceremonial procedures.
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1.4 Ways/Approaches to knowledge Acquisition …
(III) Rationalistic mode:
• Knowledge can be obtained from the norms and rules of logic.
• The assumption is that human mind can understand the world
independent of its observable phenomena.
• The logical framework was worked by the great philosophers like
Aristotle and Immanuel kant.
• This is the foundation for the advancement of science.
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1.4 Ways/Approaches to knowledge Acquisition …
(IV) Scientific mode:
• Scientific research is a systematic, controlled, empirical, and
critical investigation of hypothetical propositions about the
presumed relations among observed phenomena;
• It consist of logical procedures of steps to reach to the final
conclusion which is called research methodology;
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Chapter 2: Steps of Scientific Research Process
1) Identifying a research problem
2) Statement of the problem
3) Specifying objectives and research hypotheses
or questions
4) Reviewing the literature
5) Collecting data (Qualitative & Quantitative)
6) Analyzing and interpreting data
7) Reporting and evaluating research
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2.1 Identifying a Research Problem
 Research begins with identifying a problem that need to be addressed;
 Research topics are problems, questions, controversies, or concerns
that guide the need for conducting a study.
 Issues to be considered while selecting a research problem:
 Whether the problem is researchable;
 Whether people, sites & data are accessible;
 Time & resources availability & adequacy;
 Having knowledge & skill in the issue to be studied;
 Personal interest
 Relevance of the study
 Contribution to the field
 Breadth(Extent) & scope, etc.
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2.1 Identifying a research problem ……
The issues below can be sources of research ideas/topics:
 Personal experience (everyday life, workplace)
 People around you (friends, teachers, colleagues etc…)
 Proposal of funding organizations
 Practical issues
 Past research
 Theory
 Journals, books, and dissertations in your field
 Conferences, workshops, presentations
 Recommendations about future research
 Courses
 Expert consultations
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2.2 Statement of the Research Problem
 It is the process of substantiating problems requiring solutions,
controversies in need of resolution & questions requiring answers;
 Is developed upon facts & empirical evidences from d/t sources;
Problem statement requires:
 Identifying practical problems & research-based research problems;
 Magnifying clear research gaps to be filled by your research;
 Stating what makes your study special from other similar studies;
 Referencing the problem using literature; i.e. supporting it with
sources of evidences from references.
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2.3 Developing Research Objectives
 Objectives: are aims of a study targeted to be addressed by the
researcher.
 Objectives are designed using specific & measurable words, e.g.
 To determine, - To measure,
 To compare, - To quantify,
 To verify, - To illustrate,
 To evaluate, - To assess,
 To calculate/compute, - To correlate,
 To describe, - To explain,
 To analyze, - To explore,
 To establish, - To examine, etc.
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2.3 Developing Research Objectives ……
 Avoid using vague non-action verbs in designing objectives;
 Examples:
 To appreciate,
 To understand,
 To study,
 To comprehend
 Master,
 Etc.
 Verbs like understand, comprehend, master, etc., are broad & vague
words; hence, they can be used for developing general objective of
a study.
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 Anyway, objectives should be SMART:
 That is, that is they should be:
 Specific,
 Measurable,
 Attainable,
 Realistic, and
 Time bound,
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 General objective states what is expected to be achieved by the
study in general terms, e.g. to: understand, comprehend, assess, etc.
 Specific objectives:
 Are derived from the general objective;
 Systematically address the various aspects of the problem studied;
 Require specifying:
 What you shall do “it” in the study
 Where you will do it
 Why will you do “it”
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Hypothesis:
 Hypothesis: is a tentative educative guess about the nature (e.g.
causes, impacts, associations, solution, etc.,) of a problem studied;
 It is a “formal statement that presents the expected relationship
between independent & dependent variables”(Creswell, 1994);
 “It is a tentative prediction about the nature of the relationship
between two or more variables.”
 Characteristically, hypotheses are:
 verifiable & falsifiable via testing;
 not moral or ethical questions;
 neither too specific nor to general;
 predictions of consequences;
 considered valuable even if proven to be false.
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 Hypothesis: is categorized into two broad classes:
 (A) Null hypothesis: its design often begins notifying nonexistence
of reality; i.e. it often starts as: “there is no …”
 E.g.1) There is no significant variation in academic achievement
b/n males & females;
 E.g. 2) There is no association b/n “intelligent quotient” &
“academic achievement” of students;
 (B) Alternative hypothesis: is of types:
 (i) Directional alternative hypothesis: indicates the direction of
occurrences of actions (e.g. relation, effect, etc.,);
 E.g.1) There is a positive association b/n fertilizer application &
crop yield;
 E.g.2) There is a negative relationship b/n temperature & elevation;
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 E.g.3) Depth of GWT decreases with increasing rainfall amount;
 E.g.4) Rainfall amount & elevation are directly related, or rainfall
amount increases with increasing elevation;
 (ii) Non-directional alternative hypothesis: does not indicate the
direction of occurrences of actions (e.g. relation, effect, etc.,);
 E.g.1) There is an association b/n fertilizer use & crop yield;
 E.g.2) There is a relationship b/n temperature & elevation;
 Non-directional hypotheses are used when researchers are not clear
or not sure (or have dilemma) about the r/n s/p b/n d/t variables;
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Research Questions:
 “A research question is a hypothesis stated in form of a question;”
 Research questions are used in place of hypotheses;
 While writing research questions:
 Begin with “how,” “what,” or “why.”
 Avoid using “yes” or “no” questions which begin with verbs like is,
are, do, does, etc.
 Specify the independent and dependent variables.
 Use words describe, compare, or relate to indicate the action or
connection among the variables.
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How to write abstract?
 Abstract is the total view of the research within half page.
• 250- 300 words
• presents objectives, samples, data collection instruments,
data analysis techniques, basic findings and conclusion,
recommendations.
• Key words put separately at the end.
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Ch 3. Literature review
3.1 Essence of Literature Review
 A study begins with understanding literature in the field of study;
 It is an extension or broad version of problem statement;
 It helps to develop both integrity & sophistication in research;
 It is likely a continuous up to the end of the research process;
A comprehensive(full) review of literature is essential b/s it:
a) Provides an up-to-date understanding of the issues (theories,
models, controversies, etc.,) related to the study;
b) Is useful to identify vital issues/themes useful to magnify gaps
for further research in current knowledge;
c) Enables to shape scope of your research;
d) Gives you key evidences valuable for discuss of research results;
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Ch 3. Literature review ……
3.2 Components of Related Literature has two components:
 (A) Conceptual & theoretical Literature:
This type of literature:
 Requires defining, clarifying & differentiating ideas, terminology,
concepts useful for a study (conceptual literature);
 Involves reviewing theories & philosophies related to title of the
study (theoretical Literature);
 Is data-free literature;
 Need facts & evidences from books, journals, etc.
 Can be reviewed from both foreign & local sources (documents).
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Ch. 3: Literature review ……
3.2 Components of Related Literature ……
 (B) Empirical literature:
 It is review of empirical data based-documents,
 E.g. journal articles, & dissertations, theses, both published &
unpublished ones obtained from local & foreign sources;
 Requires use of recent documents, having publication date < 10
years.
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3.3 Bases of a Good Review of Related Literature:
 It:
 The review material must be current.
 Literature & studies reviewed must be relevant to the study.
 Reviewed findings of study should be objective & free of biases.
 Data used of reviewed materials should be scrutinized in terms of
sampling technique used to ensure that generalizations are upon
normal population.
 Reviewed materials related to the current study should be enough
to establish a strong & viable trending of result.
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3.4 Sources & Benefits of Reviewing Related Literature:
 Graduate theses and Dissertation
 Encyclopedia of Educational research
 Books
 Internet sites and resources (website, e- journals, e-books)
 Dictionaries in education and Geography or your specific field of
study.
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Benefits of reviewing literature, i.e. it:
 Helps the researcher to identify & define a research problem;
 Is useful to justify the need for studying a problem;
 Prevents unnecessary duplication of a study;
 Can be source of theoretical basis for the study;
 Enables researchers to conceptualize a research problem and
properly identify & operationally define study variables;
 Helps formulate & refine research instruments;
 Provides lesson for data analysis & interpretation.
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3.5 When to start reviewing & What Material to Review:
 The researcher start reviewing literature the moment the problem
is being conceptualized.
 It is important that the researcher knows:
 what is already known about the problem or
 what earlier researchers have found about it, and
 what questions still need to be answered before the research
questions or objectives are finalized.
 The researcher must have already read adequate literature at
the start of the research activity.
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Ch 3. Literature review ….
What Materials to Review?
 General References:
 Examples are indexes, reviews & abstracts,
 Primary Sources:
 Examples are researches found on published journals.
 Secondary Sources:
 Publications - authors cite the works of others. E.g. books,
encyclopedias, etc.
 Secondary sources are good references for overview of the
problem.
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3.6 Steps in Reviewing Literature:
 1) Review the precise definition of the research problem; note the
key variables specified in the study objectives & hypothesis;
 2) Formulate “search terms” (key words or phrases):
 e.g. Problem: Status of Soil Fertility and Its Influence on Crop
Yield in Southwest Ethiopia;
 3) Using indexes of general references, search for relevant primary
and secondary sources guided by the search terms.
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 4) List in a note of index card the bibliographical data of the
pertinent information such as:
author & title
name of publication
date of publication (include place and date)
pages of the article
 5) Read the selected reading materials, take/summarize key points.
 Prepare a note card for easy retrieval & classification.
 Take brief note on all relevant evidences such as: the problem,
objectives, hypotheses, major findings, & conclusions.
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Ch 3. Literature Review…..
3.7 Research Framework:
 Research framework consists of: (1) theoretical, (2) conceptual,
and (3) analytical frameworks
(1) Theoretical framework:
 Is a structure that guides research by relying on a formal theory;
 I.e., it is constructed by using the established, coherent explanation
of certain phenomena & relationship;
 It requires reviewing all relevant theories related to your study;
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Ch 3. Literature Review…..
(2) Conceptual framework:
 A written or visual presentation that:
 “explains either graphically or in narrative form, the main issues
(e.g. key factors, concepts, variables) in the study; and
 the presumed relationship among them”(Miles & Huberman, 1994)
 It is a skeletal structure of justification, rather than a skeletal
structure of explanation, based on accumulated experience.
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Customers
Changing
customers
Experience Values Lifestyles Demographics
Product
expectations
Quality Price Purchasing Information
Physical Service
Ease Flexibility
Range
Individuality
Health
Age
composition
Security
Loss of
loyalty
Value Image
Priorities
Expectations
Knowledge Access
Currency
Variety
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Where to place conceptual framework ?
• Research problem:
• Aims and objectives:
• Literature review:
• Conceptual framework:
• Research questions:
• Data collection and analysis:
• Interpretation of the results:
• Evaluation of the research:
-The theoretical or practical interest.
-What we want to know and how the
answer may be built up.
-A critical and evaluative review of
the thoughts & experiences of others.
-Provides the structure/content for the
whole study based on literature and
personal experience
-Specific questions that require answers.
-Methodology, methods and analysis.
-Making sense of the results.
-Revisit conceptual framework.
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Ch 3. Literature Review ….
Conceptual frameworks provide researchers with:
 The ability to move beyond descriptions of ‘what’ to explanations
of ‘why’ & ‘how;’
 A means of setting out an explanation set that is used to define &
make sense of the data that flow from the research question;
 A filtering tool for selecting appropriate research questions and
related data collection methods.
 A reference point/structure for the discussion of the literature,
methodology and results.
 The boundaries of the work.
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(3) Analytical Framework:
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The 3 Frameworks:
To sum up:
 Analytical Framework: explains the relationship b/n concepts.
 Conceptual framework: only explains concepts, not the relation
b/n these concepts in terms of dependent & independent variables;
 Theoretical framework explains the relationship b/n concepts
based on the existing theory/theories.
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Ch 4. Data (Quantitative & Qualitative) Collection
4.1 Techniques/Methods of Data Collection:
4.1.1 Questionnaire: is used for collecting primary data
 questionnaire is a research tool by which people are asked to
respond to the same set of questions in a predetermined order.
 This method of data collection is quite popular, particularly in
case of big enquiries.
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Ch4. Data (Quantitative & Qualitative) Collection ….
Why do we use questionnaire? Advantages/Merits:
 It is free from the bias of the interviewer; answers are in respondents’
own words.
 They are low cost in terms of both time and money
 The flow of data is quick and form many people
 Respondents have adequate time to give well thought out answers at a
time and place that suit them.
 Respondents, who are not easily approachable, can also be reached
conveniently.
 Large samples can be made use of and thus the results can be made
more dependable and reliable.
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Ch 4. Data (Quantitative & Qualitative) Collection ….
Questions of a good questionnaire should/must:
1) Be clear, precise & not misunderstood by respondents;
2) Be reasonably short & reasonably long;
3) Be free of technical terms & vague words;
4) Be asked in a more direct or a more indirect form;
5) Be free of leading words;
6) Have adequate response options in close-ended questions;
7) Have numerical response options having the same interval; e.g.
(a) 1 – 3, (b) 4 – 6, (c) 7 – 9, (d) 10 – 12; interval = 3;
8) Have options like ‘don’t know,’ ‘not applicable,’ etc.
9) Have the same meaning for all respondents;
10) Not be misleading b/s of unseen implications;
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 Questionnaire consists of open-ended & close-ended questions:
(A) Open ended questions:
 Are questions with no definite answer;
 Start with words like why, how, what, etc.
 E.g. What are some of the adverse effects of land shortage on your
land management activities?
 You can continue with “why” question following each response;
 This may lead you to unexpected or interesting responses.
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(B) Close-ended questions:
 This consists of:
(i) Dichotomous (yes/no) questions:
 Most often used is: ‘in your opinion, do you think that your land
ownership style affects your land management effort?’
1. Yes 2. No
(ii) Scale Questions:
 Scale or rating questions are used to measure variable;
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 A common type is the Likert scale by which people are asked to
indicate how they agree or disagree with a series of statements.
 E.g. of scale response options: Strongly agree, Agree, Disagree,
Strongly disagree.
(iii) List questions:
 Provides the respondent with a list of response options;
 In this case, respondents may give one or multiple responses
for a question posed/asked;
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 E.g. What actions do you take to enhance fertility of your farmland?
(multiple responses possible):
1. Put under fallow
2. Practicing rotational cropping
3. Adding organic manure
4. Adding artificial fertilizers
5. Multiple cropping
6. Others, specify --------------------------------------------------
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iv. Category Questions
 Designed if only one response is possible (required);
 It is used by most researchers;
 E.g. How can you evaluate your household in terms of
food security?
1. Food secures
2. Food insecure
3. Varies from one year to another
4. Do not know

v. Ranking Questions
 Requires respondents to rank responses in order of importance
 E.g. Which of the following food crops and vegetables are widely
produced in your farm? ( 1 indicates the most important, 2 the next
important, etc.)
Crop
type
Rank
1 Barley,
2 wheat
3 potato
4 maize
5 Taro
6 carrot
Cabbage (greens)

Questionnaire layout:
 Finalizing Questionnaire, carefully consider the following:
 Putting boxes around groups of questions
 Selecting clear and clean font faces
 using lines to take the respondent’s eye from question to
response;
 numbering all questions and sections
 proper instructions of all cases
 Check validity (in terms of content and detail) and reliability
(consistency and stability over time)

Demerits of Questionnaire:
• Low rate of return of the duly filled questionnaires; bias due to no-
response is often indeterminate.
• It can be often used only when respondents are educated and
cooperating.
• The control over questionnaire may be lost once it is sent.
• There is inbuilt inflexibility because of the difficulty of amending
the approach once questionnaires have been dispatched.
• There is also the possibility of ambiguous replies or omission of
replies altogether to certain questions; interpretation of omissions
is difficult.
• It is difficult to know whether willing respondents are truly
representative.

4.1.2 Interviewing
 Is a way of data gathering using conversation b/n the interviewer
(researcher) & the interviewee/respondent.
 Unlike other methods, interview allows in-depth examination of:
 Experiences, knowledge, skill, & attitudes of interviewees;
 View, opinions, feelings & aspirations of the target people.

The interview is the favorable approach where:
 There is a need to attain highly personalized data
 Substantiating ideas are needed;
 Respondents are not fluent in the native language of the country,
 There is difficulty with written language;
 Better reliability of data is needed;

Selecting interview approaches:
 Interview is divided into five categories
Structured interview
 Semi-structured interview
 Non-directive (in-depth)
 Focused interview
 Informal conventional interviews

i. Structured interviews
 Used to collect data for qualitative analysis
 is the use of pre-prepared & standardized questions
 The same questions are posed to all respondents
 Responses are recorded by the interviewer on a standardized
schedule.
 while interviewing, additional interaction b/n interviewer &
respondents should be kept to minimum.

ii. Semi-structured interviews
 Semi-structured are none-standardized questions
 The interviewer has a list of issues and questions to be covered
but may not deal with all of them in each interview.
 The order of questions may also change depending on what
direction the interview takes.
 Additional questions may be asked as new issues arise
 Responses will be documented by not-taking or possibly by-
recording the interview.

iii. None-directive (in-depth ) Questions
• Used to explore an issue or topic in depth.
• Questions are not, generally, pre-planned
• The format of the interview will be such that the respondents are
allowed to talk freely around the subject

Iv. Focused interviews
 Bases up on the respondent’s subjective response to a known
situation
 The interviewer have a prior knowledge of the situation and is able
to re-focus respondents if they drift away from the theme.

v. Informal Conventional interviews
• Is the spontaneous generation of questions as the interview
progress.
• Is the most open-ended form of interview technique
• It offers flexibility in terms of what path the interviewer follows

Interview Guidelines:
 Interviewer must plan in advance and should fully know the
problem under consideration
 Interviewer’s approach must be friendly and informal
 Interviewer must know that ability to listen with understanding,
respect and curiosity is the gateway to communication, and hence
must act accordingly during the interview.
 All possible effort should be made to establish proper rapport
with the interviewee; people are motivated to communicate when
the atmosphere is favorable

Questions that must be avoided
• Jargon questions – question made of technical words;
• Use of prejudicial languages
• Ambiguous questions
• Leading questions
• Double questions
• Questions that probe personal or sensitive issues

4.1.3 Observation:
• Involves the systematic viewing of people’s actions, and the
recording, analyzing and interpreting of their behavior
(Saunders et al.2007).
• Can take place overtly or covertly
• overt observation is where those being observed are aware that
the observation is taking place
• Covert observation is where those observed are unaware of the
observation

Types of observation:
 Participant Observation:
 largely qualitative
Dual purpose of observation: research and
participation
emphasizes the meanings that people give to their
actions
Part of the process is the reporting of the researcher’s
own experience, feelings, fears, and social meanings
The researcher become member of a group being
researched
The researcher becomes ‘immersed’ in the research
setting

Structured ( non-participant) Observation
Largely quantitative
focuses on the frequency of people’s actions
Should result in more reliable data
 Needs standardized checklist

 observe across physical & cultural boundaries
 construct an observation schedule
 conduct observation: participant & non –observation
 write up field notes
 collective observation – individual transcription of field notes
 conduct an observation
 transcription of an observation
 speak to the complexities of observation
 taking photos and the role of visual analysis
 reflect on researcher reflexivity in observations
Activities that you will perform in observation:

 Do not take what happens in the field personally
 Have someone introduce you during the first visit
 Don’t try to accomplish too much in the first days
 Be relatively passive and friendly and polite
 Be very well prepared and always remember your key research
question
Some initial thoughts in observation:

 Negotiate access
 Adopt a passive role at first
 Be a researcher, not therapist
 Be frank and truthful
 Must have trust and confidence of participants
In Observation:

Observation 2
 Speak ‘their’ language
 Be aware of interpersonal and psychological dynamics
 Explain while you observe: what do you ‘see’ and what do you not
‘see’
 Introspections: aware of myself how I use myself as researcher –
 Reflexivity: know own biases
 Verbatim principle: make the notes while they still warm , write
fresh observations
During Observation:

 Interview (face to face: un- semi- fully- structured; focus groups;
and group interview);
 Open-ended questionnaires
 Visual data: photographs; paintings; video; Story data; narratives;
auto ethnographical data
 Written texts: essays; media; magazines; advertisements
 Reflective journals; personal diaries; memory boxes
 Physical traces: artifacts (objects that people produce);
 Documents and records: letters, court and meeting transcripts;
emails;
Field texts to support observations

4.1.4 Focus Group Discussion (FGD)
 A focus group is a small group of 6-8 people led through an open
discussion by a skilled moderator.
 The focus group moderator facilitates disclosure in an open and
spontaneous format.
 The ideal amount of time for one focus group is from 45 - 90
minutes.
 Beyond that most groups are not productive;

4.1.4 Focus Group Discussion (FGD)
 FGD are structured around a set of carefully predetermined
questions, usually no more than 10, for the discussion to be free-
flowing.
 I.e. # of questions:
 12 is the maximum
 10 is better, and
 8 eight is ideal.

FGD ……
 A homogeneous group of strangers comprise the focus group.
 Homogeneity levels the playing field and reduces shyness among
people who will probably never see each other again.
 Estimates range from 6-12 individuals who are homogeneous to
a specific variable (e.g., gender, age range, educational level)

FGD ….
 It takes more than one focus group on any one topic to
produce valid results – usually three or four.
 You’ll know you’ve conducted enough groups (with the same
set of questions) when you’re not hearing anything new
anymore, i.e. you’ve reached a point of saturation.

FGD ….
 questions should be:
 Short and to the point
 Focused on one dimension each
 Unambiguously worded – should be simple;
 Open-ended or sentence completion types
 Non-threatening or embarrassing
 Worded in a way that they cannot be answered with a simple “yes”
or “no” answer (use “why” & “how” instead)

 Genuine interest in people, their behavior & emotions
 Accepting and respecting different people
 Good listening skills of what was said and of what was not said
 Good observational skills; skills of detailed perception:
what is going on and why?
 Interpretation of body language
 Deal with silence; anti-task; too many notes
 Flexibility, capability of quick decision making and independent
thinking
Roles of the research team: the moderator

4.1.5 Transect Walk
• A transect walk is a tool for describing and showing the location
and distribution of:
 resources,
features,
landscape,
main land uses along a given transect.
• A walk along the suggested alignment by a researcher and key
informants to:
• observe,
• listen, and
• ask questions which would enable identification of
problems .

4.1.5 Transect Walk ….
 Transect walk is used for sampling & field measurement about:
 Soil (color, structure, texture, etc.)
 land use/land cover,
 slope gradient & length, etc.
 It helps to quickly learn about:
 land/soil erosion features (rills, gulley)
 land use & land cover;
 social structure, social impacts & community
assets, and
 To triangulate data already available.

PLANNING AND PREPAREDNESS FOR A TRANSECT WALK
O Where to start?
O Where to end?
O What to see?
O At what time to start?
O How long will it take?
O Does the walk need to be split into sections?
O When does the transect team stop?
Enjoy!

5.1 Research Approaches and Design
5.1.1 Qualitative Research:
 is a means for exploring and understanding the meaning
individuals or groups ascribe to a social or human problem
(Creswell, 2009).
 Qualitative research process requires:
Using emerging question & procedures,
No sample representativeness;
Collecting data in the participants setting,
No inferential statistics;
data analysis inductively - building from particulars to
general themes,
making interpretations of the meaning of data.
Flexible written report

5.1.2 Quantitative Research Approach
Quantitative research:
 Often begins from a theory;
 Requires deriving hypothesis/research questions from the theory;
 Needs data collection from representative samples;
 Involves testing objective theories by examining the relationship
among variables.
 Requires use of inferential statistics: involves data analysis using
statistical procedures;
 Often needs to have assumption about testing theories deductively.
 Is built in protection against bias;
 Needs setting controls for alternative explanation;
 Involves deductive generalization; - conclusion for the whole
study population upon analysis of data from sample;

Differences b/n the two approaches:
Quantitative approach Qualitative approach
Epistemological positions
(philosophy)
Objectivist (positivist), post-
positivist
Constructivists /advocacy/
or participatory knowledge
claims
Relationship b/n researcher
& subject (people)
Distant/Outsider Close/insider
Research focus Facts Meanings
Relationship between
theory/concepts & research
Deduction/confirmation Inductive/emergent
The nature of data Data based up on numbers Data based up on text

5.1.3 Mixed-Methods Approach:
 Mixed-methods approach – involves addressing a problem using
d/t data collection & analyses methods from both the quantitative
& qualitative approaches;
 Involves to d/t (data coaction & analysis) methods from both the
quantitative & qualitative approaches;
 It is more than simply collecting & analyzing both kinds of data;
 It also involves the use of both approaches in sequential (one
behind the other) or in parallel (side-by-side ) (Creswell, 2009);

Mixing of Quant. & Qualit. Approaches:
 Sequential (QUAL QUAN) or
(QUAN QUAL)
 Concurrent (QUAL + QUAN,
QUAL + Quan ,
QUAN + Qual)
 Transformative (Concurrent/Sequential ) the researcher
uses theoretical lens as an overarching perspective within a
design

Visual Models of Mixed Method
(Creswell and Plano Clark, 2007)
1. Sequential Explanatory Design
QUAN QUAN qual qual
Data Data Data data interpretation of
Collection analysis collection analysis entire analysis
2. Sequential Exploratory Design
QUAL QUAL quant quant
Data Data Data data interpretation of
Collection analysis collection analysis entire analysis
QUAN qual
QUAL quan

3. Sequential Transformative Design (c)
4. Concurrent Triangulation Design
QUAL quan
Theory: qualitative theory, advocacy worldview
QUAN qual
Theory, qualitative theory, advocacy worldview
QUAN QUAL
QUAN QUAL
Data Data
Collection Collection
QUAN QUAL
Data Data
analysis Data results compared analysis

5. Concurrent Embedded Design
Analysis of Finding Analysis of Finding
6. Concurrent Transformative Design
QUAN
Qual
QUAL
Quan
QUAN + QUAL
Social science theory,
qualitative theory,
advocacy/participatory worldview
Qual
QUAN
Social science theory,
qualitative theory,
advocacy/participatory worldview

Which approach is the beast ?
Quantitative approach is the
best
Qualitative approach is
the best
Constructivism,
inductive logic,
exploratory
research,
grounded theory
Postmodernism,
deductive logic,
confirmatory
research
Mixed approach is the best
(The research questions derive
everything
Pragmatism,
deductive and
inductive logic,
integrated QUAL
& QUAN data
collection &
analysis.

What is research design?
 Is a plan that “involves the intersection of philosophy, strategies
of inquiry & specific methods” of data collection & analysis
 It is the complete strategy of attack on the central research
problem
 It is essentially a blueprint, or set of plan for collecting &
analyzing information.
 In other words, the research design articulates:
 what data are required,
 what methods are going to be used to collect & analyze data, and
 how all of these are going to answer the research questions.
5.2 Research Design

Research design?
 The ideal design collects a maximum amount of information with
a minimum expenditure.
 A researcher is more effective and efficient if she/he identify
her/his resources, procedures, and data.
 Generally, research design is a logical structure of the inquiry
4/6/2022

Design ….
Four (4) characteristics of Research Design (Tayie, 2005):
1. Naturalistic setting:
 To have external validity, the study must be conducted under normally
encountered environmental conditions.
 This means that subjects should be unaware of the research situation.
2. Clear cause-and-effect relationship:
 the researcher must make every effort to control
intervening independent/dependent variable relationship
3. Valid measurement:
 There should be no perceptible connection between the
communication presented to subjects & the measurement
instruments used.
4. Realism
 A research design must be realistic.
 A carful consideration of the availability of time, money,
personnel to conduct the study.

Three questions central to the design of research:
1) What knowledge claims are being made by the
researcher (including a theoretical perspective)?
2) What strategies of inquiry will inform the
procedures?
3) What methods of data collection and analysis will
be used?

Design …..
 The research design also reflects the purpose of the inquiry,
which can be characterized as one or more of the following:
Exploration
Description
Explanation
Prediction
Evaluation
History

Design …..
Question Type Question
Exploratory
questions
What is the case?
What are the key factors?
Descriptive
questions
How many?
What is the incidence of x?
Are x and y related?
Causal questions Why?
What are the causes of y?
Evaluative
questions
What was the outcome of x?
Has A been successful?
Predictive
questions
What will the effect of x be on y?
Historical
questions
What led to y happening?
What were the events that led up to y?
What caused y?

 Research design must, at least, contain ( Kothari, 2004):
 a clear statement of the research problem;
 procedures and techniques to be used for gathering
information;
 the population to be studied and
 methods to be used in processing and analyzing data.
Design …..

TYPES OF RESEARCH DESIGN:
 Research design types:
 Survey (cross-sectional & longitudinal) design: involves
planning to gather data through HH survey & field survey;
 Experimental design – requires panning to gather data from
controlled & experimental groups using experiment;
 Correlational – testing association (cause-effect) b/n variables
 Case study – detail investigation of a problem
 Ethnographic design – targeting to explore a group/people;

RESEARCH DESIGN VS RESEARCH METHODOLOGY
Research design Research methodology
 Focuses on the end-product: What kind of
study is being planned and what kind of
results are aimed at.
E.g. Historical - comparative study,
interpretive approach, exploratory study,
inductive and deductive etc
 Focuses on the research process and the
kind of tools and procedures to be used.
E.g. Document analysis, survey methods,
analysis of existing (secondary)
data/statistics etc)
 point of departure (driven by) = Research
problem or question.
 Point of departure (driven by) = Specific
tasks (data collection or sampling) at hand.
 Focuses on the logic of research: What
evidence is required to address the question
adequately?
Focuses on the individual (not linear) steps
in the research process and the most
‘objective’ (unbiased) procedures to be
employed.

5.3 Determining Sample Design
 All the items under consideration in any field of inquiry
constitute a ‘universe’ or ‘population’.
 A census is a complete enumeration of all the items in the
‘population’
 A sample design is a definite plan determined before any data
are actually collected for obtaining a sample from a given
population.
 Samples can be either probability samples or non-probability
samples.

5.3 Sampling …
 With probability samples each element has a known probability of
being included in the sample but;
 the non-probability samples do not allow the researcher to
determine equal chance for all.
1) Probability samples are those based on:
simple random sampling,
systematic sampling,
stratified sampling,
cluster/area sampling
2) Non-probability samples are those based on:
 convenience sampling,
 judgment/purposive sampling and
quota sampling techniques.

1. Simple random sampling
In this sampling technique:
 Each & every element in the population has an equal chance of
inclusion in the sample;
 Each of the possible samples has the same probability of being
selected;
 For e.g., if we need a sample of 300 items from a universe of
15,000 items, then the researcher should:
 Put names or numbers of all the 15,000 items on slips of paper;
 Draw the 300 samples using the lottery method

Simple random sampling …………
 Using the table of random numbers is another method of random
sampling.
 To do this, there are several standardized tables of random
numbers.
 Suppose we want to select a sample of size n, then:
 Make a list of the population to be sampled
 Give a distinct code number to each unit of the population
 Choose the direction of selection randomly
 Take n units whose code numbers coincide with the random
numbers

Simple random sampling ….
EXAMPLE:
 An instructor wants to select a sample of 20 students, by using
simple random sampling, from a population of 90 students.
 After preparing a list of all 90 students he prepared a serial
numbers to all students; 01,02,03…,88, 89,90.
 Then, the selected students were chosen from a table of random
numbers as shown below.

Table of random numbers
 It is also possible to take a samples horizontally
 13, 22, 17, 11, 09, 45, 41, 65, 31, 37, 83, 84, ……Or
 13, 46, 29, 77, 42, 22, 31, 91, 72, 74, 11, 21,…..
13462 97742 22319 17274 11219 09901
45987 41206 65541 31450 91542 37851
96514 83124 84432 29134 35351 222331
03162 88145 03263 90042 26954 42614

2. Systematic Sampling
 Is obtained by selecting one unit randomly and then
choosing additional units at equally spaced intervals.
 Example: to select every 15th name on a list, or every
10th house on one side of a street and so on.
 This procedure is useful when sampling frame is
available in the form of a list.
 In such a design the selection process starts by picking
some random point in the list and then every nth
element is selected until the desired number is
secured.

Systematic
• EXAMPLE
An instructor wants to select a sample of 20
students, by using simple random sampling, from a
population of 100 students.
You can use the formula K= N/n to determine
interval size
 where N is the population
n is the specified sample
 Then 100/20 = 5th

3. Stratified Sampling
 If the population from which a sample is to be selected
is heterogeneous, then stratified sampling technique is
applied so as to obtain a representative sample.
 In this technique, the population is stratified into a
number of non-overlapping subpopulations or strata
and sample items are selected from each stratum.
 If the items selected from each stratum is based on
simple random sampling the entire procedure, first
stratification and then simple random sampling, is
known as stratified random sampling.

Merits of Stratifies Sampling
 More representative
 Greater accuracy
 Greater geographical coverage

Demerits of Stratifies Sampling
High cost
Effect of bias
Needs skilled Supervisor

4. Cluster/area sampling
 Involves grouping the population & selecting the groups or
clusters rather than individual elements for sample.
 Sample size must often be larger than the simple random sample
to ensure the same level of accuracy since cluster sampling
procedural potential for order bias and other sources of error is
usually accentuated.
 The clustering approach can, however, make the sampling
procedure relatively easier and increase the efficiency of field
work, specially in the case of personal interviews.

Cluster/Area Sampling ….
• Area sampling is quite close to cluster sampling and is often talked
about when the total land area of interest happens to be big one.
• It involves dividing total area into a # of smaller non-overlapping
areas, known as geographical clusters,
• Then a number of smaller areas is randomly selected, and all units
in these small areas are included in the sample.
• Area sampling is specially helpful where we do not have the list of
the population concerned.
• It also makes the field interviewing more efficient since interviewer
can do many interviews at each location.

5.3.2 Non-Probability Sampling
1. Convenience sampling
 It involves obtaining sample by taking population
members which are readily available on-site;
 Convenient sampling:
 Is a very cheap sampling technique;
 Is full of bias;
 Does not enable to get representative samples;
 Is mostly used for conducting pilot survey.

2. Judgmental/purposive sampling
• Sample selection depends on judgment of the
investigator.
• Selecting samples deliberately by the investigator by
taking some factors into consideration
• Useful when dealing with small sized population
• It is not scientific method because of exaggerated bias

3. Quota Sampling
 In stratified sampling the cost of taking random samples from
individual strata is often so expensive that interviewers are simply
given quota to be filled from different strata,
 The actual selection of items for sample being left to the
interviewer’s judgement.
 The size of the quota for each stratum is generally
proportionate to the size of that stratum in the population.
 Quota samples generally happen to be judgement samples rather
than random samples

Determining sample size:
 There is no hard and fast rule that puts sample size using
percentages or any other…
 But, sample size for a study is determined by:
 Population size
 Resources available
 The degree of accuracy desired
 Nature of the investigation
 Homogeneity/heterogeneity of the population
Sampling Techniques used

Determining sample size ….
 The sample size must be large enough:
 To allow for reliable analysis of cross-
tabulation
 To provide for desired levels of accuracy in
estimates of proportions
 To test for the significance of differences
between proportions.

Determine …
 Yamane (1967) provides a simplified formula to
calculate sample sizes.
Where:
n is the sample size,
N is the population size, and
e is the degree of confidence desired, usually set at 0.05

Determine ….
Example:
If the sample frame is 2000, calculate the
sample size

Determining samples of each stratum
• We usually follow the method of proportional allocation under
which the sizes of the samples from the different strata are kept
proportional to the sizes of the strata.
• That is, if Pi represents the proportion of population included in
stratum i, and n represents the total sample size, the number of
elements selected from stratum iis n . Pi
Pi = N1/N
n = n.pi

• To illustrate it, let us suppose that we want a sample of size n
= 30 to be drawn from a population of size N = 8000 which is
divided into three strata of size:
Therefore: N = 8000
N1 = 4000,
N2 = 2400
N3 = 1600
Determining

• Adopting proportional allocation, we shall get the sample sizes as
under for the different strata: For strata with
N1 = 4000
we have P1 = N1/N
P1 = 4000/8000 = 0.5
n1 = n . P1
= 30 (0.5)
n1 = 15
Similarly, for strata with
N2 = 2400,
we have n2 = n . P2
= 30 (2400/8000)
= 9,
For strata with N3 = 1600,
we have n3 = n . P3
= 30 (1600/8000) =
6.

Data can be measured into 4 levels of measurements
4/6/2022

1. Nominal Data:
 Constitute a name value or category with no order
or ranking implied
 The categories are distinct and non-overlapping, but
not ordered
 Can be leveled (equal interval b/n levels)
 More of qualitative in nature
 Example- type of farm, type of product, location,
occupational description of employees, Religious
affiliation, Party affiliation, Social Class, Gender,
ethnicity, type of disability, etc.

Examples of Nominal Questions
1. Which category describes where you work place?
(Tick one)
Retail department
Warehouse
Accounts
Personnel
2. Type of crops produced in your farm plot this season
Barley Wheat Taro Pea Potato Maize
3. Your marital status
Married Divorce Widowed single
 Note: marital status can be coded 1,2,3, but we cannot say 1 is greater than two

2. Ordinal Data (rank)
 Involves ordering or ranking of values eg. 1st, 2nd …
 Can be defined as nominal data which have order
and consensus
Ordinal data are data which can have meaningful
inequalities.
 < or > may indicate any meaning like softer than,
weaker than, stronger than, better than etc.
 It can also indicate agreement data like- very good,
good, fair, strongly agree, agree, disagree, strongly
disagree.

Examples of ordinal questions
1. How often do you eat enjera?(Tick one)
Every day
once a day
once a week
not at all
2. Application of chemical fertilizer resulted in
maximum yield increase
Strongly agree
Agree
Disagree
Strongly disagree

3. Interval Data (Scale)
• Ordinal data are the type of information in which an increase from
one level to the next always reflects the same increase in quality
• Numerical values are assigned along an interval scale with equal
intervals, but there is no zero points which the traits being
measured does not exist.
• The difference b/n 14 and 15 is the same as the difference b/n 91
and 92
• Eg. year of birth, temperature in centigrade etc.

Example
1. What is the amount of temperature increase in this
area (tick one)
1-2 3-4
5-6 more than 6
2. Number of employees scoring within various
ranges on IQ test
76-80
81-85
86-90
91-95

4. Ratio Data (Scale)
 Ratio data are a subset of interval data with the
additional feature of an absolute zero point.
 All measurement data like height, weight, volume,
area and monthly net income are ratio data,
 0-4
 5-9
 10-14
 15-19

Data Analysis
 Qualitative Data Analysis
 Quantitative Data Analysis

Qualitative Data Analysis
 Analysis involves the process of breaking data down into
smaller units to reveal their characteristic element and
structure (Day, 1993).
 Descriptions can lay the basis for analysis, but we need to
go beyond descriptions: we want to
interpret,
to understand and
to explain
 Through analysis we can also gain new insights into our
data

Qualitative analysis as a circular process (Dey, 1993)
Qualitative Analysis
Connecting Classifying
Describing

Description, Classification and Connection
Description
Classification
Connection
 Transcription
 Annotation
 Categorizing
 Splitting &
Splicing
 Linking & Connecting
 corroborate Evidence

136
What are qualitative data/field texts?
– Interview (Face to face: unstructured to highly
structured;
– Observation- participant and non participant;
Shadowing
– Nominal Group Technique; Delphi Technique
– Open-ended questionnaires
– Visual data: photographs; paintings; collages;
video;
– Story data – narratives; life histories;
– Written texts: essays; media; magazines
– Documents and records: letters, court transcripts;
emails; diaries

137
Types of Qualitative Data Analysis
 Content Analysis
Grounded Theory Analysis
Narrative analysis
Conversation analysis
 Discourse analysis

Content analysis
• Studies that analyze the content of texts or documents
(such as letters, speeches, annual reports)
• “Content” refers to words, meanings, pictures, symbols,
themes or any message that can be communicated
• Can be quantitative or qualitative
138

139
Content analysis
 Purpose: identify patterns/trends or themes
 Thematic analysis of texts: what are the major themes/ideas e.g. field
notes, newspaper articles, technical papers, organizational memos,
speeches and literature reviews
 Content analysis is a systematic coding and categorizing to explore large
amounts of textual data in order to ascertain trends and patterns of
words used, their frequency, their relationship and the structures and
discourses of communication.
 Indexing text documents: computer assisted scans key words
 Systematic description of e.g. shopping behavior of teenagers

Content…
After identifying categories within the text, Flick, (2006) distinguished three
steps in the analysis process
 Summarizing content analysis
- where the material is paraphrased, with similar paraphrases
bundled together, and less relevant passages eliminated.
 Explicating content analysis-
- which clarifies ambiguous or contradictory passages by
introducing context material into the analysis
 Structuring Content Analysis
- Seeks to identify types of formal structures in the material.
- Checking sequences

Content
 Merits
• Cost-effective
 Demerits
• it deals with the old data
• it is incapable of exploring associations and causal relationship
b/n variables

Grounded Theory Analysis
• Defined as a theory that is discovered, developed and
provisionally verified through systematic data collection and
analysis of data pertaining to that phenomena (Strauss and
Corbin, 1998).
• Breaking down qualitative data into discrete parts, closely
examining them, and comparing them for similarities and
differences: It is an open-ended approach
• These codes range from descriptive to the conceptual to the
theoretical, depending on what you observe in and infer
from the data and depending also on your personal
knowledge and experience you bring to the data

Narrative Analysis
• Researches that encourage the use of oral or life
histories, or use unstructured interviews, often
elicits qualitative data in the form of qualitative
data.
• Uses to capture the life experience of participants
eg. Medical research

Conversational Analysis
• Is interested in the formal analysis of everyday
conversation (Flick, 2006)
• it includes the analysis of natural texts (the result
of transcribed tape recordings)
• Less concerned with the formal analysis of
language, rather focuses on elements of social
interaction

Discourse analysis
 The focused of discourse analysis is on how both
spoken and written language is used in social
context.
 Attention is given to the structure and organization
of language with an emphasis on how participants’
version of events are constructed.

146
Data Analysis During Data Collection
• Data analysis begins with the first interaction between the
researcher and the participants
• Informal steps involve gathering data, examining data, comparing
prior data to newer data
• Refining instruments or protocols: pilot study

147
Data Analysis During Data Collection
– Have a clear sense of the research topic to help
determine which data are important
– Keep the key research question always in focus
– Write memos during data collection

148
Data Analysis After Data Collection
• Five formal steps
– Data management and organization
– Reading and memoing
– Describing context and participants
– Classifying and interpretation
– Representing findings in a report

149
 Reading and memoing
– Reading field notes, transcripts, memos, and observer’s
comments
– Purpose – to get an initial sense of the data
– Suggestions
Read for several hours at a time
Make marginal notes of your impressions, thoughts, ideas, etc.

150
 Data management and organization
• Organize and check data for completeness
• Write dates on all notes
• Label notes according to type
• Make two photocopies of all notes
• Organize computer files into folders according to data type
and stages of analysis
• Make backup copies of files
• Read through data to be sure it is complete

151
 Describing context and participants
– What is going on in the setting and among participants
– Provide a ‘true’ picture of the setting and events to
understand and appreciate the context
– Issues – the influence of context on participants’ actions
and understanding

152
 Classifying and interpreting
– Process of breaking down data into small units, determining
the importance of units, and putting pertinent units together in
a general interpreted form
– Use of coding and classifying schemes
Code – a basic unit of data – segment of text
Category – a classification of ideas or concepts (cluster of
codes)
Pattern – a relationship across codes and categories

153
 Classifying and interpreting (continued)
– Constant comparison (across data sets and within data set)
• Constantly comparing identified ideas and concepts to
determine their distinctive characteristics so they can be
placed in different appropriate categories
• Iterative in nature (repeating)
• Ongoing throughout the entire research process

154
Interpretation
 Three guiding questions
– What is important in the data?
– Why is it important?
– What can be learned from it?
 Strategies
– The nature of the research influences interpretation

Coding
What is Coding?
 Word or a short phrase that is assigned to a selected segment of
text/data
 Types of Coding
 Descriptive Coding - Word or a short phrase
 In Vivo Coding - verbatim coding, word or a short phrase
from the actual language
 Process Coding- To show action in the data; observable
action, e.g. reading, playing, watching TV, drinking coffee.
 Emotion Coding- Think about the intensity of the emotion
in % or scale
 Versus Coding- Identify in binary terms the individuals, social
groups & concepts. eg Teachers vs. school administrators
 Values Coding- Codes reflect participants’ values, attitudes
and beliefs, representing their perspectives or worldview.
4/6/2022

156
Writing memos
• Memos are spaces to think; post-it notes
• Memos can be interesting discoveries; methodological memos;
and point of curiosity
• Theoretical memos about the codes (things you read about in
the literature)
• Annotate codes: describe what you mean

6. Statistical Tools for Data Analysis
(Quantitative Data Analysis)
4/6/2022

Basic terms:
 Parameters: are statistical measures obtained from a population.
Eg. Mean, variance, standard deviation
 Discrete Data: Data obtained by counting. Always assumes whole
number
 Continuous Data: Data obtained by measuring and can assume any
decimal number.
• Variable: A variable is defined as a character of the participants
or situation for a given study that has different values
in that study.
 A variable must be able to vary or have different values.
Eg. Gender is a variable because it has two values,
female and male. Age
 If a concept has only one value in a particular study it is
not a variable. Eg Gender is not a variable if all
participants are male.

Variables:
 In quantitative research, variables are commonly divided into
independent and dependent variables.
• Independent: Factors which you think might influence the
subject of your study.
• Eg. Factors you think might influence the decision to buy a
particular house include price, investment, location etc.
 It is usually a cause/factor for something to happen
 Dependent Variable: The factor you are interested in explaining.
This is achieved by examining the influence of the independent
variable upon the dependent variable.

Variables
 Discrete variables: variables which assume only integral values,
known as discrete variable; values of such variables are obtained by
counting; e.g. the number of buildings in AMU main campus
 Its data are discrete; e.g. 1, 2, 3, …
 Continuous variables: variables which, theoretically, assume any
value between two given values is called a continuous variable. The
respective values of such continuous variables are obtained by
measuring. E.g. The age of an individual is a continuous variable.
 Its data are discrete; e.g. 15 - 19, 20 - 24, 25 - 29, …

Measurement of data
 Classifying Data types and Measurement Scales
 Data can be measured into 4 levels of measurements

6.1 Descriptive statistics
 The part of statistics comprises of the first three steps, namely,
collection, organization & summarizing of numerical data.
 This includes any kind of data processing techniques which is
assigned to summarize or describe important features of the data
without going any further; that is beyond the data themselves.
 Descriptive statistics: are used to describe or summarize results in
numeric observations, referred to as data.

6.1.1 Measures of Central Tendency
• Mean
• Median
• Mode

6.1.2 Measures of Dispersion
• Is a statistics signifying the extent of scatter of items
around a measure of central tendency.
• By applying a measure of dispersion, we can assess the
reliability of the average being used.
• Summary measures of dispersion
–Range
–Mean deviation
–Variance and standard deviation
–The coefficient of variation

Mean deviation (Average deviation)
• Measures the scatter of the individual observation around
a central value usually the mean or the median of a
distribution.
• If the deviations are taken from the mean then it is
called mean deviation from the mean
• If the deviations are taken from the median then it is
called mean deviation from the median

Variance and standard deviation
 Both measure the average dispersion of the
observations around the mean.
 Variance: is the average square deviation of the
observations from the mean
 Standard deviation is equal to the square root of
variance

The Coefficient of Variation:
•

Example:
• Dr. Kebede obtained the data of the annual income of
employees in the ministry of education and Health.
• Education workers Health workers
= 800 = 480
S.D = 20 S.D.= 12
C.V.= (20/800)x 100% C.V. = (12/480)x100%
2.5% = 2.5%
There is no difference in the relative dispersion of the two
groups.
 Therefore, CV is used to show the relative dispersion of two
groups

Skewness and Kurtosis
 Skew and kurtosis provide summary information about the
shape of a distribution.
 Skewness: if a frequency distribution is symmetrical, then it has no
skewness- that is, the skwiness is zero.
 If one or more observations are extremely large, the mean is
greater than the median or mode. In such cases, the distribution is
said to be positively skewed.
 Conversely, if one or more extremely small observation are present,
the mean is the smallest of the three averages, and the distribution
is said to be negatively skewed.

Coefficient of Kurtosis
 Kurtosis reflects the shape of a distribution.
 The concepts of kurtosis, tail, weight, and
peakedness of a distribution play an important
role in both descriptive and inferential statistics.
 It serves to introduce the concepts of tails,
center, and shoulders of a distribution, and these
in turn are useful for a discussion of limitations
of the measure.

6.2 Inferential statistics
• Inferential Statistics is concerned with drawing conclusions
about the source of the data by taking the sample.
• It refers to statistics that make inferences/interpretations about
population values based on the sample data that you have collected
and analyzed.
• Its purpose is to enable the researcher to make generalizations
beyond the specific sample data; they are:
1. Difference inferential statistics - which lead to inferences
about the differences between groups or within subjects
in the population.
2. Association inferential statistics - which lead to inferences
about the association or relationship between two or
more variables in the population.

 Inferential statistics can only be used under the following
conditions:
You have a complete list of the members of the population.
You draw a random sample from this population
Using a pre-established formula, you determine that your
sample size is large enough.
Selection of Inferential Statistics

Statistical Analysis Decision Tree
A good starting point in your statistical research is to find the category
in which your research question falls.
Are you interested in:
 the relationship between two variables, for example, the higher X and the
higher Y ( Correlation) Or
Comparing differences such as, ”X is higher for group A than it is for group B?”
(Chi-Square Test ,T-test ANOVA) Or
Predicting an outcome variable like “how does Y increase for one more unit X”
(Logistic, Simple linear, Multiple linear, Multinomial regression) Or
 classifications, for example, with seven units of x does my participants fall into
group A or B (Multinomial regression)

Common Type of Inferential Statistics
 The following types of inferential statistics are
commonly used:
T-test (Comparing means)
Confidence Interval
ANOVA
Chi Square
Correlation
Bi-variate Regression
Multi-variate Regression

6.2.1 T-test (Comparing means)
 The T-test compares the means of two (and only two)
groups when the variances are not equal.
 We have
 One sample T-test
 Independent sample T-test
 Paired sample T-test
• All the tests in the t-test family compare differences in mean
scores of observations (normally distributed data).

One sample T test
• When it is used?
 Used to compare the hypothesized mean with the sample
mean
When the dependent variable is normally distributed
within the population.
When the data are independent (scores of one participant
are not dependent on scores of the other
It tests whether a mean score calculated from a sample
equals certain hypothetically assumed value.

One Sample T- Test
 The One-sample T-test does compare the mean of a single
sample. Unlike the other tests, the independent and dependent
sample T-test, it works with only one mean score.
• Example, It would examine the question “Are old people
smaller than the rest of the population?”
• The dependent sample T-test compares before/after
measurements, like for example, ͞Do pupils͛ grades improve after
they receive tutoring?͟

One Sample T-
• Before we actually conduct the One-sample T-test, our
first step is to check the distribution for normality.
• This is best done with a Q-Q Plot.
• Analyze Descriptive statistics Q-Q Plot
• Q-Q Plot Tests the null hypothesis that the variable is
normally distributed.
• One Sample T-Test Tests the null hypothesis that there is
no difference between sample mean and hypothetical
mean

One-Sample Test
Test Value = 30
t df
Sig. (2-
tailed)
Mean
Difference
95% Confidence
Interval of the
Difference
Lower Upper
Age of the
respondent 7.933 19 .000 21.650 15.94 27.36
One-Sample Statistics
N Mean Std. Deviation Std. Error Mean
Age of the respondent 20 51.65 12.206 2.729

One sample T-test
T Calculated T tabulated
Degree of freedom The difference b/n Sample
(n-1) mean & hypo. Mean
T Calculated T- tabulated=
The 95% confidence interval for the mean is
One-Sample Test
Test Value = 30
t df
Sig. (2-
tailed)
Mean
Difference
95% Confidence
Interval of the
Difference
Lower Upper
Age of the
respondent 7.933 19 .000 21.650 15.94 27.36

Independent sample T-test
 Used to compare a sample mean with another sample mean
 When the data are independent (scores of one participant are
not related systematically to scores of the others)
 The independent variable t-test is most often used in two scenarios:
(1) as the test of significance for estimated coefficients and
(2) as the test of independence in correlation analyses.
 Compare mean value(s) of continuous-level (interval or ratio data), normally
distributed data
 Tests the null hypothesis that both samples are from the same population
and therefore do not differ in their mean scores.

Independent….
 Only compare two groups (if your independent variable defines more
than two groups, you either would need to run multiple t-tests or an
ANOVA with post hoc tests). (Go to slide number 147 for Post-hoc test)
The t- test depends upon whether the variances of
the two samples can be assumed equal. (Levene's
Test for Equality of Variances)
 Equal variance is assumed if p-value of Levene’s test is greater than
0.05 (accept the null hypothesis)
In other words the Levene test tests the null hypothesis
that the variances are homogenous (equal) in each
group of the independent variable.
• The degree of freedom associated with this test statistic is
n1+n2-2

Independent
Independent Samples Test
Levene's Test
for Equality of
Variances t-test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Differe
nce
Std.
Error
Differe
nce
95%
Confidence
Interval of the
Difference
Lower Upper
Educational
status
Equal
variances
assumed
2.320 .133 -1.007 66 .318 -.283 .281 -.843 .278
Equal
variances
not assumed
-1.067 48.153 .291 -.283 .265 -.815 .250
Group Statistics
Gender N Mean
Std.
Deviation
Std. Error
Mean
Educational
status
Female 22 1.50 .964 .205
Male 46 1.78 1.134 .167

Dependent Sample T-test (Paired Sample t-test)
• All tests from the T-test family compare one or more mean scores
with each other.
• The dependent sample T-test is used when the
observations or cases in one sample are linked with the cases in
the other sample.
• This is typically the case when repeated measures are taken, or
when analyzing similar units the dependent sample t-test only
signifies the difference between two mean scores and a direction
of change
• It does not automatically give a directionality of cause and effect.
• Paired Sample T-Test Tests the null hypothesis that there is no
difference between sample mean scores

SPSS Example: The result for the question- Do respondent’s Marital status differs
with age?
Paired Samples Statistics
Mean N
Std.
Deviation
Std. Error
Mean
Pair 1 Marrital status 1.08 52 .388 .054
Age of the
respondent
51.54 52 11.646 1.615
Paired Samples Correlations
N Correlation Sig.
Pair 1 Marital status & Age of the
respondent 52 -.079 .579
Paired Samples Test
Paired Differences
t df
Sig. (2-
tailed)
Mean
Std.
Deviation
Std. Error
Mean
95% Confidence Interval of the
Difference
Lower Upper
Pair 1 Marrital
status - Age
of the
respondent
-50.462 11.683 1.620 -53.714 -47.209 -31.147 51 .000

Dependent Sample….
 Do respondents Marital status differs with age?
• The result has three tables
• The 1st table is descriptive statistics
• The 2nd table is the correlation
• The purpose of the correlation analysis is to show whether
the use of dependent samples can increase the reliability of
the analysis compared to the independent samples t-test.
• The higher the correlation coefficient the stronger the strength
of association between both variable and thus the higher the
impact of pairing the data compared to conducting an
unpaired t-test.
• The 3rd table sample test you can read

ANOVA
• ANOVA is short for Analysis of Variance.
• The main purpose of an ANOVA is to test if two or
more groups differ from each other significantly in
one or more characteristics.
• It is generally assumed that the ANOVA is an analysis
of dependencies.͛
• It is referred to as such because it is a test to prove
an assumed cause and effect relationships.

ANOVA:
 It only requires a nominal, scale for the
independent variables and when you have a
categorical and continuous variable.
• Whereas the ANOVA can have one or more
independent variables, it always has only one
dependent variable.
• There are:
1. One-way- ANOVA
2. Factorial ANOVA

One-war- ANOVA
 Tests the effect of one independent variables on one dependent
variables.
 Example: Do the size of the land affect the fertility level
of the cultivated land
First, we examine the multivariate normality of the
dependent variable.
 We can check graphically either with a histogram
(Analyze/Descriptive Statistics/ frequencies… and then in the
menu chart …) or
 with a Q-Q plot (Analyze/Descriptive Statistics/Q-Q Plot
 It tests the null hypothesis that the mean scores are equal
 The One-way- ANOVA dialog box has a couple of
options
1. Contrasts
2. Post hoc tests (also called multiple comparisons) and
3. Options.

On the One-way ANOVA menu-
1. Contrasts are differences in mean scores.
• It allows you to group multiple groups into one
and test the average mean of the two groups
against our third group.
• Contrast=(mean first group + mean second
group)/2.
• It is only equal to the pooled mean, if the groups
are of equal size.

On the menu-
2. Post Hoc tests are useful if your independent variable
includes more than two groups.
• Another test method commonly employed is
the Student-Newman-Keuls test (or short S.N.K), which
pools the groups that do not differ significantly from each
other.
• The Bonferoni adjustment should be selected, which
corrects for multiple pair wise comparisons.
3. Options – click and choose your interest

ANOVA Results
Test of Homogeneity of Variances
fertility level of respondent's cultivated land
Levene Statistic df1 df2 Sig.
23.696 5 62 .000
ANOVA
Fertility level of respondent's cultivated
land
Sum of
Squares df Mean Square F Sig.
Between Groups 2.662 5 .532 2.881 .021
Within Groups 11.456 62 .185
Total 14.118 67

Result
• The first table is the Levene Test or the Test of
Homogeneity of Variances (Homoscedasticity).
• The null hypothesis of the Levene Test is: the variances
are equal.
• The test in our example is significant with p = 0.000 <
0.05 thus we can reject the null hypothesis and cannot
assume that the variances are equal between the
groups with variations.
• This means that the t-test with unequal variances is the
right test to answer our research question.

Result
• The second table presents the results of the ANOVA.
• ANOVA splits the total variance into explained variance
(between groups) and unexplained variance (within groups),
 the variance is defined as Var(x) = sum of squares(x) /degrees
of freedom(x).
 The F value, which is the critical test value that we need for
the ANOVA is defined as
F = Varb /Varw .

Result
• The ANOVA's F-test of significance results in p <
0.001.
• It tests the null hypothesis that the mean scores are
equal; which is the same as saying that the independent
variable has no influence on the dependent variable.
• We can reject the null hypothesis and state that we can
assume the mean scores to be different.
• We can conclude that “The ANOVA shows that a
significant difference of fertility level of respondent's
cultivated land exists between farm land sizes.

Factorial ANOVA
• Compares means across two or more independent variables.
• Has two or more independent variables that split the sample in
four or more groups.
• It tests the effect of one or more independent variables on
one dependent variable.
• It assumes an effect of Y = f(x1, x2, x3͕… xn).
 The factorial ANOVA tests the null hypothesis that all means
are the same

Factorial…
• Example: Do land size and Educational status affect the fertility
level of respondent's cultivated land
 The factorial ANOVA is part of the SPSS General Linear Model
 Analyze/General Linear Model/Univariate . . .
 Thus the ANOVA itself does not tell which of the means in our design
are different, or if indeed they are different.
 Post Hoc tests is used to conduct a separate comparison between
factor levels. You can use Student-Newman-Keuls (SNK) test.
 Post Hoc tests is useful if the factorial ANOVA includes actors that
have more than two factor levels.
 The Contrast dialog model us to group multiple groups into one and
test the average mean of the two groups against our third group.
 In the options tick homogeneity test and observed power

The Result
Levene's Test of Equality of Error Variancesa
Dependent Variable: fertility level of respondent's cultivated land
F df1 df2 Sig.
9.412 11 56 .000
Tests the null hypothesis that the error variance of the dependent variable is equal across groups.
a. Design: Intercept + landsize + EDUCATIO + landsize * EDUCATIO
Tests of Between-Subjects Effects
Dependent Variable: fertility level of respondent's cultivated
land
Source
Type III Sum of
Squares df Mean Square F Sig.
Noncent.
Parameter
Observed
Powerb
Corrected Model 5.379a 11 .489 3.133 .002 34.467 .977
Intercept 155.446 1 155.446 996.108 .000 996.108 1.000
land size .736 5 .147 .943 .461 4.714 .312
EDUCATIO 2.454 3 .818 5.241 .003 15.723 .910
landsize * EDUCATIO .355 3 .118 .757 .523 2.272 .202
Error 8.739 56 .156
Total 372.000 68
Corrected Total 14.118 67
a. R Squared = .381 (Adjusted R Squared = .259)
b. Computed using alpha = .05

Result
• The 1st Table: the Levene's Test of Equality of
Error Variances is significant (p=0.000) and thus we
can reject the null hypothesis. So, the error variance is
not homogenous
• The 2nd Table:

Confidence Interval
• Used to estimate a value/score in a population
based on the score of the participants in your
sample.
• A 95% confidence interval indicates you are 95%
confident that you can predict/infer the value/score
of a population within a specified range based on
the value/score of your sample.

Chi-Square statistic
• Used when you have two categorical (nominal
or ordinal) variables which are normal.
• And you want to know if they are related.
• Are good choices for statistics when analyzing
two nominal variables

Chi-square statistics
 It only tell us whether the relationship is
statistically significant (i.e. not likely to be
due to chance).
 It does not indicate the strength of the
relationship like a correlation does

• Analyze Descriptive Statistics Crosstabs Click on
Statistics and select Chi-square & Eta continue Click on
cells and select Expected and observed then continue Click ok
• Eg. Educational status and gender
This means that there is no relationship between gender and educational status

Correlation Can be used when:
 you have a continuous independent variable
 and a continuous dependent variable.
Correlation

 Correlation coefficients range in value from –1 (a
perfect negative relationship) and +1 (a perfect
positive relationship).
• A value of 0 indicates no linear relationship.
Correlation coefficient Interpretation
-1 Perfectly negatively correlated
1 Perfectly Positively correlated
[ 1, 0.3] Positively correlated
[-1, -0.3] Negatively correlated
[-0.3, 0.3] No correlation
Correlation

Pearson/Spearman Correlation
Pearson correlation
 is used when you have two variables that
are nominal/scale and
 If the data are normally distributed
Spearman Correlation
 is used when the two variables are
ordinal
 If your data are not normally distributed

Correlations
purchasing
food staffs by
using seftinet
money
borrowing
money from
relatives or
money lenders
selling
livestock
depend on
relief
eating only
enset
purchasing foods taffs by
using seftinet money
Pearson Correlation 1 -.068 -.263 .083 -.278*
Sig. (2-tailed) .630 .060 .557 .046
N 52 52 52 52 52
borrowing money from
relatives or money
lenders
Pearson Correlation -.068 1 -.216 -.110 -.030
Sig. (2-tailed) .630 .124 .439 .830
N 52 52 52 52 52
selling livestock Pearson Correlation -.263 -.216 1 .214 .019
Sig. (2-tailed) .060 .124 .127 .895
N 52 52 52 52 52
depend on relief Pearson Correlation .083 -.110 .214 1 .133
Sig. (2-tailed) .557 .439 .127 .346
N 52 52 52 52 52
eating only enset Pearson Correlation -.278*
-.030 .019 .133 1
Sig. (2-tailed) .046 .830 .895 .346
N 52 52 52 52 52
*. Correlation is significant at the 0.05 level (2-tailed).
Correlation example
Analyze correlate Bivariate pearson/spearman two tailed Click ok

The result can be interpreted
• 1st see the variable is positively, negatively
correlated no correlation
• When the probability associated with the P
statistics is .05 of less, then you can assume there
is a relationship between the dependent and
independent variable.

Regression:
• Used to predict the value of certain variable based
on the other variable
• The way how to predict one variable based on the
other (known) variable
Y = α + βx
Where
• Y is dependent variable
• α is constant and unknown
• β coefficient of independent variable (constant)
 here we are going to calculate α & β

Regression
• Regression estimates are used to explain the
relationship between one dependent variable
and one or more independent variables.
•

Uses
• There are three major uses for Regression Analysis
1. causal analysis
It might be used to identify the strength of the effect that
the independent variable(s) have on a dependent variable.
2. Forecasting an effect
That is, regression analysis helps us to understand how much
the dependent variable will change when we change one or
more independent variables.
3. Trend forecasting
Regression analysis predicts trends and future values. The regression
analysis can be used to get point estimates.
eg. What will the level of the lake be after 3 months if the rain
continuous with this rate?

Linear Regression
• The simplest form with one dependent and one
independent variable is defined by the
Formula
y=a+b*x.

SPSS Example
Question - Does the change in Educational status
affect the use of artificial fertilizer?
Step 1. Check whether there is a linear relationship
between dependent variable and independent
variables in the data. For that we check
i) The scatter plot (Graphs-Chart builder
The scatter plot indicates a good linear
relationship, which allows us to
conduct a linear regression analysis.
ii) We can also check the Pearson's
Bivariate Correlation

Step 2. We can check 1-Sample Kolmogorov-Smirnov
test (Analyze/Non-Parametric Tests /Legacy Dialogs/1-
Sample KS…͙).
The test has the null hypothesis that the variable
approximates a normal distribution.
Step 3. Conduct the linear regression analysis.

The Output of the Linear Regression Analysis
Question - Does Educational status affect the use of artificial fertilizer?
Table 1- Model Summaryb
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate Durbin-Watson
1 .413a .170 .158 .281 1.562
a. Predictors: (Constant), Educational status
b. Dependent Variable: Use of artificial fertilizer by respondent
Table 1- indicates the model summary and overall fit statistics.
The R² =.170. This means that the linear regression explains 17.0% of the
variance in the data.
The adjusted R² corrects the R² for the number of independent variables in the
analysis, thus it helps detect over-fitting, because every new independent
variable in a regression model always explains a little additional bit of the
variation, which increases the R².
The Durbin Watson d =1.562 is between the two critical values of 1.5 < d < 2.5,
therefore we can assume that there is no first Order linear autocorrelation in
the data.

Table 2- ANOVAb
Model
Sum of
Squares df
Mean
Square F Sig.
1 Regression 1.069 1 1.069 13.542 .000a
Residual
5.210 66 .079
Total
6.279 67
a. Predictors: (Constant), Educational status
b. Dependent Variable: Use of artificial fertilizer by respondent
• Table 2- indicates the F-test.
The linear regression's F-test has the null hypothesis that
there is no linear relationship between the two variables
(in other words R²=0).
With F = 13.542 and 67 df the test is significant, thus we
can assume that there is a linear relationship between the
variables in our model.

Table 3- Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
95% Confidence
Interval for B Collinearity Statistics
B
Std.
Error Beta
Lower
Bound
Upper
Bound Tolerance VIF
1 (Constant) .906 .064 14.254 .000 .779 1.032
Educational status .117 .032 .413 3.680 .000 .053 .180 1.000 1.000
a. Dependent Variable: Use of artificial fertilizer by respondent
Table 3 shows the regression coefficients, the intercept, and the significance of all
coefficients and the intercept in the model.
The linear regression analysis estimates the linear regression function to be
Using artificial fertilizer (y) =.906+ .117* x.
This means that an increase in one unit of x results in an increase of .117 units of y.
The test of significance of the linear regression analysis tests the null hypothesis that the
estimated coefficient is 0.
The t-test finds that both intercept and variable are highly significant (p<0.05) and thus we
might say that they are significantly different from zero.

This table also includes the Beta weights.
Beta weights are the standardized coefficients and they allow
comparing of the size of the effects of different independent
variables if the variables have different The last thing we need to
check is the homoscedasticity and normality of residuals.

Multiple Linear Regression
Multiple linear regression is used to explain the
relationship between one dependent variable
and two or more independent variables.

SPSS Example
Question - Does the change in Educational status and
age affect the use of artificial fertilizer?
• Analyze – Regression - Linear
In the methods dialog – select ‘enter’ which
means that all variables are forced to
be in the model.
 In the statistics dialog- check:
 Model fit
 estimates
 confidence interval
 Collinearity diagnosis - to check for autocorrelation.
Durbin –Watson - to check for autocorrelation
------- Continue

2nd table -
The R² =.8.6%. This means that the linear regression explains 8.6% of the variance in the
data.

3rd table -
 The F-test has the null hypothesis that there is no linear
relationship between the variables (in other words R²=0).

3rd table -
 The regression equation a+bx1+b2x2
Using artificial fertilizer = 1.325+0.006*age +0.171*educational status
 For every additional increase on age there is 0.06 increase in the use of artificial
fertilizer.
 While for every additional increase on education there is 0.171 increase in the use
of artificial fertilizer.
 Beta weights compare the relative importance of each independent variable in
standardized terms.
Multicollinearity is the extent to which independent variables are correlated with
each other. Tolerance should be greater than 0.1 (or VIF< 10) for
all variables.
N.B. If tolerance is less than 0.1 there is a suspicion of multicollinearity, and with
tolerance less than 0.01 there is proof of multicollinearity.

The techniques of hypothesis testing
and probability
 Hypothesis testing procedures
 Rejection and accepting the null hypothesis

Hypothesis/significance testing
 The method in which we select samples to learn
more about characteristics in a given population is
called hypothesis testing.
 Hypothesis testing is a method for testing a claim or
hypothesis about a parameter in a population, using
data measured in a sample.
 Hypothesis testing is the method of testing whether
claims or hypotheses regarding a population are
likely to be true.

The method of hypothesis testing can be summarized in
four steps.
1. To begin, we identify a hypothesis or claim that we feel
should be tested. For example, we might want to test the
claim that the mean number of hours that instructors in PG
class is 3 hours per week.
2. We select a criterion upon which we decide that the claim
being tested is true or not. For example, the claim is that
instructors in PG class is 3 hours per week.
3. Select a random sample from the population and measure
the sample mean. For example, we could select 10
instructors and measure the mean time (in hours) that they
watch TV per week.

The method…
4. Compare what we observe in the sample to what
we expect to observe if the claim we are testing is
true. We expect the sample mean to be around 3
hours.
If the discrepancy between the sample mean and
population mean is small, then we will likely decide
that the claim we are testing is indeed true.
If the discrepancy is too large, then we will likely
decide to reject the claim as being not true.

four steps in short
 Step 1: State the hypotheses.
 Step 2: Set the criteria for a decision.
 Step 3: Compute the test statistic.
 Step 4: Make a decision.

The null hypothesis (H0)
 The null hypothesis (H0), stated as the null, is a
statement about a population parameter, such as
the population mean, that is assumed to be true.
 The null hypothesis is a starting point. We will test
whether the value stated in the null hypothesis is
likely to be true.
 Keep in mind that the only reason we are testing
the null hypothesis is because we think it is wrong.

Alternative hypothesis (H1)
• An alternative hypothesis (H1) is a statement that
states the actual value of a population parameter is
less than (<), greater than (>), or not equal (≠)to the
value stated in the null hypothesis.
• It states what we think is wrong about the null
hypothesis

In sum, there are two decisions a researcher
can make:
1. Reject the null hypothesis. The sample mean is
associated with a low probability of occurrence
when the null hypothesis is true.
2. Retain the null hypothesis. The sample mean is
associated with a high probability of occurrence
when the null hypothesis is true.

Rejection and accepting the null hypothesis
The probability of obtaining a sample mean, given that the
value stated in the null hypothesis is true, is stated by the
p value.
 The p value is a probability: It varies between 0 and 1 and can never
be negative.
In Step 2, we stated the criterion or probability of obtaining a
sample mean at which point we will decide to reject the value stated
in the null hypothesis, (mostly 1%, 5% and 10%).
To make a decision, we compare the p value to the criterion
we set in Step 2.

p value
 A p value is the probability of obtaining a sample
outcome, given that the value stated in the null
hypothesis is true.
 The p value for obtaining a sample outcome is
compared to the level of significance.
 Significance, or statistical significance, describes a
decision made concerning a value stated in the null
hypothesis.

Statistical Significance
 When the null hypothesis is rejected, we reach
significance.
 When the null hypothesis is retained, we fail to reach
significance.
 When the p value is less than 5% (p < .05), we
reject the null hypothesis.
 We will refer to p < .05 as the criterion for deciding to
reject the null hypothesis, although note that when p =
.05, the decision is also to reject the null hypothesis.

Ethical issues in research
Importance of adhering to ethics
• Experiments carried out By Nazi research on concentration
camp prisoners conducted without the consent of the
research subjects, and often leading predictably to extreme
pain, mutilation and death.
• These experiments led to the development of the Nuremberg
Code in 1947, largely as a legal document to codify what was
unethical about the Nazi research, but also as a code for
future research.
• It also strongly influenced the development of the World
Medical Association’s Declaration of Helsinki in 1964, a code
of ethics developed by physicians to self-regulate the conduct
of medical experimentation.
4/6/2022

Basic ethical principals
 The four ethical principles
1. Respect for autonomy - the obligation to respect
decision-making capacities of autonomous persons;
2. Non-maleficence - the obligation to avoid causing
harm;
3. Beneficence - the obligation to provide benefits and to
balance benefits against risks;
4. Justice - the obligation of fairness in the distribution of
benefits and risks.
4/6/2022

Informed Consent
• Consent involves the procedure by which an
individual may choose whether or not to participate
in a study.
• The researcher’s task is to ensure that participants
have a complete understanding of the purpose and
methods to be used in the study, the risks involved,
and the demands placed upon them as a participant
• The participant must also understand that he or
she has the right to withdraw from the study at any
time.
4/6/2022

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