Assignment given upon acceptance of task.

Slide 1 – 1

Statistics and Research Design Short Course

Prepared by: Dr. Henrique Regina, Ph.D.

Source: Jackson, Sherri L.. Research Methods and Statistics: A
critical thinking approach. Publisher: Cengage. Publication year:

2016

Spring 2021

WBU – Hawaii
Statistics Seminar

Slide 1 – 2

Psychology is not just about mental health.
Psychology is a very diverse discipline that
encompasses many areas of study. Areas of
study range from chemistry, physics, and
biology to sociology, anthropology, and
political science. To understand what
psychology is, it is important that you have an
appreciation of its diversity.

Slide 1 – 3

Psychobiology
Cognition
Human Development
Social Psychology
Psychotherapy

Popular Research Areas in Psychology

Slide 1 – 4

Sources of Knowledge

✔ Superstition
✔ Intuition
✔ Authority
✔ Tenacity
✔ Rationalism
✔ Empiricism
✔ Science
✔ Hypothesis

Slide 1 – 5

Critical Thinking

✔ Skeptic
✔ Systematic Empiricism

✔ Public Verifiable Knowledge
✔ Empirically Solvable Problems

✔ Pseudoscience

Slide 1 – 6

Goals of Scientific Research

✔ Description
✔ Prediction
✔ Explanation

Slide 1 – 7

Research Methods in Science

Descriptive (Qualitative)

✔ Observational
✔ Naturalistic Observation
✔ Laboratory Observation

Allow to describe a situation, however, they
do not allow to make accurate predictions

or to establish a cause-and-effect
relationship between variables.

Slide 1 – 8

Research Methods in Science

Predictive (Quantitative)

✔ Correlational
✔ Quasi-experimental

Allow researchers not only to
describe behaviors, but also to
predict cause-effect from one

variable to another.

Slide 1 – 9

Research Methods in Science

Explanatory (Quantitative)

✔ Experimental

✔ Independent variable & Dependent variable

✔ Control group & Experimental group

✔ Random assignment

✔ Controlled environment

Allows researchers not only to describe and predict, but
also to determine whether a cause-and-effect
relationship exists between the variables of interest by
eliminating alternative explanations with the utilization
of controls.

Slide 1 – 10

Descriptive Statistics
(non-experimental research)

✔ Observational methods

✔ Naturalistic & laboratory

✔ Qualitative Methods

✔ Case Studies

✔ Archived data

✔ Interviews

✔ Field Studies

✔ Survey Methods

Slide 1 – 11

Research Population

✔Population consists of all of the people about whom a
study is meant to generalize, whereas the sample
represents the subset of people from the population who
actually participate in the study. In almost all cases, it
isn’t feasible to survey the entire population. Instead, we
select a subgroup or sample from the population and
give the survey to them. When the sample is
representative of the population, we can be fairly
confident that the results we find based on the sample
also hold for the population. In other words, we can
generalize from the sample to the population.

Slide 1 – 12

Sample Population

✔Probability Sampling

✔ Random

✔ Stratified

✔ Cluster

✔Non-probability Sampling

✔ Convenience sampling

✔ Quota sampling

Slide 1 – 13

Scale of Measurement

✔Nominal
✔Ordinal
✔Interval
✔Ratio

Slide 1 – 14

Organizing Data

✔ Frequency Distribution

✔ Lowest to Highest

✔ Frequency

✔ Relative frequency

✔ Graphs

✔ Convenience sampling

✔ Quota sampling

Slide 1 – 15

Organizing Data (frequency example)

Slide 1 – 16

Organizing Data (frequency example)

Slide 1 – 17

Organizing Data
Graphs

✔ Graphs

✔ Bar Graphs

o Qualitative variable (Nominal scale)

✔ Histogram

o Quantitative variable (ordinal, interval, or ratio scale)

✔ Frequency Polygon

o Quantitative variable (ordinal, interval, or ratio scale)

Slide 1 – 18

Graphs (example)
HistogramBar Graph

Frequency
Polygon

Slide 1 – 19

Descriptive Statistics

✔ Measures of Central Tendency

✔ Mean

✔ Median

✔ Mode

Numerical measures that describe a distribution by
providing information on the central tendency of the
distribution, the width of the distribution, and the
distribution’s shape.

Slide 1 – 20

Descriptive Statistics

✔ Measures of Variation

✔ Range

✔ Standard deviation

✔ Average deviation

To assess the width of a distribution, we need a
measure of variation or dispersion. A measure of
variation indicates the degree to which scores are either
clustered or spread out in a distribution.

Slide 1 – 21

Descriptive Statistics
Variations of Normal Distributions

✔ Symmetric (normal curve)
✔ Kurtosis (how flat or peaked)
✔ Mesokurtic
✔ Leptokurtic
✔ Platykurtic

When a distribution of scores is fairly large (N > 30), it
often tends to approximate a pattern called a normal
distribution. When plotted as a frequency polygon, a
normal distribution forms a symmetrical, bell-shaped
pattern often called a normal curve.

Slide 1 – 22

Descriptive Statistics
Normal Distribution (example)

Slide 1 – 23

Descriptive Statistics
Kurtosis (examples)

Slide 1 – 24

Descriptive Statistics
Skewed Distribution (example)

✔ Positively Skewed
✔ Negatively Skewed

Slide 1 – 25

Descriptive Statistics
Making Sense of the Distribution

✔ Z-Scores (standard score)
A number that indicates how many standard deviation

units a raw score is from the mean of a distribution.

Slide 1 – 26

Descriptive Statistics
Making Sense of the Distribution

✔ Probability
The expected relative frequency

of a particular outcome.
Assuming the English score
was 91 and its z = 0.63, it
means the score was 0.63
standard deviation from the
mean. Looking at the table,
we link the z score to .2357
(mean and z) and .2643
(beyond z). The probability of
a score being higher than 91
was 26.43%, below 91 was
73.57%.

Slide 1 – 27

Rules of Probability

✔ Probability is the study of likelihood and uncertainty.
Most decisions that we make are probabilistic in
nature.

✔ Hypothesis testing is the process of determining
whether a hypothesis is supported by the results of a
research project.

Slide 1 – 28

Rules of Probability (example)

✔ How likely would it be for an individual to roll a 2 in
one roll of a die? 17%

✔ What is the probability of rolling an odd number in a
single roll of a die? 50%

Slide 1 – 29

Rules of Probability
Multiplication Rule

✔ States that the probability of a series of outcomes
occurring on successive trials is the product of their
individual probabilities, when the sequence of
outcomes is independent.

✔ If the probability of getting a 2 when rolling a die is
17%, then what is the probability of getting two 2s in
a row (or one 2 followed by a 4)? Answer: 0.17 x
0.17 = 0.0289 or 2.89%

Slide 1 – 30

Rules of Probability
Addition Rule

✔ States that the probability of one outcome or another
outcome occurring on a particular trial is the sum of
their individual probabilities, when the outcomes are
mutually exclusive.

✔ If the probability of getting a number when rolling a
die is 17%, then what is the probability of getting one
or another number between 1 and 6 (like 2 or 4)?
Answer: 0.17 + 0.17 = 0.34 or 34%

Slide 1 – 31

Applying the Rules of Probability in a
Normal Distribution

✔ Assume two distinct English exam scores (80 and 125)
were converted to z scores of −1.33 and 1.67. From the
z score table, we find that the probability of selecting a
student with a score of 80 or lower is .09175 (9.175%),
and the probability of selecting a student with a score of
125 or higher is .04745 (4.745%). We now apply the
multiplication rule to determine the probability of
selecting the first person followed by the second person.
Thus, we multiply the first probability by the second
probability, or .09175 x 0.04745 = .00435. Answer is
0.435%

Slide 1 – 32

Hypothesis Testing
✔ Research is usually designed to answer a specific question—for

example— Do science majors score higher on tests of intelligence
than students in the general population? The process of
determining whether this statement is supported by the results of
the research project is referred to as hypothesis testing.

✔ We therefore construct what is known as a null hypothesis (H0).
Whatever the research topic, the null hypothesis always predicts
that there is no difference between the groups being compared.
This is typically what the researcher does not expect to find.
Therefore, if the null hypothesis is not supported by statistically
significant results, then the original (alternative) hypothesis (H1) is
supported (accepted). Based on the example above, if scores are
equal, H0 is supported (accepted) and H1 is rejected. If the scores
are higher for science majors students, then H1 is supported
(accepted) and HO is rejected.

Slide 1 – 33

Hypothesis Testing –
Errors & Statistical Significance

✔ Type I error: An error in hypothesis testing in which the null
hypothesis is rejected when it is true (supposed to be
accepted).

✔ Type II error: An error in hypothesis testing in which there is
a failure to reject the null hypothesis when it is false
(supposed to be rejected).

✔ Statistical significance: An observed difference between two
descriptive statistics (such as means) that is unlikely to have
occurred by chance.

Slide 1 – 34

Prediction & Correlation

✔ Correlation coefficients not only describe the
relationship between variables; they also allow
you to make predictions from one variable to
another. Correlations between variables
indicate that when one variable is present at a
certain level, the other also tends to be
present at a certain level. The statement is
qualified by the phrase “tends to.” We are not
saying that a prediction is guaranteed or that
the relationship is causal—but simply that the
variables seem to occur together at specific
levels.

Slide 1 – 35

Regression Analysis
Advanced Correlational techniques

✔ A procedure that allows us to predict an individual’s score on
one variable based on knowing one or more other variables.

✔ For example, imagine that you are an admissions counselor at
a university, and you want to predict how well a prospective
student might do at your school based on both SAT scores
and high school GPA.

✔ Regression analysis allows you to make such predictions by
developing a regression equation.

Slide 1 – 36

Regression Analysis
Advanced Correlational techniques

✔ This regression line is shown it is the best-fitting straight line
drawn through the center of the scatterplot that indicates
the relationship between the variables height and weight for
this group of individuals.

Slide 1 – 37

Analysis of Variance
ANOVA

✔ An inferential statistical test for comparing the means of
three or more groups.

✔ For multiple-group designs in which interval-ratio data are
collected, the recommended statistical analysis is the ANOVA
(analysis of variance)—an inferential parametric statistical
test for comparing the means of three or more groups. As its
name indicates, this procedure allows us to analyze the
variance in a study.

Slide 1 – 38

Reliability & Validity

✔ Reliability is an indication of the consistency or stability of a
measuring instrument.

✔ Validity is a measure of the truthfulness of a measuring
instrument. It indicates whether the instrument measures
what it claims to measure.

✔ A measure should be both reliable and valid.

1. After reviewing the Sources of Knowledge slide 4, discussed (superstition and intuition, authority, tenacity, rationalism, empiricism, and science). What you have understood about these sources of knowledge? Are you able to provide an example of each? For example; breaking a mirror leads to 7 years of bad luck. The source of knowledge in this case is superstition. Please use your own words.

1. Many psychology students believe that they do not need to know about research methods because they plan to pursue careers in clinical/ counseling psychology. What argument can you provide against this view?

2. A prediction regarding the outcome of a study is a (an) ________ and an organized system of assumptions and principles that attempts to explain certain phenomena and how they are related is a(an) __________.
a. theory; hypothesis
b. hypothesis; theory
c. independent variable; dependent variable
d. dependent variable; independent variable
3. Ray was interested in the mating behavior of squirrels so he went into the field to observe them. Ray is using the method of research.
a. case study
b. laboratory observational
c. naturalistic observational
d. correlational

5. A bird bander captured seven ovenbirds in her mist net and found that the average length (from the tip of the bill to the end of the tail) was 5.86 inches. This evaluated group is a:

a. Sample
b. Population

6. A city planner studying pension costs determined that the average of the 42 city library employees was 54.2 years. This evaluated group is a:

a. Sample
b. Population

7. The Centers for Disease Control and Prevention (CDC) reports that for the year 2003, 181,646 women and 1,826 men were diagnosed with breast cancer in the U.S. Is this statement based on sample data or population data?

a. Sample data
b. Population data

8. Imagine that you want to study cell phone use by drivers. You decide to conduct an observational study of drivers by making observations at three locations—a busy intersection, an entrance/exit to a shopping mall parking lot, and a residential intersection. You are interested in the number of people who use cell phones while driving. How would you recommend conducting this study? How would you recommend collecting the data? What concerns do you need to take into consideration?

9. A political campaign worker wishes to conduct a poll to determine how her candidate is likely to fare in the upcoming state Senate election. What is the population from which she should choose her sample?

a. All citizens in her candidate’s district
b. All citizens in her candidate’s district who are 18 or older
c. All citizens in her candidate’s district who voted in the previous election
d. All citizens in her candidate’s district who are likely to vote in the election

10. A researcher wishes to determine the average number of text messages per month sent by high school students who have cell phones. Which sample most represents the population of interest?

a. Ask a random sample of 100 students how many text messages they send.
b. Ask a random sample of 100 students with cell phones how many text messages they send.
c. Interview 100 students in the mall who are seen talking on their cell phones.
d. Randomly select the cell phone records of 100 high school students.

11. A quality improvement technician samples every 500th bag of potato chips coming off the assembly line to test the chips for fat content. Identify the type of sampling.

a. Systematic
b. Cluster
c. Convenience
d. Stratified

12. The following data represent a distribution of speeds (in miles per hour) at which individuals were traveling on a highway. 64 80 64 70 76 79 67 72 65 73 68 65 67 65 70 62 67 68 65 64 Organize these data into a frequency distribution with frequency (f) and relative frequency (rf) columns.

13. Which type of figure should be used to represent the data in question 12 —a bar graph, histogram, or frequency polygon? Why? Draw the appropriate figure for these data.

14. Based on the Statistics short seminar PowerPoint presentation slide 26 and 27, if the Psychology exam score was 86 and its corresponding z was 0.760, what is the probability of an exam score to be higher than 86? What is the probability of an exam score being lower than 86? What was the exam score percentile rank?

15. Imagine we are playing die for fun.

a. What is the probability of getting the number 5?
b. What is the probability of getting the number 2, followed by the number 5?
c. What is the probability of getting the number 2 or the number 5?
d. What is the probability of getting a 2, followed by 5, and followed by a 3?

16. Strong correlation coefficient is to weak correlation coefficient as is to:

a. -1.00; +:1.00
b. -1.00; -.10
c. +1.00; -1.00
d. +.10; -1.00

17. Which of the following correlation coefficients represents the variables with the weakest degree of relationship?

a. +.89
b. -1.00
c. +.10
d. -.47

18. How Analysis of Variance (ANOVA) can be utilized in research?

19. Which of the following correlation coefficients represents the highest (best) reliability score?

a. +.10
b. –.95
c. +.83
d. d. .00

20. Which of the following represents the best operational (in terms of a variable that can be measured) definition of depression for reliability and validity purposes?

a. Depression is defined as that low feeling you get sometimes.
b. Depression is defined as what happens when a relationship ends.
c. Depression is defined as your score on a 50- item depression inventory.
d. Depression is defined as the number of boxes of tissues that you cry your way through.