Here are the steps to interpret the output of an independent samples t-test:
1. Check the t-value and degrees of freedom (df) from the output.
2. Locate the critical t-value based on the df and your pre-determined significance level (usually 0.05) using a t-distribution table.
3. Compare your obtained t-value to the critical t-value. If the obtained t-value is equal to or greater than the critical t-value, then the null hypothesis can be rejected.
4. Check the p-value or significance value from the output. If it is less than your pre-determined significance level (usually 0.05), then the null
Measurement scales are used to categorize and/or quantify variables. This presentation describes the four scales of measurement that are commonly used in statistical analysis. This presentation explains the characteristics of nominal, ordinal, interval, and ratio scales with suitable illustrations.
Measurement scales are used to categorize and/or quantify variables. This presentation describes the four scales of measurement that are commonly used in statistical analysis. This presentation explains the characteristics of nominal, ordinal, interval, and ratio scales with suitable illustrations.
Causal Comparative Research At least two different groups are compared on a dependent variable or measure of performance (called the “effect”) because the independent variable (called the “cause”) has already occurred or cannot be manipulated. Dependent variable-the change or difference occurring as a result of the independent variable. Independent variable- an activity of characteristic believed to make a difference with respect to some behavior.
Dr. Lani discusses all aspects of the dissertation methodology, including: selecting a survey instrument, population, reliability, validity, data analysis plan, and IRB/URR considerations.
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.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
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A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
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Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
2. Types of categorical and quantifiable data
Data
Categorical Quantifiable
Nominal Ordinal Interval Ratio
3. What is categorical data?
• Data which cannot be quantified numerically
BUT
• Place into sets or categories (nominal data) or
ranked in some way (ordinal data)
4. What is quantifiable data
• Data can be measured numerically
• More precise
• Consist of interval data and ratio data
5. Four kinds of measurement scales
• Nominal
• Ordinal
• Interval
• Ratio
6. Nominal data
• A name value or category with no order or
ranking
Example:-
• Type of school
• Types of teaching method
• Gender
• Race
7. Ordinal data
• When categories are ordered, the variable is
known as an ordinal variable.
• Ordinal data tell us not only that things have
occurred, but also the order in which they
occurred.
8. Example
How of often you felt like insulting a student
(Please tick one)
• Every day
• Once a week
• Sometimes
• Never
9. Other examples of ordinal data
• Questions that rate the quality of students’
performance (for example, very good, good,
fair, poor)
• Agreements of attitude towards science
(Strongly agree, Agree, Disagree, Strongly
disagree)
10. Interval data
• Numerical values are assigned along an
interval scale with
• Equal intervals
• There is no zero point where the trait being
measured does not exist
11. Number of students scoring within
various ranges in IQ test
Scores Frequency
76-80 1
81-85 0
86-90 4
91-95 10
96-100 21
101-105 25
106-110 48
111-115 18
116-120 11
13. Ratio data
• Same characteristics with interval data
BUT
• There is an absolute zero that represent some
meaning
Example:-
Costs, sales, number of students, number of
teachers,
14. Types of categorical and quantifiable data
Data
Categorical Quantifiable
Nominal Ordinal Interval Ratio
15. Example of the scoring data
Students should be given an opportunity to
select a school of their choice
• Strongly agree _____
• Agree _____
• Disagree _____
• Strongly Disagree _____
16. A numeric score (or value) to each
response category
• Strongly agree 4
• Agree 3
• Disagree 2
• Strongly Disagree 1
17. Other example of scoring data
How of often you felt like insulting a student
(Please tick one)
• Every day
• Once a week
• Sometimes
• Never
18. A numeric score (or value) to each
response category
• Every day 4
• Once a week 3
• Sometimes 2
• Never 1
19. An example of multiple choice
question
• The quantity of charge which passes through
a circuit is measure in
A. Amps
B. Volts
C. Coulombs *
D. Watts
20. A numeric score (or value) to each
response category
• Correct response- 1 mark,
• Incorrect response- 0 mark
A. Amps 0
B. Volts 0
C. Coulombs 1
D. Watts 0
21. What is the level of measurement
of the following variables?
• The number of downloads of different bands’ songs on iTunes
• The names of the bands that were downloaded.
• The position in the iTunes download chart.
• The money earned by the bands from the downloads.
• The weight of bags brought by the passengers into the flight.
• The type of bags brought by the passengers into the flight.
• The phone numbers that the bands obtained because of their
fame.
• The gender of the people giving the bands their phone numbers.
• The instruments played by the band members.
• The time they had spent learning to play their instruments.
22. • Survey research (where we observe what
naturally goes on in the world without directly
interfering with it)
• Experimental research (where we manipulate
one variable to see its effect on another)
23. Two Main Types of Research
Variables
• Independent variableIndependent variable
• Dependent variableDependent variable
24. Independent variables
• An activity of characteristic believed to make a
difference with respect to some behavior
• Also known as experimental variable, cause,
treatment
25. Dependent variables
• The change or difference occurring as a result
of the independent variable
• Also known as criterion variable, effect,
outcome, posttest
26. Survey Research
• Survey research is the method of gathering
data from respondents thought to be
representative of some population, using
an instrument composed of closed
structure or open-ended items (questions).
• The goal is to describe some aspect or
characteristics (opinions, knowledge,
attitudes, beliefs) of a population.
27. Types of Surveys
• Cross-sectional
• Longitudinal
• Causal-comparative research
28. Cross-sectional
• Collects information from a sample of the
population at a specific point in time (“one
and done”)
• Information is collected at just one point in
time, even though it could take days to gather
all the data
29. Longitudinal
• Information is collected at different points
in time in order to study change over time
– Graduate students typically don’t do this
type of research because they want to
graduate
30. Causal-comparative
• To determine the cause for, or consequences
of, existing differencesexisting differences in groups of
individuals
• Also referred to as ‘ex post factoex post facto’ research
(Latin for ‘after the fact’) – retrospection
• Non-experimental research
31. Differences in causal-comparison
and experimental studies
• Causal-comparative studies
– the researcher cannot manipulate the
independent variable
• Experimental studies
– the researcher manipulates the independent
variable
35. Diagramming
The Experimental/ Quasi Experimental
Research
• To illustrate research designs, a number
of symbols are used
– X1 = Treatment
– X2 = Control Group
– O = Observation (pretest or posttest)
36. Single-Group Pretest-Treatment-
Posttest Design
R O X1 O
This means subjects are randomly
assigned to a group, which is then
given a pretest, then there is a
treatment, then there is a posttest.
This means subjects are randomly
assigned to a group, which is then
given a pretest, then there is a
treatment, then there is a posttest.
40. Quasi-Experimental Designs
X1 O
X2 O
X1 O
X2 O
The absence of R indicates there
is no random assignment.
Sometimes you will see a dotted
line between the two groups. This
indicates the two groups may not
be equivalent.
43. What is the purpose of inferential
statistics?
• To compare two or more groups on the
independent variable in terms of the
dependent variable ( for example: “Is there
a significant difference between boys and
girls on self esteem?”)
Independent variable: gender (boys and
girls
Dependent variable: self esteem
44. Inferential statistics involves
hypothesis testing
• Null hypothesis: There is no significance
difference between boys and girls on self
esteem
• Alternative hypothesis: There is a significant
difference between boys and girls on self
esteem
45. Other purpose of inferential statistics
• Relate two or more variables (for example:
Does self esteem relate to academic
achievement?)
• Null hypothesis: There is no significant
relationship between self esteem and
academic achievement
• Alternative hypothesis: There is a
significant relationship between self
esteem and academic achievement
46. Important Perspectives
• Inferential statistics
– Allow researchers to generalize to a population
of individuals based on information obtained
from a sample of those individuals
– Assess whether the results obtained from a
sample are the same as those that would have
been calculated for the entire population
47. Types of Inferential Statistics
• Two issues discussed
– Steps involved in testing for significance
– Types of tests
48. Steps in Statistical Testing
• State the null and alternative hypotheses
• Set alpha level
• Identify and compute the test statistic
• Compare the computed test statistic to the
criteria for significance
Objectives 20.1 – 20.9
49. Alpha Level
• An established probability level which serves
as the criterion to determine whether to
accept or reject the null hypothesis
• Common levels in education
– .01
– .05 (the most common)
– .10
50. The null hypothesis
• If the probability values (Alpha level) is
less than or equal to the significance
level, then reject the null hypothesis
• If the probability values is greater than
the significance level, then fail to reject
the null hypothesis
51. Examples of Statistical Test
• T-test (independent samples)
• Analysis of variance
• Analysis of covariance
• Multiple analysis of variance
• Multiple analysis of covariance
• Chi-Square
• Pearson product moment correlation
• Multiple regression
52. Examples of Statistical Test
• Spearman rank-order correlation
• Point biserial correlation
• Phi coefficient
53. Independent samples t-test
• Types of hypothesis/ question: Group
comparison
• Number of independent variables: 1
• Number of dependent variables: 1
• Number of covariates: 0
• Type of data for independent variable:
Categorical
• Type of data for dependent variable:
Continuous
54. One way analysis of variance
(ANOVA)
• Types of hypothesis/ question: Group
comparison
• Number of independent variables: 1
• Number of dependent variables: 1
• Number of covariates: 0
• Type of data for independent variable:
Categorical
• Type of data for dependent variable:
Continuous
55. Analysis of covariance (ANCOVA)
• Types of hypothesis/ question: Group
comparison
• Number of independent variables: 1 or more
• Number of dependent variables: 1
• Number of covariates: 1
• Type of data for independent variable:
Categorical
• Type of data for dependent variable:
Continuous
56. Multiple analysis of variance
(MANOVA)
• Types of hypothesis/ question: Group
comparison
• Number of independent variables: 1 or more
• Number of dependent variables: 2 or more
• Number of covariates: 0
• Type of data for independent variable:
Categorical
• Type of data for dependent variable:
Continous
57. Multiple analysis of covariance
(MANCOVA)
• Types of hypothesis/ question: Group
comparison
• Number of independent variables: 1 or more
• Number of dependent variables: 2 or more
• Number of covariates: 1 or more
• Type of data for independent variable:
Categorical
• Type of data for dependent variable:
Continuous
58. Pearson product moment correlation
• Types of hypothesis/ question: Relate
variables
• Number of variables: 2
• Number of covariates: 0
• Type of data for variables: One variable is
continuous and another variable is continuous
59. Multiple regression
• Types of hypothesis/ question: Relate
variables in form of prediction
• Number of independent variables: 2 or more
• Number of dependent variables: 1
• Number of covariates: 0
• Type of data for independent variable:
Continuous
• Type of data for dependent variable:
Continuous
62. Ask your self
• Can you match/decide
• Research problem with
• Research objective with
• Research hypothesis with
• Research methodology with
• Numbers and characteristics of the sample
with
• Types of statistical analysis
64. • Aim of the research: Compare the attitude
towards learning between boy and girl
• Research question:
Is there any significant difference between
boys and girls on attitude towards learning?
• The null hypothesis:
There is no significant difference between
boys and girls on attitude towards learning
• The alternative hypothesis:
There is a significant difference between boys
and girls on attitude towards learning
65. • Independent variable: 1(Gender)
• Dependent variable: 1(Attitude towards
learning)
• Type of data for independent variable:
Categorical
• Type of data for dependent variable:
Continuous
• Statistical test: ?
67. • Aim of the research: Compare the leadership
skills between principals from Smart School,
Boarding School and Cluster School
• Research question:
Is there any significant difference between
principals from Smart School, Boarding School
and Cluster School on leadership skills?
68. • The null hypothesis:
There is no significant difference between
principal between principals from Smart
School, Boarding School and Cluster School on
leadership skills
• The alternative hypothesis:
There is a significant difference between
principals from Smart School, Boarding School
and Cluster School on leadership skills
69. • Independent variable: 1(Type of school where
the principal works)
• Dependent variable: 1 (Leadership skills)
• Type of data for independent variable:
Categorical
• Type of data for dependent variable:
Continuous
• Statistical test: ?
71. • Aim of the research: Compare the level of
stress between male and female teachers
• Research question:
Is there any significant difference between
male and female teachers on stress?
• The null hypothesis:
There is no significant difference between
male and female teachers on stress
• The alternative hypothesis:
There is a significant difference between male
and female teachers on stress
72. • Independent variable: 1(Gender)
• Dependent variable: 1(Stress)
• Type of data for independent variable:
Categorical
• Type of data for dependent variable:
Continuous
• Statistical test: ?
74. • Aim of the research: To study whether
creative thinking skill or critical thinking skill is
the best predictor of academic achievement:
• Research question:
Which is the best predictor of academic
achievement: creative thinking skill or critical
thinking skill?
75. • The null hypothesis:
There is no significant contribution of all
predictor variables which are creative thinking
skill and critical thinking skill towards variation
in students’ academic achievement
• The alternative hypothesis:
There is a significant contribution of all
predictor variables which are creative thinking
skill and critical thinking skill towards variation
in students’ academic achievement
76. • Independent variable: 2 (Creative and critical
thinking skills)
• Dependent variable: 1(Academic
achievement)
• Type of data for independent variable:
Continuous
• Type of data for dependent variable:
Continuous
• Statistical test: ?
78. • Aim of the research: Compare the academic
achievement between students who learn
maths using the computer courseware and
students who learn maths without using the
computer courseware
• Research question:
Is there any significant difference between
students who learn maths using computer
courseware and students who learn maths
without using the computer courseware on
mean scores of math post test after the effect
mean scores of math pre test is controlled?
79. • The null hypothesis:
There is no significant difference between
students who learn maths using computer
courseware and students who learn maths
without using the computer courseware on
mean scores of math post test after the effect
mean scores of math pre test is controlled
• The alternative hypothesis:
There is a significant difference between
students who learn maths using computer
courseware and students who learn maths
without using the computer courseware on
mean scores of math post test after the effect
mean scores of math pre test is controlled
80. • Independent variable: 1 (Types of learning
method)
• Dependent variable: 1 (Mean scores of math
posttest)
• Covariate: Mean scores of math pretest
• Type of data for independent variable:
Categorical
• Type of data for dependent variable:
Continuous
• Statistical test: Analysis of Covariance
(ANCOVA)
83. • Is the movie Scream 2 scarier than the original
Scream?
• How scary the movie is will be measured by
heart rates (which indicate anxiety) during
both films
• Use independent samples t-test to answer the
research question
84. • Does listening to music while you work
improve your work?
• Get some people to write an essay while
listening to their favourite music, and then
another group of people write an essay when
working in silence (this is a control group).
• Compare the essay marks by using
independent samples t-test
85. Hands-on exercise
• There are 12 spider-phobes who were
exposed to a picture of a spider and 12
different spider-phobes who were exposed to
a real-life tarantula (the groups are coded
using the variable group)
• Their anxiety was measured in each condition
(anxiety).
86. Research Question
• Is there any significant difference on the
anxiety between spider-phobes who were
exposed to a picture of a spider and spider-
phobes who were exposed to a real-life
tarantula?
89. • For these data, Levene’s test is non-significant
(because p = .386, which is greater than .05)
and so we should read the test statistics in the
row labelled Equal variances assumed.
• Had Levene’s test been significant, then we
would have read the test statistics from the
row labelled Equal variances not assumed.
92. • Is there any significant difference on the
objective measure of libido between one
group of patients which receive a placebo,
one group of patients which receive a low
dose of Viagra and one group of patients
which receive a high dose of Viagra?
95. Analysis of Covariance
(ANCOVA)
extension of ANOVA that allows you
to explore differences between
groups while statistically controlling
for an additional (continuous)
variable
96. • Is there any significant difference on the
objective measure of libido between one
group of patients which receive a placebo,
one group of patients which receive a low
dose of Viagra and one group of patients
which receive a high dose of Viagra after the
effect of the partner’s libido is controlled?
103. 2 ways ANOVA
• Determine main effect on achievement for
method (determine there is a significant
difference between mean scores of Method A
and Method B)
104. 2 ways ANOVA
• Determine main effect on achievement for
ability (determine there is a significant
difference between mean scores of high and
low ability)
105. Interaction effect
• Is there a significant interaction effect
between method and ability on achievement?
106. How to understand there is an
interaction effect between method
(method A and method B) and
students ability (high and low?
107.
108.
109.
110.
111. Hands-on Exercise
• Does gender moderate the relationship
between age-group and optimism?
OR
• Is there any significant interaction effect
between age-group and gender on the
optimism?
114. • Multivariate analysis of variance (MANOVA) is
an extension of analysis of variance for use
when you have more than one dependent
variable.
• These dependent variables should be related
in some way, or there should be some
conceptual reason for considering them
together.
115. Example of research question
• Are males better adjusted than females in
terms of their positive and negative mood
states and levels of perceived stress?
OR
• Is the any significant difference on the linear
combination of positive mood, negative mood
and perceived stress between males and
females?
121. • An investigator is interested in studying how
exposure to different levels of temperature
influences problem-solving ability.
• Thus, each subject was required to solve a set
of mathematical problems under the four
temperature conditions of 35°C, 30°C, 25°C,
and 20°C.
122. • The investigator expected that fewer errors
will be made when the room temperature is
dropping by 5°C every 5 min
123. Research Question
• Is there any significant differences on the
respondents’ problem solving ability when
they were exposed repeatedly with different
levels of temperature?
126. Within-Subject Factor
Name
• TEMP to denote the name of the within-
subject factor
• In the Number of Levels field, type 4 to
denote the four levels of the within-subject
factor TEMP (Temp1, Temp2, Temp3, and
Temp4)
132. Step 1
• If the Mauchly’s Test of Sphericity is not
significant (i.e., the assumption about the
characteristics of the variance-covariance
matrix is not violated), the Tests of Within-
Subjects Effects can be used.
• If the Mauchly’s Test of Sphericity is
significant, the Multivariate Tests should be
used.
133. Step 2
• As the within-subjects variable of TEMP is
statistically significant, results from the
Repeated contrast can be interpreted to
determine which variables contributed to the
overall difference.
135. Pearson’s correlation
• Is there any significant relationship between
time spent revising and exam performance?
• Is there any significant relationship between
anxiety and exam performance?
137. Pearson’s correlation
• Exam performance is positively related to the
amount of time spent revising, with a
coefficient of r = .397, which is also significant
at p < .001.
• Finally, exam anxiety appears to be negatively
related to the time spent revising, r = −.709, p
< .001.
138. Pearson’s correlation
• As the time spent revising increases, exam
performance will increase.
• As anxiety increases, exam performance will
decrease.
140. Spearman’s correlation
• There is contest which require participants to
tell the biggest lie in the world
• There are 68 past contestants in this
competition
• They were placed in the competition (first,
second, third, etc.) and also gave them a
creativity questionnaire (maximum score 60).
141. Spearman’s correlation
• The position in the competition is an ordinal
variable because the places are categories but
have a meaningful order
148. • The value of R square is .335, which tells us
that advertising expenditure can account for
33.5% of the variation in record sales.
149.
150. • The regression model overall predicts record
sales significantly well.
151.
152. • Although this value is the slope of the
regression line, it is more useful to think of this
value as representing the change in the
outcome associated with a unit change in the
predictor.
• Therefore, if our predictor variable is increased
by one unit (if the advertising budget is
increased by 1), then our model predicts that
0.096 extra records will be sold.
155. Multiple Regression
• How well the Mastery scale and the Perceived
Control of Internal States Scale (PCOISS) are
able to predict scores on a measure of
perceived stress?
• OR
• Is there any significant contribution of all
predictor variables which are Mastery scale
and the Perceived Control of Internal States
Scale (PCOISS) towards variation in perceived
stress
156.
157. R Square
• Tells you how much of the variance in the
dependent variable (perceived stress) is
explained by the model (which includes the
variables of Total Mastery and Total PCOISS).
• The value is .466 means that our model (which
includes Mastery and PCOISS) explains 46.6
per cent of the variance in perceived stress.
158.
159. ANOVA
• The model in this example reaches statistical
significance (Sig. = .000; this really means
p<.0005).
160.
161. • Of these two variables, mastery makes the
largest unique contribution (beta = -.42),
although PCOISS also made a statistically
significant contribution (beta = -.36)
163. Hierarchical Multiple Regression
• Flexible as it allows the researcher to
determine the order of entry of the
independent variables into the regression
equation.
• The order of entry is normally dictated by
logical or theoretical considerations.
164. Research Question
• If we control for the possible effect of age and
socially desirable responding, is this set of
variables (total mastery and PCOSISS) still able
to predict a significant amount of the variance
in perceived stress?
167. Research question
• Does noble values mediate the relationship
between knowledge about environmental
conservation and intention to practice
environmental conservation?
168. • H02: The relationship between knowledge
about environmental conservation and
intention to practice environmental
conservation will not be significantly mediated
by noble values.
172. Step 3
• Regressing the dependent variable on both
the independent variable and on the
mediator.
173. To establish mediation, the following
conditions must hold:
• First, the independent variable must affect the
mediator in the first equation,
• Second, the independent variable must be
shown to affect the dependent variable in the
second equation,
• Third, the mediator must affect the
dependent variable in the third equation
174. • Complete mediation is indicated if the
previously significant independent variable
becomes non-significant in the final analysis.
• Partial mediation may occur when the
standardized regression coefficient of the
independent variable shows a reduction from
analyses of step 1 to step 3, but not to the
extent that it becomes non-significant.
176. History of moderation
• The technique comes out of ANOVA and
General Linear Model, in that multiple
regression is seen as an extension of analysis
of variance.
• Most people know of moderation from the
seminal Baron & Kenny article as well as a thin
paperback book by Leona Aiken and Stephen
West (1991) entitled Multiple regression:
Testing and interpreting interactions.
177. What is moderation?
• A moderating variable affects the
relationship of the IV on the DV.
• The moderator interacts with the IV to
predict outcome scores.
• Thus, certain levels of a moderator under
certain conditions of the IV might predict
different levels of the DV.
180. • The model diagrammed in Figure 1 has three
causal paths that feed into the outcome
variable of task performance: the impact of
predictor (Path a), the impact of as a
moderator (Path b), and the interaction or
product of these two (Path c).
181.
182. • The moderator hypothesis is supported if the
interaction (Path c) is significant.
• There may also be significant main effects for
the predictor and the moderator (Paths a and
b), but these are not directly relevant
conceptually to testing the moderator
hypothesis
183. • We speak of statistical interaction when a
relation between 2 variables (say X-Y) changes
as a function of a third variable (say Z).
• Note that the interactive effect is a
multiplicative effect, the effect of the product
oftwo IVs scores (i.e., XZ product).
184. Moderator
• It is a variable that changes the relationship
between an IV and a DV.
• A significant interaction between the
moderator and the IV means that the effect of
the IV on the DV changes depending on the
level of the moderator.
• We generally compare “high” levels of the
moderator (+1 standard deviation above the
mean) to “low” levels (-1 SD below the mean).
185. Mediators vs Moderators
• In mediation, the IV and the mediator are
associated (correlated), and the IV and the DV
are correlated, and there is an implied causal
path (“because”) that links the three variables.
• The IV causes the DV because the IV causes
the mediator which causes the DV.
186. Mediators vs Moderators
• In moderation (to get a significant
interaction), the IVs need not be correlated
with each other or with the DV.
• In moderation, the link between the IV and
the DV is different for high vs low levels of the
moderator. There is no because.
• It’s more like if-then contingencies: If there’s
high moderator, then the IV does this with the
DV, and if there’s low moderator, the IV does
this with the DV.
187. • The IV (self-esteem) impacts on grades (the
DV) but it’s moderated by motivation to study.
[At high motivation, there’s a link between
self-esteem and grades, but at low motivation,
there’s no link – everyone does badly.]