This document provides an overview of hypotheses for a presentation. It begins with learning outcomes which are to explain the meaning and significance of hypotheses, identify types of hypotheses, and illustrate why hypotheses are needed.
The presentation will cover the scientific method, meaning and types of variables, characteristics of good hypotheses, categories of hypotheses including null and alternative, and how to form and test hypotheses. Hypotheses are defined as educated guesses that relate variables and guide research. They must be testable, falsifiable, and contribute to theory. Hypotheses can be categorized by their formulation as null or alternative, by direction as directional or non-directional, and by their derivation as inductive or deductive.
This document provides an introduction to hypotheses, including definitions, characteristics, purposes, variables, sources, and types of hypotheses. It defines a hypothesis as a tentative statement made to explain certain facts or observations that can be tested. Hypotheses should be clear, specific, testable, limited in scope, and logically consistent. The sources of hypotheses include theories, observations, past experiences, and case studies. The document outlines different types of hypotheses and gives an example of a research hypothesis. It also describes common hypothesis tests like t-tests, z-tests, ANOVA, and chi-square tests and notes that good decisions come from effective research.
This document outlines the steps for formulating a research problem:
1. Select a broad research area from literature and personal experience.
2. Review literature and theories to understand what has been done and how the research could expand knowledge or test theories.
3. Delimit the topic to a more specific research problem.
4. Evaluate the problem for significance, researchability, and feasibility considering factors like time, cost, and ethics.
5. Formulate a final statement of the research problem that is clear, concise, and measurable.
This document defines and provides examples of different types of variables:
- Dependent variables are affected by independent variables. Independent variables are presumed to influence other variables.
- Intervening/mediating variables are caused by the independent variable and themselves cause the dependent variable.
- Organismic variables are personal characteristics used for classification.
- Control/constant variables are not allowed to change during experiments.
- Variables can also be interval, ratio, nominal/categorical, ordinal, dummy, preference, multiple response, or extraneous.
Increasing apple consumption in over-60s has no effect on frequency of doctor's visits.
Does social media use affect
teenagers' mental health?
Teenagers who spend more than 2 hours per day on
social media will report higher levels of anxiety and
depression than those who spend less time.
Teenagers' time spent on social media has no effect on reported levels of anxiety and depression.
Does exercise improve cognitive
function in older adults?
Older adults who engage in regular exercise will
perform better on cognitive tests than those who do
not exercise regularly.
Regular exercise has no effect on cognitive test performance in older adults.
This document defines literature review and outlines its importance and purpose. A literature review aims to critically review knowledge on a research topic. It provides a guide for professionals to stay up-to-date in their field. Literature reviews help identify research problems, gaps in knowledge, and inform the methodology. Sources include primary research articles and secondary sources that summarize others' findings. The document describes the steps of literature review including searching databases and other sources, analyzing sources, and writing an introduction, body, and conclusion. It also outlines strategies like using references and searching forward and backward to identify relevant literature.
Hypothesis testing involves 4 steps: 1) stating the null and alternative hypotheses, 2) setting the significance level criteria, 3) computing a test statistic to evaluate the hypotheses, and 4) making a decision to either reject or fail to reject the null hypothesis based on the significance level and test statistic. The goal is to correctly identify true null hypotheses while minimizing errors like falsely rejecting a true null hypothesis (Type I error) or retaining a false null hypothesis (Type II error).
The document discusses various aspects of research design including:
1. Research design involves decisions about what, where, when, how much, and by what means to study a research problem.
2. Key parts of research design include sampling design, observational design, statistical design, and operational design.
3. Experimental designs aim to establish cause-and-effect relationships through control and manipulation of variables while quasi-experimental and non-experimental designs do not involve manipulation.
This document provides an introduction to hypotheses, including definitions, characteristics, purposes, variables, sources, and types of hypotheses. It defines a hypothesis as a tentative statement made to explain certain facts or observations that can be tested. Hypotheses should be clear, specific, testable, limited in scope, and logically consistent. The sources of hypotheses include theories, observations, past experiences, and case studies. The document outlines different types of hypotheses and gives an example of a research hypothesis. It also describes common hypothesis tests like t-tests, z-tests, ANOVA, and chi-square tests and notes that good decisions come from effective research.
This document outlines the steps for formulating a research problem:
1. Select a broad research area from literature and personal experience.
2. Review literature and theories to understand what has been done and how the research could expand knowledge or test theories.
3. Delimit the topic to a more specific research problem.
4. Evaluate the problem for significance, researchability, and feasibility considering factors like time, cost, and ethics.
5. Formulate a final statement of the research problem that is clear, concise, and measurable.
This document defines and provides examples of different types of variables:
- Dependent variables are affected by independent variables. Independent variables are presumed to influence other variables.
- Intervening/mediating variables are caused by the independent variable and themselves cause the dependent variable.
- Organismic variables are personal characteristics used for classification.
- Control/constant variables are not allowed to change during experiments.
- Variables can also be interval, ratio, nominal/categorical, ordinal, dummy, preference, multiple response, or extraneous.
Increasing apple consumption in over-60s has no effect on frequency of doctor's visits.
Does social media use affect
teenagers' mental health?
Teenagers who spend more than 2 hours per day on
social media will report higher levels of anxiety and
depression than those who spend less time.
Teenagers' time spent on social media has no effect on reported levels of anxiety and depression.
Does exercise improve cognitive
function in older adults?
Older adults who engage in regular exercise will
perform better on cognitive tests than those who do
not exercise regularly.
Regular exercise has no effect on cognitive test performance in older adults.
This document defines literature review and outlines its importance and purpose. A literature review aims to critically review knowledge on a research topic. It provides a guide for professionals to stay up-to-date in their field. Literature reviews help identify research problems, gaps in knowledge, and inform the methodology. Sources include primary research articles and secondary sources that summarize others' findings. The document describes the steps of literature review including searching databases and other sources, analyzing sources, and writing an introduction, body, and conclusion. It also outlines strategies like using references and searching forward and backward to identify relevant literature.
Hypothesis testing involves 4 steps: 1) stating the null and alternative hypotheses, 2) setting the significance level criteria, 3) computing a test statistic to evaluate the hypotheses, and 4) making a decision to either reject or fail to reject the null hypothesis based on the significance level and test statistic. The goal is to correctly identify true null hypotheses while minimizing errors like falsely rejecting a true null hypothesis (Type I error) or retaining a false null hypothesis (Type II error).
The document discusses various aspects of research design including:
1. Research design involves decisions about what, where, when, how much, and by what means to study a research problem.
2. Key parts of research design include sampling design, observational design, statistical design, and operational design.
3. Experimental designs aim to establish cause-and-effect relationships through control and manipulation of variables while quasi-experimental and non-experimental designs do not involve manipulation.
Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.
UNIVARIATE & BIVARIATE ANALYSIS
UNIVARIATE BIVARIATE & MULTIVARIATE
UNIVARIATE ANALYSIS
-One variable analysed at a time
BIVARIATE ANALYSIS
-Two variable analysed at a time
MULTIVARIATE ANALYSIS
-More than two variables analysed at a time
TYPES OF ANALYSIS
DESCRIPTIVE ANALYSIS
INFERENTIAL ANALYSIS
DESCRIPTIVE ANALYSIS
Transformation of raw data
Facilitate easy understanding and interpretation
Deals with summary measures relating to sample data
Eg-what is the average age of the sample?
INFERENTIAL ANALYSIS
Carried out after descriptive analysis
Inferences drawn on population parameters based on sample results
Generalizes results to the population based on sample results
Eg-is the average age of population different from 35?
DESCRIPTIVE ANALYSIS OF UNIVARIATE DATA
1. Prepare frequency distribution of each variable
Missing Data
Situation where certain questions are left unanswered
Analysis of multiple responses
Measures of central tendency
3 measures of central tendency
1.Mean
2.Median
3.Mode
MEAN
Arithmetic average of a variable
Appropriate for interval and ratio scale data
x
MEDIAN
Calculates the middle value of the data
Computed for ratio, interval or ordinal scale.
Data needs to be arranged in ascending or descending order
MODE
Point of maximum frequency
Should not be computed for ordinal or interval data unless grouped.
Widely used in business
MEASURE OF DISPERSION
Measures of central tendency do not explain distribution of variables
4 measures of dispersion
1.Range
2.Variance and standard deviation
3.Coefficient of variation
4.Relative and absolute frequencies
DESCRIPTIVE ANALYSIS OF BIVARIATE DATA
There are three types of measure used.
1.Cross tabulation
2.Spearmans rank correlation coefficient
3.Pearsons linear correlation coefficient
Cross Tabulation
Responses of two questions are combined
Spearman’s rank order correlation coefficient.
Used in case of ordinal data
This document discusses research hypotheses. It defines a hypothesis as a tentative, testable statement about the relationship between two or more variables. A hypothesis helps translate research problems into clear predictions about expected outcomes. Hypotheses are derived from literature reviews and conceptual frameworks. The main types discussed are research hypotheses, null hypotheses, and testable hypotheses. Research hypotheses make predictions, while null hypotheses predict no relationship. Testable hypotheses involve measurable variables. Variables are also discussed, including independent, dependent, extraneous, and demographic variables. Assumptions and limitations of research are briefly covered.
This document outlines the characteristics and criteria of good research. It defines research as the systematic process of collecting and analyzing data to increase understanding. Good research is guided by a question or problem, has a clear goal and plan, and divides main problems into subproblems. It relies on collecting and interpreting data in a cyclical process. Good research clearly defines its scope, explains its process so others can reproduce it, and has a planned, objective design with ethical standards and justified conclusions. The research process involves raising a question, suggesting hypotheses, reviewing literature, acquiring data, analyzing and interpreting data, and determining if hypotheses are supported.
1) A hypothesis is a tentative statement proposed for testing through scientific investigation. It predicts the relationship between two or more variables.
2) Hypotheses guide research design and analysis by specifying the variables to be studied and their expected relationships.
3) The main types of hypotheses are simple, complex, directional, non-directional, null, and alternative. Hypotheses can also be classified as associative, causal, statistical, or research hypotheses.
The document discusses research design and its key principles. It defines research design as a plan or blueprint for conducting a study that maximizes control over interfering factors and validity of findings. Some key points made:
- Research design refers to how a study will be conducted, the type of data collected, and means used to obtain the data.
- Reliability refers to consistency of data, while validity refers to accuracy and truth of measurements.
- Threats to validity include history, selection, testing, instrumentation, maturation, and mortality.
- Descriptive, experimental, and qualitative designs are three basic types of research design.
This document defines and discusses hypotheses. It begins by defining a hypothesis as an assumption or educated guess that is intended to be tested. It then provides definitions of hypotheses from several scholars and discusses their key characteristics. Hypotheses are categorized as null or alternative, directional or non-directional, and deductive or inductive. The document outlines guidelines for forming a hypothesis and testing it, including relating it to the research problem, making it clear and testable, and falsifiable. In summary, this document provides an overview of what a hypothesis is and best practices for developing and testing one.
This document discusses research, including its meaning, objectives, characteristics, significance, and approaches. It defines research as a systematic, organized process of asking questions and gathering evidence to answer them. The objectives of research are to gain new insights and knowledge, accurately portray characteristics of individuals or groups, and test hypotheses. Characteristics include reliability, validity, accuracy, credibility, and generalizability. Research is significant as it encourages scientific thinking, aids in economic and business decision-making, and helps solve social problems. Different approaches to research include quantitative, inferential, experimental, simulation, and qualitative methods. Research methods refer to specific techniques for gathering data while research methodology explains the overall process.
This document defines different types of variables that may be studied in research. It explains that independent variables are those that are manipulated by the researcher, while dependent variables are those affected by the independent variable. Examples are provided such as stress being an independent variable that could affect the dependent variable of mental state. Other variable types discussed include intervening variables, constant variables, and attribute variables. Tests are provided to help understand the difference between independent and dependent variables.
Research design and types of research design final pptPrahlada G
This document discusses research design. It defines research design as the conceptual framework for a research study that includes plans for data collection, measurement, and analysis. The main components of a research design are outlined, including the problem statement, literature review, objectives, methodology, and data analysis plan. Four common types of research designs are explored in more detail: exploratory, descriptive, experimental, and quasi-experimental. Key principles of experimental design like replication, randomization, and local control are also summarized.
BMI (kg/m2)
22.1
23.4
24.8
26.2
27.6
28.9
30.3
31.6
32.9
34.2
35.5
36.8
38.1
39.4
The sample mean is 29.1 kg/m2 and the sample standard
deviation is 4.2 kg/m2. Test the hypothesis that the
population mean BMI is 30 kg/m2 at 5% level of
significance.
Hypothesis -Concept Sources Types
Hypothesis
It is a tentative prediction about the nature of the relationship between two or more variables.
It is a tentative explanation of the research problem
Hypotheses are always in declarative sentence form
An hypothesis is a statement or explanation that is suggested by knowledge or observation but has not, yet, been proved or disproved
Sources of hypothesis
Experience of researcher
Review of literature
Findings of the pilot study
Interaction with knowledgeable persons of the concerned field
Knowledge of culture and society
Creative thinking and imagination of researcher
Types of Hypotheses
Directional Hypotheses / One tailed Hypothesis
Non-Directional Hypotheses / Two tailed Hypothesis
Null Hypotheses
Directional Hypotheses / One Tailed Hypothesis
A directional hypothesis is a prediction made by a researcher regarding a positive or negative change, relationship, or difference between two variables /two groups or conditions
directional hypothesis predicts the nature of the effect of the independent variable on the dependent variable.
It is often symbolized as H1
Non-Directional Hypotheses / Two Tailed Hypothesis
A non-directional simply states that there will be a difference between the two groups/conditions but does not say which will be greater/smaller, quicker/slower etc.
non-directional hypothesis predicts that the independent variable will have an effect on the dependent variable, but the direction of the effect is not specified.
Null Hypotheses
A null hypothesis is a hypothesis that says there is no statistical significance between the two variables.
null hypothesis states that there is no relationship between the two variables being studied (one variable does not affect the other).
It is the hypothesis that the researcher is trying to disprove.
the null hypothesis is a statement of
-‘no effect’ or ‘no difference’
It is often symbolized as H0.
Examples
“ In a clinical trial of a new drug with the current drug ”
We would write Null Hypotheses (H0):
H0 : there is no difference between the two drugs.
We would write Directional Hypotheses (H1):
H1 : the new drug is better than the current drug.
We would write Non-Directional Hypothesis:
the two drugs have different effects, on average.
The document provides an overview of reviewing literature for research. It discusses that a literature review summarizes previous research related to the topic of study. The review helps identify what is already known, research gaps, and informs the research design. It also describes the various types of literature reviews, sources of literature, characteristics of a good review, and the steps involved in conducting a review. These include developing an annotated bibliography, organizing sources thematically, integrating new findings, writing individual sections, and tying the sections together with an introduction and conclusion.
The data, collected for research, has to be processed, analyzed and interpreted to develop a solution to the research question.
Data analysis is a practice in which unorganized or unfinished data is ordered and organized so that useful information can be extracted from it.
It is the most enjoyable part of carrying out the research since after all of the hard works and waiting the researcher gets the chance to find out the answers. So analyzing the data and interpreting the results are the “reward” for the work of collecting the data.
This document discusses the validity and reliability of research tools. It defines validity as the degree to which a tool measures what it is intended to measure. There are four main types of validity: face validity, content validity, criterion validity, and construct validity. Reliability refers to the consistency of a measurement tool. There are three aspects of reliability: stability, internal consistency, and equivalence. Stability assesses a tool's consistency over time, internal consistency examines consistency between items, and equivalence evaluates consistency between raters. Factors like length, training, and instructions can impact a tool's reliability. Overall, validity and reliability are important for ensuring research tools produce accurate and reproducible results.
The document discusses key concepts related to formulating and testing hypotheses, including:
- Null and alternative hypotheses, which are mutually exclusive statements tested through sample analysis.
- Type I and Type II errors that can occur when making decisions to accept or reject the null hypothesis.
- The level of significance, critical region, and test statistics used to determine whether to reject the null hypothesis.
- The differences between one-tailed and two-tailed tests, parametric vs. non-parametric tests, and one-sample vs. two-sample tests.
Difference between qualitative and quantitative research shaniShani Jyothis
nursing research### quantitative research###qualitative research###difference#### process of research ......
Quantitative Vs qualitative research.......÷######$###@@@@@@@@@@ based on hypothesis, ............., variables analysis,............ interpretation, .............
Research Objective
Research is an organized investigation of a problem in which there is an attempt to gain solution to a problem.
To get right solution of a right problem, clearly defined objectives are very important.
Clearly defined objectives enlighten the way in which the researcher has to proceed.
What is Research Objective?
A research objective is a clear, concise, declarative statement, which provides direction to investigate the variables.
Generally research objective focus on the ways to measure the variables , such as to identify or describe them.
Sometime objectives are directed towards identifying the relationship or difference between two variables.
Research objective are the results sought by the researcher at the end of the research process, i.e. what the researcher will be able to achieve at the end of the research study.
The objectives of a research project summarize what is to be achieved by the study.
Objective should be closely related to the statement of the problem.
CHARACTERISTICS OF RESEARCH OBJECTIVES
Research objectives is a concrete statement describing what the research is trying to achieve. A well-worded objective will be SMART, i.e Specific, Measurable, Attainable, Realistic, & Time-bound.
Research objective should be Relevant, Feasible, Logical, Observable, Unequivocal & Measurable.
Objective is a purpose that can be reasonably achieved within the expected timeframe &with the available resources.
The objective or research project summarizes what is to be achieved by the study.
The research objectives are the specific accomplishment the researchers hopes to achieve by the study
The objective include obtaining answers to research questions or testing the research hypothesis.
Why need Research Objectives?
The formulation of research objectives will help researcher to:
With clearly defined objectives, the researchers can focus on the study.
Avoid the collection of data which are not strictly necessary for understanding & solving problem that he or she has defined.
The formulation of objectives organize the study in clearly defined parts or phases.
Properly formulated, specific objectives will facilitate the development of research methodology & will help to orient the collection, analysis, interpretation, &utilization of data.
Types of Research Objectives
General Objective
General objectives are broad goals to be achieved.
The general objectives of the study state what the researcher expects to achieve by the study in general terms.
General objectives are usually less in number.
Introduction-An assumption is a realistic expectation which is something that we believe to be true.
An assumption is an act of faith which does not have empirical evidence to support it.
Assumption provides a basis to develop theories and research instruments and therefore, influence the development and implementation of research process.
Definition-Assumptions are statement that is taken for granted or are considered true, even though they have not been scientifically tested.
Assumptions are principles those are accepted as being true based on logic or reason, but without proof or verification.
Types of assumptions- 1. Universal assumptions are beliefs that are assumed to be true by a large part of society, but testing such assumptions is not always possible.
Example: there is a supernatural power which governs this universe.
2. Based on theories-Assumptions may also be drawn from theories.
If a research study is based on a theory, the assumption of the particular theory may become assumption of that particular research study.
Example: a study based on Roy’s Adaptation Model will use assumption of this particular theoretical model.
3. Needed to conduct a research-Some of the common-sense assumption may be develop to conduct a particular study.
Example: prevalence of coronary artery disease is more common among urban people as compared to rural people.
4. Warranted-These are stated along with evidence to support.
Example: regular prayers bring success because they boost morale.
5. Unwarranted-These are stated without any supportive
Example: almighty God exist everywhere in this universe.
Uses of assumptions in research-Research is built upon assumptions since a foundation is needed to move forward. One must assume something to discover something.
Assumptions listed in research paper may be good sources of the research topics.
Assumptions provide basis to conduct of the research.
Tested assumptions through research studies expand the professional body of knowledge.
Examples of assumptions-People are aware of the experiences that most affect their life choices.
People want to assume control of their own health problems.
Stress should be avoided.
Health is the priority for most of the people.
Increased knowledge about an event lowers anxiety about the event.
Receiving health care at home is preferred to receiving health care in an institute.
Difference b/w hypothesis and Assumptions- Assumptions are basically beliefs & ideas that we hold to be true.
Often with little or no evidence & are not statistically tested in research.
Beliefs about the variables.
Based on the beliefs, the researchers attempt to discover the correlation.
Hypothesis-Hypothesis is a prediction.
Can be statistically tested & may be accepted or rejected.
Predictions about the relationship of two or more variables.
Predict a relation between variables & statistically tested to conclude the study.
This document discusses research design. It defines research design as the planned sequence of the entire research process, including the framework of methods chosen. A good research design includes accurate purpose and methodology statements, appropriate settings and techniques for data collection and analysis, and consideration of timeline and measurements. Key aspects of research design include type of data needed, participants, variables or research questions, and data analysis methods. Choosing a research design requires considering priorities, practicalities, and the type of primary or secondary, qualitative or quantitative data required, as well as how that data will be collected and analyzed.
The document discusses hypothesis testing in research. It defines a hypothesis as a proposition that can be tested scientifically. The key points are:
- A hypothesis aims to explain a phenomenon and can be tested objectively. Common hypotheses compare two groups or variables.
- Statistical hypothesis testing involves a null hypothesis (H0) and alternative hypothesis (Ha). H0 is the initial assumption being tested, while Ha is what would be accepted if H0 is rejected.
- Type I errors incorrectly reject a true null hypothesis. Type II errors fail to reject a false null hypothesis. Hypothesis tests aim to control the probability of type I errors.
- The significance level is the probability of a type I error,
The document discusses hypotheses in research. It defines a hypothesis as a tentative statement about the relationship between two or more variables. Hypotheses help translate research problems into clear predictions and guide investigation. They provide objectivity, direction for data collection, and goals for researchers. Well-stated hypotheses are testable, consistent with existing knowledge, and help establish a link between theory and empirical research. Different types of hypotheses, such as simple, complex, associative, causal, directional, and null hypotheses are described. Sources for developing hypotheses include theoretical frameworks, previous research findings, literature, and experiences.
Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.
UNIVARIATE & BIVARIATE ANALYSIS
UNIVARIATE BIVARIATE & MULTIVARIATE
UNIVARIATE ANALYSIS
-One variable analysed at a time
BIVARIATE ANALYSIS
-Two variable analysed at a time
MULTIVARIATE ANALYSIS
-More than two variables analysed at a time
TYPES OF ANALYSIS
DESCRIPTIVE ANALYSIS
INFERENTIAL ANALYSIS
DESCRIPTIVE ANALYSIS
Transformation of raw data
Facilitate easy understanding and interpretation
Deals with summary measures relating to sample data
Eg-what is the average age of the sample?
INFERENTIAL ANALYSIS
Carried out after descriptive analysis
Inferences drawn on population parameters based on sample results
Generalizes results to the population based on sample results
Eg-is the average age of population different from 35?
DESCRIPTIVE ANALYSIS OF UNIVARIATE DATA
1. Prepare frequency distribution of each variable
Missing Data
Situation where certain questions are left unanswered
Analysis of multiple responses
Measures of central tendency
3 measures of central tendency
1.Mean
2.Median
3.Mode
MEAN
Arithmetic average of a variable
Appropriate for interval and ratio scale data
x
MEDIAN
Calculates the middle value of the data
Computed for ratio, interval or ordinal scale.
Data needs to be arranged in ascending or descending order
MODE
Point of maximum frequency
Should not be computed for ordinal or interval data unless grouped.
Widely used in business
MEASURE OF DISPERSION
Measures of central tendency do not explain distribution of variables
4 measures of dispersion
1.Range
2.Variance and standard deviation
3.Coefficient of variation
4.Relative and absolute frequencies
DESCRIPTIVE ANALYSIS OF BIVARIATE DATA
There are three types of measure used.
1.Cross tabulation
2.Spearmans rank correlation coefficient
3.Pearsons linear correlation coefficient
Cross Tabulation
Responses of two questions are combined
Spearman’s rank order correlation coefficient.
Used in case of ordinal data
This document discusses research hypotheses. It defines a hypothesis as a tentative, testable statement about the relationship between two or more variables. A hypothesis helps translate research problems into clear predictions about expected outcomes. Hypotheses are derived from literature reviews and conceptual frameworks. The main types discussed are research hypotheses, null hypotheses, and testable hypotheses. Research hypotheses make predictions, while null hypotheses predict no relationship. Testable hypotheses involve measurable variables. Variables are also discussed, including independent, dependent, extraneous, and demographic variables. Assumptions and limitations of research are briefly covered.
This document outlines the characteristics and criteria of good research. It defines research as the systematic process of collecting and analyzing data to increase understanding. Good research is guided by a question or problem, has a clear goal and plan, and divides main problems into subproblems. It relies on collecting and interpreting data in a cyclical process. Good research clearly defines its scope, explains its process so others can reproduce it, and has a planned, objective design with ethical standards and justified conclusions. The research process involves raising a question, suggesting hypotheses, reviewing literature, acquiring data, analyzing and interpreting data, and determining if hypotheses are supported.
1) A hypothesis is a tentative statement proposed for testing through scientific investigation. It predicts the relationship between two or more variables.
2) Hypotheses guide research design and analysis by specifying the variables to be studied and their expected relationships.
3) The main types of hypotheses are simple, complex, directional, non-directional, null, and alternative. Hypotheses can also be classified as associative, causal, statistical, or research hypotheses.
The document discusses research design and its key principles. It defines research design as a plan or blueprint for conducting a study that maximizes control over interfering factors and validity of findings. Some key points made:
- Research design refers to how a study will be conducted, the type of data collected, and means used to obtain the data.
- Reliability refers to consistency of data, while validity refers to accuracy and truth of measurements.
- Threats to validity include history, selection, testing, instrumentation, maturation, and mortality.
- Descriptive, experimental, and qualitative designs are three basic types of research design.
This document defines and discusses hypotheses. It begins by defining a hypothesis as an assumption or educated guess that is intended to be tested. It then provides definitions of hypotheses from several scholars and discusses their key characteristics. Hypotheses are categorized as null or alternative, directional or non-directional, and deductive or inductive. The document outlines guidelines for forming a hypothesis and testing it, including relating it to the research problem, making it clear and testable, and falsifiable. In summary, this document provides an overview of what a hypothesis is and best practices for developing and testing one.
This document discusses research, including its meaning, objectives, characteristics, significance, and approaches. It defines research as a systematic, organized process of asking questions and gathering evidence to answer them. The objectives of research are to gain new insights and knowledge, accurately portray characteristics of individuals or groups, and test hypotheses. Characteristics include reliability, validity, accuracy, credibility, and generalizability. Research is significant as it encourages scientific thinking, aids in economic and business decision-making, and helps solve social problems. Different approaches to research include quantitative, inferential, experimental, simulation, and qualitative methods. Research methods refer to specific techniques for gathering data while research methodology explains the overall process.
This document defines different types of variables that may be studied in research. It explains that independent variables are those that are manipulated by the researcher, while dependent variables are those affected by the independent variable. Examples are provided such as stress being an independent variable that could affect the dependent variable of mental state. Other variable types discussed include intervening variables, constant variables, and attribute variables. Tests are provided to help understand the difference between independent and dependent variables.
Research design and types of research design final pptPrahlada G
This document discusses research design. It defines research design as the conceptual framework for a research study that includes plans for data collection, measurement, and analysis. The main components of a research design are outlined, including the problem statement, literature review, objectives, methodology, and data analysis plan. Four common types of research designs are explored in more detail: exploratory, descriptive, experimental, and quasi-experimental. Key principles of experimental design like replication, randomization, and local control are also summarized.
BMI (kg/m2)
22.1
23.4
24.8
26.2
27.6
28.9
30.3
31.6
32.9
34.2
35.5
36.8
38.1
39.4
The sample mean is 29.1 kg/m2 and the sample standard
deviation is 4.2 kg/m2. Test the hypothesis that the
population mean BMI is 30 kg/m2 at 5% level of
significance.
Hypothesis -Concept Sources Types
Hypothesis
It is a tentative prediction about the nature of the relationship between two or more variables.
It is a tentative explanation of the research problem
Hypotheses are always in declarative sentence form
An hypothesis is a statement or explanation that is suggested by knowledge or observation but has not, yet, been proved or disproved
Sources of hypothesis
Experience of researcher
Review of literature
Findings of the pilot study
Interaction with knowledgeable persons of the concerned field
Knowledge of culture and society
Creative thinking and imagination of researcher
Types of Hypotheses
Directional Hypotheses / One tailed Hypothesis
Non-Directional Hypotheses / Two tailed Hypothesis
Null Hypotheses
Directional Hypotheses / One Tailed Hypothesis
A directional hypothesis is a prediction made by a researcher regarding a positive or negative change, relationship, or difference between two variables /two groups or conditions
directional hypothesis predicts the nature of the effect of the independent variable on the dependent variable.
It is often symbolized as H1
Non-Directional Hypotheses / Two Tailed Hypothesis
A non-directional simply states that there will be a difference between the two groups/conditions but does not say which will be greater/smaller, quicker/slower etc.
non-directional hypothesis predicts that the independent variable will have an effect on the dependent variable, but the direction of the effect is not specified.
Null Hypotheses
A null hypothesis is a hypothesis that says there is no statistical significance between the two variables.
null hypothesis states that there is no relationship between the two variables being studied (one variable does not affect the other).
It is the hypothesis that the researcher is trying to disprove.
the null hypothesis is a statement of
-‘no effect’ or ‘no difference’
It is often symbolized as H0.
Examples
“ In a clinical trial of a new drug with the current drug ”
We would write Null Hypotheses (H0):
H0 : there is no difference between the two drugs.
We would write Directional Hypotheses (H1):
H1 : the new drug is better than the current drug.
We would write Non-Directional Hypothesis:
the two drugs have different effects, on average.
The document provides an overview of reviewing literature for research. It discusses that a literature review summarizes previous research related to the topic of study. The review helps identify what is already known, research gaps, and informs the research design. It also describes the various types of literature reviews, sources of literature, characteristics of a good review, and the steps involved in conducting a review. These include developing an annotated bibliography, organizing sources thematically, integrating new findings, writing individual sections, and tying the sections together with an introduction and conclusion.
The data, collected for research, has to be processed, analyzed and interpreted to develop a solution to the research question.
Data analysis is a practice in which unorganized or unfinished data is ordered and organized so that useful information can be extracted from it.
It is the most enjoyable part of carrying out the research since after all of the hard works and waiting the researcher gets the chance to find out the answers. So analyzing the data and interpreting the results are the “reward” for the work of collecting the data.
This document discusses the validity and reliability of research tools. It defines validity as the degree to which a tool measures what it is intended to measure. There are four main types of validity: face validity, content validity, criterion validity, and construct validity. Reliability refers to the consistency of a measurement tool. There are three aspects of reliability: stability, internal consistency, and equivalence. Stability assesses a tool's consistency over time, internal consistency examines consistency between items, and equivalence evaluates consistency between raters. Factors like length, training, and instructions can impact a tool's reliability. Overall, validity and reliability are important for ensuring research tools produce accurate and reproducible results.
The document discusses key concepts related to formulating and testing hypotheses, including:
- Null and alternative hypotheses, which are mutually exclusive statements tested through sample analysis.
- Type I and Type II errors that can occur when making decisions to accept or reject the null hypothesis.
- The level of significance, critical region, and test statistics used to determine whether to reject the null hypothesis.
- The differences between one-tailed and two-tailed tests, parametric vs. non-parametric tests, and one-sample vs. two-sample tests.
Difference between qualitative and quantitative research shaniShani Jyothis
nursing research### quantitative research###qualitative research###difference#### process of research ......
Quantitative Vs qualitative research.......÷######$###@@@@@@@@@@ based on hypothesis, ............., variables analysis,............ interpretation, .............
Research Objective
Research is an organized investigation of a problem in which there is an attempt to gain solution to a problem.
To get right solution of a right problem, clearly defined objectives are very important.
Clearly defined objectives enlighten the way in which the researcher has to proceed.
What is Research Objective?
A research objective is a clear, concise, declarative statement, which provides direction to investigate the variables.
Generally research objective focus on the ways to measure the variables , such as to identify or describe them.
Sometime objectives are directed towards identifying the relationship or difference between two variables.
Research objective are the results sought by the researcher at the end of the research process, i.e. what the researcher will be able to achieve at the end of the research study.
The objectives of a research project summarize what is to be achieved by the study.
Objective should be closely related to the statement of the problem.
CHARACTERISTICS OF RESEARCH OBJECTIVES
Research objectives is a concrete statement describing what the research is trying to achieve. A well-worded objective will be SMART, i.e Specific, Measurable, Attainable, Realistic, & Time-bound.
Research objective should be Relevant, Feasible, Logical, Observable, Unequivocal & Measurable.
Objective is a purpose that can be reasonably achieved within the expected timeframe &with the available resources.
The objective or research project summarizes what is to be achieved by the study.
The research objectives are the specific accomplishment the researchers hopes to achieve by the study
The objective include obtaining answers to research questions or testing the research hypothesis.
Why need Research Objectives?
The formulation of research objectives will help researcher to:
With clearly defined objectives, the researchers can focus on the study.
Avoid the collection of data which are not strictly necessary for understanding & solving problem that he or she has defined.
The formulation of objectives organize the study in clearly defined parts or phases.
Properly formulated, specific objectives will facilitate the development of research methodology & will help to orient the collection, analysis, interpretation, &utilization of data.
Types of Research Objectives
General Objective
General objectives are broad goals to be achieved.
The general objectives of the study state what the researcher expects to achieve by the study in general terms.
General objectives are usually less in number.
Introduction-An assumption is a realistic expectation which is something that we believe to be true.
An assumption is an act of faith which does not have empirical evidence to support it.
Assumption provides a basis to develop theories and research instruments and therefore, influence the development and implementation of research process.
Definition-Assumptions are statement that is taken for granted or are considered true, even though they have not been scientifically tested.
Assumptions are principles those are accepted as being true based on logic or reason, but without proof or verification.
Types of assumptions- 1. Universal assumptions are beliefs that are assumed to be true by a large part of society, but testing such assumptions is not always possible.
Example: there is a supernatural power which governs this universe.
2. Based on theories-Assumptions may also be drawn from theories.
If a research study is based on a theory, the assumption of the particular theory may become assumption of that particular research study.
Example: a study based on Roy’s Adaptation Model will use assumption of this particular theoretical model.
3. Needed to conduct a research-Some of the common-sense assumption may be develop to conduct a particular study.
Example: prevalence of coronary artery disease is more common among urban people as compared to rural people.
4. Warranted-These are stated along with evidence to support.
Example: regular prayers bring success because they boost morale.
5. Unwarranted-These are stated without any supportive
Example: almighty God exist everywhere in this universe.
Uses of assumptions in research-Research is built upon assumptions since a foundation is needed to move forward. One must assume something to discover something.
Assumptions listed in research paper may be good sources of the research topics.
Assumptions provide basis to conduct of the research.
Tested assumptions through research studies expand the professional body of knowledge.
Examples of assumptions-People are aware of the experiences that most affect their life choices.
People want to assume control of their own health problems.
Stress should be avoided.
Health is the priority for most of the people.
Increased knowledge about an event lowers anxiety about the event.
Receiving health care at home is preferred to receiving health care in an institute.
Difference b/w hypothesis and Assumptions- Assumptions are basically beliefs & ideas that we hold to be true.
Often with little or no evidence & are not statistically tested in research.
Beliefs about the variables.
Based on the beliefs, the researchers attempt to discover the correlation.
Hypothesis-Hypothesis is a prediction.
Can be statistically tested & may be accepted or rejected.
Predictions about the relationship of two or more variables.
Predict a relation between variables & statistically tested to conclude the study.
This document discusses research design. It defines research design as the planned sequence of the entire research process, including the framework of methods chosen. A good research design includes accurate purpose and methodology statements, appropriate settings and techniques for data collection and analysis, and consideration of timeline and measurements. Key aspects of research design include type of data needed, participants, variables or research questions, and data analysis methods. Choosing a research design requires considering priorities, practicalities, and the type of primary or secondary, qualitative or quantitative data required, as well as how that data will be collected and analyzed.
The document discusses hypothesis testing in research. It defines a hypothesis as a proposition that can be tested scientifically. The key points are:
- A hypothesis aims to explain a phenomenon and can be tested objectively. Common hypotheses compare two groups or variables.
- Statistical hypothesis testing involves a null hypothesis (H0) and alternative hypothesis (Ha). H0 is the initial assumption being tested, while Ha is what would be accepted if H0 is rejected.
- Type I errors incorrectly reject a true null hypothesis. Type II errors fail to reject a false null hypothesis. Hypothesis tests aim to control the probability of type I errors.
- The significance level is the probability of a type I error,
The document discusses hypotheses in research. It defines a hypothesis as a tentative statement about the relationship between two or more variables. Hypotheses help translate research problems into clear predictions and guide investigation. They provide objectivity, direction for data collection, and goals for researchers. Well-stated hypotheses are testable, consistent with existing knowledge, and help establish a link between theory and empirical research. Different types of hypotheses, such as simple, complex, associative, causal, directional, and null hypotheses are described. Sources for developing hypotheses include theoretical frameworks, previous research findings, literature, and experiences.
The document discusses different types of hypotheses:
- Directional hypotheses specify the expected direction of relationships between variables, while non-directional hypotheses do not.
- Hypotheses can take declarative, null, question, or predictive forms. Declarative hypotheses state expected relationships, while null hypotheses state no relationship exists. Question hypotheses are research questions. Predictive hypotheses allow researchers to state expected principles.
- Examples are provided for each type to illustrate their meanings.
This document provides an overview of hypothesis testing including:
- Defining null and alternative hypotheses
- Types of errors like Type I and Type II
- Test statistics and significance levels for comparing means, proportions, and standard deviations of one and two populations
- Examples are given for hypothesis tests on population means, proportions, and comparing two population means.
The document discusses different types of hypotheses used in research studies, including simple, complex, empirical, null, alternative, statistical, directional, non-directional, causal, and associative hypotheses. It defines each type of hypothesis and provides examples. The document also covers the functions, characteristics, and contributions of hypotheses in structuring research problems and guiding the research process.
This document provides an overview of hypothesis testing in inferential statistics. It defines a hypothesis as a statement or assumption about relationships between variables or tentative explanations for events. There are two main types of hypotheses: the null hypothesis (H0), which is the default position that is tested, and the alternative hypothesis (Ha or H1). Steps in hypothesis testing include establishing the null and alternative hypotheses, selecting a suitable test of significance or test statistic based on sample characteristics, formulating a decision rule to either accept or reject the null hypothesis based on where the test statistic value falls, and understanding the potential for errors. Key criteria for constructing hypotheses and selecting appropriate statistical tests are also outlined.
Hypothesis is usually considered as the principal instrument in research and quality control. Its main function is to suggest new experiments and observations. In fact, many experiments are carried out with the deliberate object of testing hypothesis. Decision makers often face situations wherein they are interested in testing hypothesis on the basis of available information and then take decisions on the basis of such testing. In Six –Sigma methodology, hypothesis testing is a tool of substance and used in analysis phase of the six sigma project so that improvement can be done in right direction
This document discusses research methodology and sampling techniques. It defines key terms like population, sample, census, and probability and non-probability sampling. It describes different sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and their advantages and disadvantages. Finally, it discusses issues around internet sampling and methods like using web site visitors, panels, and opt-in lists.
A hypothesis is an assumption or proposed explanation made on limited information to guide further investigation. It provides the basis for investigation by establishing the direction of inquiry. A good hypothesis is specific, testable, and related to existing theory. There are two main types - crude hypotheses indicate what data to collect, while refined hypotheses state empirical relationships. Hypotheses come in descriptive, explanatory, null, and alternative forms and serve important functions such as explaining facts, directing inquiry, and enabling deductions.
The document discusses research hypotheses and defining variables. It explains that a research hypothesis is an educated guess that can be tested and measured. There are two types of hypotheses: the null hypothesis, which denies a relationship, and the alternative hypothesis, which affirms a relationship. Good hypotheses are testable, logical, related to the problem, and represent a single concept. Research variables are qualities that can change or vary, and must be measurable. Variables can be independent or dependent, quantitative or qualitative, continuous or categorical. The definitions of variables should include both a conceptual definition and an operational definition for the study.
The presentation discusses null and alternative hypotheses. The null hypothesis expresses no difference or inequality between variables, while the alternative hypothesis expresses a difference or conflict. The null hypothesis is what researchers expect will definitely happen, while the alternative hypothesis is what researchers want to test. Examples are provided of null hypotheses stating children who eat oily fish do not show higher IQ increases than others, and that extroverts and introverts are equally healthy. The alternative hypotheses are that children eating oily fish will show higher IQ increases, and that introverts are not healthier than extroverts.
This document provides an overview of survey research, including survey design, objectives, advantages, disadvantages, and methods of data collection. It discusses determining the information required and target respondents for a survey. Common data collection methods described are personal interviews, telephone interviews, mail surveys, and online surveys. Key factors in choosing a method include the target population, accessibility, and costs. Both structured and unstructured interview techniques are covered.
This document discusses different types of validity in testing:
1. Content validity refers to how well a test measures the specific construct it aims to assess. A test needs to be related to the relevant class content.
2. Criterion-related validity is the degree of agreement between a test and an independent, reliable standard. There are two types: concurrent and predictive validity.
3. Construct validity provides evidence that test items measure the intended underlying abilities. Think-aloud and retrospection methods can provide evidence of construct validity.
Validity in scoring and face validity are also discussed. To improve validity, test specifications and a representative sample of content should be used, and scoring should directly relate to what
This document is a project report on consumer behavior regarding various branded shoes. It includes an introduction, objectives of the research, research methodology, limitations of the study, company profiles, data analysis and interpretation, suggestions and conclusions, annexure and bibliography. The research aims to understand consumer preferences between different branded shoes like Reebok, Nike and Adidas. A sample size of 50 respondents was selected from Ludhiana through random sampling and primary data was collected using questionnaires and interviews. The report provides insights into consumer decision making factors and brand perceptions.
There are two main types of sampling: probability sampling and non-probability sampling. Probability sampling involves methods where the probability of selection of each individual is known, such as simple random sampling, systematic random sampling, stratified random sampling, and cluster random sampling. Simple random sampling involves selecting a sample that gives each individual an equal chance of being selected by identifying the population, determining sample size, listing all population members, assigning them numbers, selecting numbers at random from a table, and including individuals in the sample if their number is selected. The advantages are it is easy to conduct and requires minimum population knowledge, while disadvantages include needing all population member names and potential over or under representation.
This document discusses the importance and process of interpreting numerical data and research findings. Interpretation is making sense of collected and analyzed data through statistical analysis and inferential statistics. It involves drawing inferences to understand underlying relationships and extend findings beyond the current study. Correct interpretation establishes connections between current results and past research, and develops explanatory concepts to guide future studies. The researcher must consider all relevant factors, remain cautious of errors, and avoid broad generalizations when interpreting results.
The document defines and discusses hypotheses in research contexts. It provides that a hypothesis is a formal, testable statement of the expected relationship between independent and dependent variables. The document outlines several definitions of a hypothesis provided by authors and discusses the key characteristics of a good hypothesis. It also differentiates between different types of hypotheses such as universal, existential, null, alternate, non-directional, directional, and research hypotheses. The purpose, components, and process of hypothesis making and testing are described.
The document provides guidelines for writing a research report. It discusses the various sections included in a research report such as the introduction, literature review, methodology, results, analysis, conclusions, and appendices. It also compares the differences between a technical research report aimed at experts and a popular research report aimed at a general audience. Key differences include technical reports emphasizing methods and data while popular reports focus on practical findings and recommendations.
This document discusses confidence intervals, which provide a range of values that is likely to include an unknown population parameter based on a sample statistic. It defines key concepts like confidence level, confidence limits, and factors that determine how to set the confidence interval like sample size, population variability, and precision of values. It explains how larger sample sizes and more precise measurements result in narrower confidence intervals. Applications to clinical trials are discussed, showing how sample size impacts the ability to make definitive recommendations based on trial results.
This document provides information on hypotheses in research. It defines a hypothesis as a tentative statement proposed to explain certain facts or observations. A hypothesis should be specific, testable, and stated in advance of data collection. Hypotheses can be categorized as null or research hypotheses based on their formulation. They can also be directional or non-directional based on whether they specify an expected direction of results. Hypotheses are either deductively or inductively derived depending on whether they are tested top-down from existing theory or built bottom-up from observations.
Writing introduction, hypothesis and objectives of a thesis and scientific pa...Md. Nazrul Islam
The document provides guidance on writing the introduction, hypotheses, and objectives for a thesis or scientific paper. It discusses including an introduction that interests readers and establishes the research problem and context. Hypotheses should make tentative predictions about variable relationships. Objectives should be specific, achievable, and measurable. The introduction identifies the problem and significance while assumptions and limitations acknowledge research constraints.
This document discusses hypotheses in research. It defines a hypothesis as a tentative statement about the relationship between two or more variables that can be tested. The key types of hypotheses discussed are simple vs complex, directional vs nondirectional, null vs research, and associative vs causal. Good characteristics of a hypothesis include being testable, consistent with existing knowledge, and having conceptual clarity. Hypotheses can be generated from theoretical frameworks, previous research, literature reviews, and real-life experiences. The steps of hypothesis testing include setting the null and research hypotheses, determining the test statistic and significance level, calculating the test statistic, and making a decision to accept or reject the null hypothesis based on the critical value. Type 1 and Type 2
There are several considerations when selecting a research topic, including academic/intellectual factors and practical applicability. Students may choose from assigned topics, field study topics using various resources, or free choice topics based on their own interests. Key factors in topic selection include the researcher's ability to study the topic thoroughly, available resources and techniques, and the topic's relevance to existing theories. Formulating a research problem involves discovering an issue in need of study and narrowing it to a manageable size. Developing testable hypotheses, clearly defining concepts, and establishing operational definitions allows relating findings to broader knowledge.
Here is the refined hypothesis based on the last homework:
If temperature affects leaf color change, then exposing maple tree leaves to temperatures below 10°C for a period of 2 weeks will result in the leaves changing color from green to shades of red, orange, and yellow earlier than maple tree leaves not exposed to low temperatures.
The independent variable is temperature, and the dependent variable is the timing of leaf color change. The hypothesis predicts that exposing leaves to low temperatures (below 10°C) for 2 weeks will cause them to change color earlier than leaves not exposed to low temperatures.
This document discusses research methodology and the concept of hypotheses. It defines a hypothesis as a tentative statement about a problem's solution that can be empirically tested. The document outlines the key characteristics of hypotheses, including that they are conceptual, declarative statements that reference empirical variables and have a future orientation toward verification. Hypotheses are important as they focus research, guide the investigator, and prevent blind searches for data. Different types of hypotheses are discussed, including question, declarative statement, directional statement, and null forms.
The document discusses different categories of research including:
- By type (primary vs secondary research)
- By objective (qualitative, quantitative, mixed methods)
- By form (exploratory, constructive, empirical research)
- By reasoning (deductive vs inductive reasoning)
It also briefly outlines four main research paradigms: postpositivism, social constructivism, advocacy/participatory, and pragmatism.
Research Methodology Course - Unit 2a . pptsvarsastry
This document provides an overview of research methodology and design. It defines research as a systematic investigation to establish facts. Research design refers to the systematic planning of a research study and aims to achieve research goals. Good research design has several key characteristics - it is theory-grounded, feasible, efficient, and flexible. The main components of research design are the title, problem statement, objectives, variables, hypotheses, sampling, and data collection and analysis. Experimental and non-experimental are the two main types of research designs. Hypotheses help guide the research by offering testable explanations of relationships between variables.
This document provides an overview of hypotheses in research methodology. It defines a hypothesis as a tentative explanation or educated guess about a research problem or outcome. There are several types of hypotheses, including research hypotheses (simple or complex), directional vs. non-directional, associative vs. causal, statistical vs. null hypotheses. Variables are also defined, including independent and dependent variables. Formulating a strong hypothesis requires an understanding of the topic area and existing research findings. Overall, hypotheses help focus research and provide a framework for analyzing results.
The document discusses various topics related to research methodology including definitions of research, types of research, research methods, sampling techniques, data collection methods, and experimental research. Some key points:
- Research is defined as a systematic effort to gain new knowledge through objective and scientific methods. It involves identifying a problem, formulating a hypothesis, collecting and analyzing data, and reporting findings.
- There are different types of research including descriptive, analytical, applied, fundamental, quantitative, qualitative, and more. Research methods can be quantitative, qualitative, experimental, case study, etc.
- Important steps in research include formulating the problem, literature review, developing hypotheses, research design, sampling, data collection, analysis, testing hypotheses,
This document discusses different types of hypotheses that can be formulated in research. It defines what a hypothesis is and explains its importance in focusing a study and guiding data collection. The document outlines characteristics of effective hypotheses and discusses how to formulate hypotheses based on background knowledge, intellect, and analogy. It also describes different forms hypotheses can take, including directional, non-directional, declarative, null, and question forms. Finally, it defines simple, complex, working, alternative, statistical, and logical hypotheses.
Research is defined as a systematic effort to gain new knowledge. It involves formulating a research problem, conducting a literature review, developing hypotheses, designing a study, collecting and analyzing data, and reporting findings. The goal of research is to discover answers to questions through objective and systematic methods of finding solutions.
This document discusses hypotheses in research studies. It defines a hypothesis and explains their purposes, including guiding research design and statistical analysis. Hypotheses can be classified in various ways, such as simple vs complex, directional vs nondirectional, and causal vs associative. The null hypothesis predicts no relationship while the research hypothesis states an expected relationship. Guidelines are provided for developing testable hypotheses and critiquing them in research reports.
This document discusses hypotheses, including their meaning, definition, characteristics, categories, and how they are formed and tested. A hypothesis is defined as a tentative explanation for an observed phenomenon that can be tested by further investigation. Hypotheses can be categorized based on their formulation as null or alternative, based on direction as directional or non-directional, and based on their derivation as inductive or deductive. Well-formed hypotheses should be testable, falsifiable, precise, and relate variables in a relationship that can be investigated. The document also outlines guidelines for developing a hypothesis, such as ensuring it relates to the research problem and can be investigated through further study.
This document provides an overview and guidelines for developing different chapters of a research paper, including the introduction, literature review, and methodology sections.
The introduction chapter should include a rationale explaining the need for the study and a problem statement clearly outlining the research problem or question. It also defines any important terms and states the purpose and significance of the study.
The literature review chapter summarizes and critiques previous research relevant to the topic. It is organized by topic and presents related literature and studies in a logical order.
The methodology chapter describes the research methods and procedures used in the study, including the research design, environment/location of the study, population and sampling techniques, data collection instruments and procedures, and methods
Research problem, hypothesis & conceptual frameworkMeghana Sudhir
The document summarizes key aspects of developing a research problem, hypothesis, and conceptual framework. It discusses sources of research problems, steps in developing and refining problems, criteria for evaluating problems, and how to write problem statements. It also covers developing hypotheses, including types of hypotheses and hypothesis testing. Finally, it discusses operational definitions, conceptual frameworks, and examples of theories frequently used in nursing research conceptual frameworks.
Research problem, hypothesis & conceptual frameworkMeghana Sudhir
The document discusses sources of research problems, developing hypotheses, and conceptual frameworks. It provides the following key points:
1. Research problems can come from experience, literature, or existing theories. Developing a problem involves selecting a topic and narrowing it.
2. Hypotheses predict relationships between variables and can be inductive, deductive, simple or complex. They are tested statistically but never proven.
3. Conceptual frameworks organize ideas and provide guidance for research. Theories summarize phenomena and help make findings meaningful and generalizable.
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8
More Components: Knowledge, Literature, Intellectual Projects
Keywords
action; critical evaluation; instrumentalism; intellectual projects; knowledge; literature; policy; practice; reflexive action; research; theory; understanding; value stances
In the last two chapters, we first introduced the idea of a mental map for navigating the literature plus the tools for thinking that represent the key to this map. We then looked at the first map component: the two dimensions of variation amongst knowledge claims. Here we complete our introduction to the mental map by describing its other three components:
three
kinds of knowledge
that are generated by reflecting on, investigating and taking action in the social world;
four
types of literature
that inform understanding and practice;
five
sorts of intellectual project
that generate literature about the social world.
Figure 8.1 Tools for thinking and the creation of three kinds of knowledge about the social world
Three kinds of knowledge
The three kinds of knowledge that we distinguish are
theoretical
,
research
and
practice
. We describe each below and show how they relate to the set of tools for thinking summarized in
Chapter 6
.
Figure 8.1
represents that relationship, showing that the tools for thinking play a central role. They are employed both to generate and to question the three kinds of knowledge.
What is theoretical knowledge?
The tools for thinking are most obviously reflected in
theoretical knowledge
– you cannot have a theory without a set of connected concepts. We define theoretical knowledge as deriving from the creation or use of theory, in the following way. On the basis of a theory about the social world, we make claims to knowledge about what the social world is like. The theory itself may or may not be our own and will have been developed on the basis of patterns discerned in that social world, whether through general observation (armchair theorizing), through specific investigations (empirically based theorizing) or a mixture of the two.
For example, in order to provide warranting for the claim that all children should be given the chance to learn a foreign language before the age of eight, an author might offer as evidence the theoretical knowledge that there is a ‘critical period’ for language acquisition. The theory upon which the author is drawing for this knowledge has been built up over the years by various theorists (beginning with Eric Lenneberg). The theorists have used both general observation about what happens when people of different ages learn a language and a range of empirical studies that have sought to establish what the critical age and determining factors are. Bundled up in the theory are potential claims about roles for biology, environment and motivation. The author would need to unpack these roles if the fundamental claim were to be developed into an empirical research study (to see how well it worked to offer foreign langua ...
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
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Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
2. Hypothesis Prepared By:- Group 1 WagariRefu TeklewoinKassaye ZewduHakimu MeseretYohannes HunbelewGebreTsadik Michael Gezae
3. Learning Outcomes Upon completion of this program, we will be able to Explain the meaning and significance of hypothesis in scientific research Identify the types of hypotheses Illustrate why we need a hypothesis Identify and categorize research variables Create Operational Definitions Formulate a valid hypothesis Identify Characteristics of a good hypothesis Test the hypothesis
4. Presentation Content Brief summary on the Scientific Method Meaning of Hypothesis Meaning and Types of variables Characteristics of Hypothesis Categories of Hypothesis Forming a Hypothesis Testing a Hypothesis
5. The scientific Method Is an overarching perspective On how scientific investigations should proceed Consists of a set of research principles and methods that help researchers obtain valid results from their research studies
6. The scientific Method (Cont…) Researchers generally agree that the scientific method is composed of the following key elements An empirical approach, Observations, Questions, Hypotheses, Experiments, Analyses, Conclusions, and Replication
7. Research Questions & Hypothesis Hypothesis is the fourth element of the scientific method However, we may not use hypothesis for all types of research. In a qualitative study, inquirers state research questions, not objectives (i.e., specific goals for the research) or hypotheses (i.e., predictions that involve variables and statistical tests).
8. In qualitative research, the research questions assume two forms: a central question and associated sub questions The central question is a statement of the question being examined in the study in its most general form. so as to not limit the inquiry Research Questions & Hypothesis
9. Guidelines for writing broad, qualitative research questions: Ask one or two central questions followed by no more than five to seven sub-questions Relate the central question to the specific qualitative strategy of inquiry (like ethnography , phenomenology, etc) Begin the research questions with the words “what” or “how” to convey an open and emerging design Examples: How do women in a psychology doctoral program describe their decision to return to school? “What is it like for a mother to live with a teenage child who is dying of cancer?” Focus on a single phenomenon or concept
10. Guidelines (Cont…) Use exploratory verbs that convey the language of emerging design of research. These verbs tell the reader that the study will Discover (e.g., grounded theory) Seek to understand (e.g., ethnography) Explore a process (e.g., case study) Describe the experiences (e.g., phenomenology) Report the stories (e.g., narrative research) Use non-directional language Expect the research questions to evolve and to change during the study Use open-ended questions without reference to the literature or theory If the information is not redundant with the purpose statement, specify the participants and the research site for the study
11. Hypothesis Defined An educated guess A tentative point of view A proposition not yet tested A preliminary explanation A preliminary Postulate
12. Various Authors “A hypothesis is a conjectural statement of the relation between two or more variables”. (Kerlinger, 1956) “Hypotheses are single tentative guesses, good hunches – assumed for use in devising theory or planning experiments intended to be given a direct experimental test when possible”. (Eric Rogers, 1966) “Hypothesis is a formal statement that presents the expected relationship between an independent and dependent variable.”(Creswell, 1994) A hypothesis is a logical supposition, a reasonable guess, an educated conjecture. It provides a tentative explanation for a phenomenon under investigation." (Leedy and Ormrod, 2001).
13. Hypothesis vs Theory vs Fact A theory is a well-established principle that has been developed to explain some aspect of the natural world. A theory arises from repeated observation and testing and incorporates facts, laws, predictions, and tested hypotheses that are widely accepted. A hypothesis is a specific, testable prediction about what you expect to happen in your study. For example, a study designed to look at the relationship between study habits and test anxiety might have a hypothesis that states, “This study is designed to assess the hypothesis that students with better study habits will suffer less test anxiety.” Unless your study is exploratory in nature, your hypothesis should always explain what you expect to happen during the course of your experiment or research. While the terms are sometimes used interchangeably in general practice, the difference between a theory and a hypothesis is important when studying experimental design. Some important distinctions to note include: A theory predicts events in general terms, while a hypothesis makes a specific prediction about a specified set of circumstances. A theory has been extensively tested and is generally accepted, while a hypothesis is a speculative guess that has yet to be tested.
14. One common feature for facts, theories, and hypotheses in science is that they are all treated as fallible — the likelihood of error might vary greatly, but they are still regarded as something less than absolute truth.
15. Purpose Guides/gives direction to the study/investigation Defines Facts that are relevant and not relevant Suggests which form of research design is likely to be the most appropriate Provides a framework for organizing the conclusions of the findings Limits the research to specific area Offers explanations for the relationships between those variables that can be empirically tested Furnishes proof that the researcher has sufficient background knowledge to enable her/him to make suggestions in order to extend existing knowledge Structures the next phase in the investigation and therefore furnishes continuity to the examination of the problem
17. Forms of Hypothesis Hypotheses can take various forms, depending on the question being asked and the type of study being conducted Some hypotheses may simply describe how two things may be related. For example, correlational research In others the researcher might hypothesize that one variable causes a change in the other variable (causal relationship In their simplest forms, hypotheses are typically phrased as “if-then” statements
18. A Hypothesis must make a prediction must identify at least two variables should have an elucidating power should strive to furnish an acceptable explanation or accounting of a fact must be falsifiable meaning hypotheses must be capable of being refuted based on the results of the study must be formulated in simple, understandable terms should correspond with existing knowledge In general, a hypothesis needs to be unambiguous, specific, quantifiable, testable and generalizable.
19. Characteristics of a Testable Hypothesis 1. A Hypothesis must be conceptually clear - concepts should be clearly defined - the definitions should be commonly accepted - the definitions should be easily communicable 2. The hypothesis should have empirical reference - Variables in the hypothesis should be empirical realities - If they are not it would not be possible to make the observation and ultimately the test 3. The Hypothesis must be specific - Place, situation and operation
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22. Categorizing Hypotheses (Cont…) 1. Null Hypotheses and Alternate Hypotheses Null hypothesis always predicts that no differences between the groups being studied (e.g., experimental vs. control group) or no relationship between the variables being studied By contrast, the alternate hypothesis always predicts that there will be a difference between the groups being studied (or a relationship between the variables being studied)
23. Categorizing Hypotheses (Cont…) Alternate Hypothesis can further be classified as 2. Directional Hypothesis and Non-directional Hypothesis
24. Categorizing Hypotheses (Cont…) 2. Directional Hypothesis and Non-directional Hypothesis Simply based on the wording of the hypotheses we can tell the difference between directional and non-directional If the hypothesis simply predicts that there will be a difference between the two groups, then it is a non-directional hypothesis. It is non-directional because it predicts that there will be a difference but does not specify how the groups will differ. If, however, the hypothesis uses so-called comparison terms, such as “greater,”“less,”“better,” or “worse,” then it is a directional hypothesis. It is directional because it predicts that there will be a difference between the two groups and it specifies how the two groups will differ
25. Categorizing Hypotheses (Cont…) 3. Inductive and Deductive Hypotheses(Theory Building and Theory Testing) classified in terms of how they were derived: - Inductive hypothesis - a generalization based on observation - Deductive hypothesis - derived from theory
26. Forming/Developing a Hypothesis Articulating the hypotheses that will be tested is one of the steps in the planning phase of a research study A hypothesis is formulated after the problem has been stated and the literature study has been conducted It is formulated when the researcher is totally aware of the theoretical and empirical background to the problem
27. The Initial Idea The initial idea is the starting point Often vague or general, it requires refining before research hypotheses can be generated Refinement of the initial idea is based on (1) a search of relevant research literature (2) initial observations of the phenomenon Narrow and formalize the initial idea into a statement of the problem
28. Statement of the Problem In the form of a question that clearly indicates an expected relationship The nature of the question will dictate the required level of constraint of a study Causal questions will require experimental research Questions about relationships can be answered with lower constraint research Convert into research hypothesis by operationally defining the variables
29. In General Ideas lead to observations library research Then Statement of problem and Then Problem statements become research hypotheses when constructs are operationalized
30. Operational Definitions The procedures used to measure and/or manipulate a variable Most variables can be operationally defined in many different ways, Thus creating many different research hypotheses from a single statement of a problem
31. Hypotheses States clearly the expected relationship between the variables The form is a declarative statement, but it is a tentative statement to be tested in research Implicitly or explicitly, the variables in the research hypothesis are stated in operational definition terms
32. The Role of Theory In research planning, theory guides the process Theory is often the primary source of research hypotheses Theory guides the selection of variables as well as their operational definitions Most research is based on multiple, overlapping and interacting theories
33. Variables Any factor that can take on different values is a scientific variable and influences the outcome of a research. Examples include Gender, Colour, Country Weight, Time, Height, etc..
34. Types of Variables There are many categories of variables Independent vs. Dependent vs. Controlled Variables Categorical vs. Continuous Variables Quantitative vs. Qualitative Variables
35. Independent vs. Dependent vs. Controlled Variables The independent variable is called “independent” because it is independent of the outcome being measured. It is what causes or influences the outcome. The dependent variable is influenced by the independent variable. Controlled variables are variables that the scientist does not want to change during the course of the experiment Hence the research includes finding ways to vary the independent variable Finding ways to keep the controlled variables from changing and measure the dependent variable
36. Categorical vs. Continuous Variables Categorical variables are variables that can take on specific values only within a defined range of values like gender, marital status consisting of discrete, mutually exclusive categories, such as “male/female,” “White/Black,” etc Continuous variables are variables that can theoretically take on any value along a continuum like age, income weight, height etc.. When compared with categorical variables, continuous variables can be measured with a greater degree of precision. The choice of which statistical tests will be used to analyze the data is partially dependent on whether the researcher uses categorical or continuous variables. Certain statistical tests are appropriate for categorical variables, while other statistical tests are appropriate for continuous variables. As with many decisions in the research-planning process, the choice of which type of variable to use is partially dependent on the question that the researcher is attempting to answer.
37. Quantitative vs. Qualitative Variables Qualitative variables are variables that vary in kind, like “attractive” or “not attractive,” “helpful” or “not helpful,” or “consistent” or “not consistent” Quantitative variables are those that vary in amount like height, weight, salary etc
38. Summary - Hypothesis Formation First identify a general area of interest to be researched; Example: effects of smoking on health Then identify a research question – the research question should be more narrowly defined (more specific) than the general research topic. Example: “Does smoking cause lung cancer?” Then operationally define the variables. The researcher is in control of the independent variable in the experiment. The dependant variable, however, is merely observed in the context of the experiment. For an experiment to be valid, it must contain at least two variables. Now it is time to formulate the hypothesis in an attempt to answer the question by making it a conditional statement like "Smoking may cause lung cancer.” Refine it by writing a formalized hypothesis like "If smoking causes lung cancer, then individuals who smoke have a higher frequency of developing the disease." This type of "if-then" hypothesis is considered the most useful. Verify that the hypothesis includes a subject group. A subject group defines who or what the researcher is studying. In the example above, the subject group is the smokers.
39. Summary (Cont…) Verify that a treatment or exposure is included in the experiment. A treatment is literally what is being done to the subject group. In our example, the exposure is smoke or smoking. Prepare for an outcome measure, which is a measurement concerned with how the treatment is going to be assessed. The outcome measure in our smoking scenario is the frequency of smokers developing cancer in subject population. Understand your control group. The control group or placebo is a group similar to the subject group, but this group does not receive the treatment. It is a population that the subject group is compared to. In the smoking example, the control group is non-smokers. Remember: - Hypothesis can be adjusted/refined/changed as more information is gathered but before the actual examination/experiment is carried out.
40. Hypothesis Testing All hypothesis tests are conducted the same way. The researcher states a hypothesis to be tested, formulates an analysis plan, analyzes sample data according to the plan, and accepts or rejects the null hypothesis, based on results of the analysis.
41. Hypothesis Testing (Cont..) 1. State the hypotheses. Every hypothesis test requires the analyst to state a null and an alternative hypothesis. The hypotheses are stated in such a way that they are mutually exclusive. That is, if one is true, the other must be false; and vice versa. 2. Formulate an analysis plan. The analysis plan describes how to use sample data to accept or reject the null hypothesis. It should specify the following elements. Significance level. Often, researchers choose significance levels equal to 0.01, 0.05, or 0.10; but any value between 0 and 1 can be used. Test method. Typically, the test method involves a test statistic and a sampling distribution. Computed from sample data, the test statistic might be a mean score, proportion, difference between means, difference between proportions, z-score, t-score, chi-square, etc. Given a test statistic and its sampling distribution, a researcher can assess probabilities associated with the test statistic. If the test statistic probability is less than the significance level, the null hypothesis is rejected.
42. Analyze sample data. Using sample data perform computations called for in the analysis plan.Test statistic. When the null hypothesis involves a mean or proportion, use either of the following equations to compute the test statistic. Test statistic = (Statistic - Parameter) / (Standard deviation of statistic) Test statistic = (Statistic - Parameter) / (Standard error of statistic) where Parameter is the value appearing in the null hypothesis, and Statistic is the point estimate of Parameter. As part of the analysis, you may need to compute the standard deviation or standard error of the statistic. Previously, we presented common formulas for the standard deviation and standard error.When the parameter in the null hypothesis involves categorical data, you may use a chi-square statistic as the test statistic. Instructions for computing a chi-square test statistic are presented in the lesson on the chi-square goodness of fit test. P-value. The P-value is the probability of observing a sample statistic as extreme as the test statistic, assuming the null hypothesis is true. Hypothesis Testing (Cont..)
43. Hypothesis Testing When you want to make statements about a population, you usually draw samples How generalizable is the sample-based finding? Evidence has to be evaluated statistically before arriving at a conclusion regarding the hypothesis Depends on whether information is generated from the sample with fewer or larger observations
44. Steps in Hypothesis Testing Problem Definition Clearly state the null and alternate hypotheses. Choose the relevant test and the appropriate probability distribution Determine the degrees of freedom Determine the significance level Choose the critical value Compare test statistic and critical value Compute relevant test statistic Decide if one-or two-tailed test Does the test statistic fall in the critical region? No Do not reject null Yes Reject null
45. Basic Concepts of Hypothesis Testing The Null and Alternate hypothesis Choosing the relevant statistical test and appropriate probability distribution. Depends on - Size of the sample - Whether the population standard deviation is known or not Choosing the Critical Value. The three criteria used are - Significance Level - Degrees of Freedom - One or Two Tailed Test
46. Significance Level Indicates the percentage of sample means that is outside the cut-off limits (critical value) The higher the significance level () used for testing a hypothesis, the higher the probability of rejecting a null hypothesis when it is true (Type I error) Accepting a null hypothesis when it is false is called a Type II error and its probability is ()
47. Significance Level (Contd.) When choosing a level of significance, there is an inherent tradeoff between these two types of errors Power of hypothesis test (1 - ) A good test of hypothesis ought to reject a null hypothesis when it is false 1 - should be as high a value as possible
48. Degree of Freedom The number or bits of "free" or unconstrained data used in calculating a sample statistic or test statistic A sample mean (X) has `n' degree of freedom A sample variance (s2) has (n-1) degrees of freedom
49. One or Two-tail Test One-tailed Hypothesis Test Determines whether a particular population parameter is larger or smaller than some predefined value Uses one critical value of test statistic Two-tailed Hypothesis Test Determines the likelihood that a population parameter is within certain upper and lower bounds May use one or two critical values
51. Hypothesis Testing About a Single Mean Step-by-Step 1) Formulate Hypotheses 2) Select appropriate formula 3) Select significance level 4) Calculate z or t statistic 5) Calculate degrees of freedom (for t-test) 6) Obtain critical value from table 7) Make decision regarding the Null-hypothesis
52. Hypothesis Testing About a Single Mean - Example 1(2 tailed) Ho: = 5000 (hypothesized value of population) Ha: 5000 (alternative hypothesis) n = 100 = 4960 = 250 = 0.05 Rejection rule: if |zcalc| > z/2 then reject Ho.
53. Hypothesis Testing About a Single Mean - Example 2 Ho: = 1000 (hypothesized value of population) Ha: 1000 (alternative hypothesis) n = 12 = 1087.1 s = 191.6 = 0.01 Rejection rule: if |tcalc| > tdf, /2 then reject Ho.
54. Hypothesis Testing About a Single Mean - Example 3(1 tailed) Ho: 5000 (hypothesized value of population) Ha: < 5000 (alternative hypothesis) n = 50 = 4970 = 250 = 0.01 Rejection rule: if then reject Ho.
55. Hypothesis Test of Difference between Means Mayor of a city wants to see if males and females earn the same A random sample of 400 males and 576 females was taken and following was found
56. Hypothesis Test of Difference between Means The appropriate test depends on - whether samples are from related or unrelated samples - whether population standard deviations are known or not - if not, whether they can be assumed to be equal or not
57. Hypothesis Test of Difference between Means In salary example, the null hypothesis is Ho: 1- 2 =c (=0) Ha: 1- 2 c Since we have unrelated samples with known (for large samples, we can use sample SD as pop SD) but unequal ’s the standard error of difference in means is
58. Hypothesis Test of Difference between Means The calculated value of z is For =.01 and a two-tailed test, the Z-table value is 2.58 Since is greater than , the null hypothesis is rejected
59. Hypothesis Testing of Proportion Quality control dept of a light bulb company claims 95% of its products are defect free The CEO checks 225 bulbs and finds only 87% to be defect free Is the claim of 95% true at .05 level of significance ? So we have hypothesized values and sample values
60. Hypothesis Testing of Proportion The null hypothesis is Ho:p=0.95 The alternate hypothesis is Ha: p 0.95 First, calculate the standard error of the proportion using hypothesized values as Since np and nq are large, we can use the Z table. The appropriate z value is 1.96
61. Hypothesis Testing of Proportion The limits of the acceptance region are Since the sample proportion of 0.87 does not fall within the acceptance region, the CEO should reject the quality control department’s claim
62. Hypothesis Testing of Difference between Proportions Manager wants to see if John and Linda, two salespeople, have the same conversion He picks samples and finds that
63. Hypothesis Testing of Difference between Proportions Are their conversion rates different at 0.05 significance level? The null hypothesis is Ho: The alternate hypothesis is Ha: The best estimate of p (proportion of success) is also,
64. Hypothesis Testing of Difference between Proportions An estimate of the standard error of the difference of proportions is The z value can be calculated as The z value obtained from the table is 1.96 (for ). Thus, we fail to reject the null hypothesis
65. The Probability Values (P-value) Approach to Hypothesis Testing P-value provides researcher with alternative method of testing hypothesis without pre-specifying Largest level of significance at which we would not reject Ho
66. The Probability Values (P-value) Approach to Hypothesis Testing Difference Between Using and p-value Hypothesis testing with a pre-specified Researcher is trying to determine, "is the probability of what has been observed less than ?“ Reject or fail to reject Ho accordingly
67. The Probability Values (P-value) Approach to Hypothesis Testing Using the p-Value Researcher can determine "how unlikely is the result that has been observed?“ Decide whether to reject or fail to reject Ho without being bound by a pre-specified significance level In general, the smaller the p-value, the greater is the researcher's confidence in sample findings
68. The Probability Values (P-value) Approach to Hypothesis Testing: Example Ho: = 25 (hypothesized value of population) Ha: 25 (alternative hypothesis) n = 50 = 25.2 = 0.7 SE( )= = 0.1; Z= =2 From Z-table, prob Z >2 is 0.0228. As this is a 2-tailed test, the p-value is 2 0.228=.0456
69. The Probability Values (P-value) Approach to Hypothesis Testing Using the p-Value P-value is generally sensitive to sample size A large sample should yield a low p-value P-value can report the impact of the sample size on the reliability of the results
70. Relationship between C.I and Hypothesis Testing (Example 1) A direct mktr knows that average no of purchases per month in entire database is 5.6 By sampling ‘loyals’ he finds that their average is 6.1(i.e, =6.1) Is it merely a sampling accident? Ho: = 5.6 (hypothesized value of population) Ha: 5.6 (alternative hypothesis) n = 35 = 2.5
71. Relationship between C.I and Hypothesis Testing (Example 1) Std err =0.42 The appropriate Z for =.05 is 1.96 The Confidence Interval is = (4.78, 6.42) Since 6.1 falls in the interval, we cannot reject the null hypothesis
72. Confidence Intervals and Hypothesis Testing Hypothesis testing and Confidence Intervals are two sides of the same coin. t = = Interval estimate for
73. Relationship between C.I and Hypothesis Testing (Example 2) Revisit the first example we started with Test the performance of two lists in terms of response rates Sample (1,000) from the first list provides a response rate of 3.5% Sample (1,200) from the second list provides a response rate of 4.5% Do the two lists (population) really have a difference or is it an artifact of the sample?
74. Relationship between C.I and Hypothesis Testing (Example 2) C.I. of list 1: (0.035)+/- 1.96*(SE1) SE1 = Sqrt[(0.035*0.965)/1000]=0.006 C.I.1=(0.0232,0.0467) C.I. of list 2: (0.045)+/-1.96*(SE2) SE2=Sqrt[(0.045*0.955)/1200]=0.006 C.I.2 =(0.033,0.0568) What can we infer based on these confidence Intervals? Lack of sufficient evidence to infer that there is any difference between the response rates in the two samples.