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Why is it important for students of behavioral research to understand statistics? It is
important to know when to use each procedure and how to interpret its answer. Students of
behavioral research need to understand statistical procedures to comprehend other people's
research. A variable is anything about a behavior or situation that, when measured, can produce
two or more different scores. A quantitative variable is a variable for which scores reflect the
amount of the variable that is present. A qualitative variable is a variable for which scores reflect
a quality or category that is present.
What is the difference in the pattern among the X and Y scores between (a) a perfectly consistent
relationship and (b) a less consistent (weaker) relationship?
a) a score is always paired with one and only one score on the other variable. This
makes for a very clear and obvious pattern.
b) The X and y scores are not consistent and are not paired to only one score on the
other variable.
The general purpose of experiments and correlational studies is to test theories and gather
data. The main difference between experiments and correlational studies is that in a correlational
study, the researcher does not control the variables; instead, they are used to investigate the
relationship between variables. Controlled experiments establish causality, whereas correlational
studies only show associations between variables. In an experimental design, you manipulate an
independent variable and measure its effect on a dependent variable. Other variables are
controlled so they can’t impact the results. In correlational studies a researcher looks for
associations among naturally occurring variables, whereas in experimental studies the researcher
introduces a change and then monitors its effects.
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In an experiment, the dependent variable is the variable that measures a behavior or attribute of
participants that we expect will be influenced by the independent variable.
Descriptive statistics aim to summarize and describe a chunk of raw data. They provide a quick
and easy understanding of a dataset without having to examine all the individual data values.
There are different methods to descriptive statistics. Summary statistics: These are single
numbers that summarize the data. Examples include measures of central tendency (such as the
mean and median) and measures of dispersion (such as the range, standard deviation, and
variance). Graphs: Visual representations like histograms, box plots, stem-and-leaf plots, and
scatter plots help us understand the data distribution. Tables: Frequency tables, for instance,
show how many data values fall within specific ranges. Example: Suppose we have test scores
for 1,000 students at a school. Descriptive statistics allow us to find the average score, visualize
the score distribution, and understand the spread of scores1.
Inferential statistics enable us to make predictions and decisions based on data. They use samples
to draw conclusions about larger populations. There are different methods of inferential statistics.
Hypothesis testing: Determines whether observed differences are statistically significant.
Confidence intervals: Provides a range of values within which population parameters likely fall.
Regression analysis: Helps predict outcomes based on relationships between variables. Sampling
techniques: Random sampling allows us to generalize findings to larger populations. Example: If
we want to know whether a new teaching method improves test scores across an entire school
district, we’d use inferential statistics to draw conclusions from a sample of students.
In summary, descriptive statistics describe data, while inferential statistics allow us to make
broader predictions and decisions based on that data. Both are essential in research, business
analytics, and data-driven decision-making.
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Define the four scales of measurement. Nominal, ordinal, interval, and ratio. (USNW, Online)
1. Nominal Scale:
oNominal scale is the simplest measurement scale used to label variables that have
no quantitative values.
oExamples of variables measured on a nominal scale include:
Gender (Male, female)
Eye color (Blue, green, brown)
Hair color (Blonde, black, brown, grey, other)
Blood type (O-, O+, A-, A+, B-, B+, AB-, AB+)
Political preference (Republican, Democrat, Independent)
Place of residence (City, suburbs, rural)
oProperties of nominal scale variables:
No natural order.
Categories are mutually exclusive.
Only counts and mode can be calculated.
oCommonly collected through surveys.
2. Ordinal Scale:
oOrdinal scale labels variables with a natural order but no quantifiable difference
between values.
oExamples of variables measured on an ordinal scale include:
Satisfaction (Very unsatisfied, unsatisfied, neutral, satisfied, very
satisfied)
Socioeconomic status (Low income, medium income, high income)
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Workplace status (Entry Analyst, Analyst I, Analyst II, Lead Analyst)
Degree of pain (Small amount of pain, medium amount of pain, high
amount of pain)
oProperties of ordinal scale variables:
Natural order exists.
Difference between values cannot be precisely evaluated.
3. Interval Scale:
oInterval scale labels variables with a natural order and equal intervals between
values.
oExamples of variables measured on an interval scale include:
Temperature (in Celsius or Fahrenheit)
IQ scores
oProperties of interval scale variables:
Equal intervals.
No true zero point (zero does not indicate absence).
4. Ratio Scale:
oRatio scale has all the properties of an interval scale, plus a true zero point.
oExamples of variables measured on a ratio scale include:
Height (in centimeters or inches)
Weight (in kilograms or pounds)
Age
oProperties of ratio scale variables:
Equal intervals.
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True zero point (zero indicates absence).
What are the two aspects of a study to consider when selecting the descriptive or inferential
statistics you should employ? Descriptive statistics are procedures for organizing and
summarizing sample data. Inferential statistics are used for procedures for determining whether
sample data accurately represent the relationship between the population.
Maria asked a sample of college students about their favorite beverage. Based on what the
majority said, she concluded that most college students prefer drinking carrot juice to other
beverages! What statistical argument can you give for not accepting this conclusion? This is just
a sample and not the whole population of all college students. Also, based on the lack of the draw
the participants may not have accurately shown and therefore are unrepresentable.
A relationship is the pattern between two variables where a change in one variable is
accompanied by a consistent change in the other. In a research study relationship determine the
ability of a variable to influence other variables. A sample is a small subset of population which
is intended to represent the population. The participants of a research study are the characters
measured in the sample. Population consists of all the units the researcher intends to draw
theories. Statistic is a number describing features of the scores in a sample data. A number
describing features of the scores in a population is called a parameter.
Which sample in problem 21 shows the most consistent relationship? How do you know?
Sample A shows a consistent relationship because one variable is always paired with one
and only one score on the other variable.
In an experiment study are directed to determine the effect in a continuous change in a
variable by a researcher. The experiment also determines whether variables are related or not.
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Experimental methods involve a hypothesis, forming, collecting information and suggestions to
prove the truth. In correlational studies a researcher looks for associations among naturally
occurring variables, whereas in experimental studies the researcher introduces a change and then
monitors its effects. (a) whether different amounts of caffeine consumed in 1 hour influence
speed of completing a complex task; Experimental since the research team has control over the
independent variable and other aspects of the experiment. (b) the relationship between number of
extracurricular activities and GPA; Correlational study since the researcher research team does
not have control over the variables in the study. (c) the relationship between the number of pairs
of sneakers owned and the person’s athleticism; Correlational study since correlational studies
look at the overall trends, not the minute details. (d) how attractive men rate a woman when she
is wearing one of three different types of perfume; Experimental since the research team has
control over the independent variable and other aspects of the experiment (e) the relationship
between GPA and the ability to pay off school loans; Correlational since the researcher simply
measures the data that she finds in the world. This allows the researcher to see if the two
variables are correlated -- whether changes in one are associated with changes in the other. (f) the
influence of different amounts of beer consumed on a person’s mood. Correlational since the
researcher would be identifying the variable and looks for a relationship between them.
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In my CJUS 740 I wrote my dissertation on Examining Job-Related Stress and Burnout
among Correctional Officers. To briefly explain the topic, due to the nature of their profession,
correctional officers face a multitude of stressors that are brought on through the years of having
to deal with complex, aggressive inmates and often the indifference of unsupportive supervisors.
Examining the causes and outcomes of job-related stress enables the identification of
problematic areas for correctional officers and suggests where resources and interventions may
be applicable. A correctional officer's job is to enforce sanctions and decrees of the courts,
detaining suspected offenders, regulating their activities both in and outside the jails, and
working towards protecting the community and reducing criminal offending. The role of a
correctional officer is highly stressful. It can be described as a complex and demanding blend of
work involving surveillance, security, controlling dangerous people, bureaucratic activities, and
dealing with personal trouble, all taking place in a volatile environment.
I can relate validity to my study by checking the consistency of results across time, across
different observers, and across parts of the test itself. Different correctional officers can be
observed on different shifts which would show the different time frames as well. I can relate
reliability to my study by checking how well the results correspond to established theories and
other measures of the same concept. By comparing the correctional officers in my facility of
choice versus an established theory it will be able to determine how well they are alike or
different. I do not believe it will be a problem because every correctional officer differs from one
another. Even on different shifts, which means different time frames, I will be able to see
different ways on how night shift will handle their stress differently than day shift. Night shift
doesn’t have readily available sources as day shift does.
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The way I intend to complete my data collection is first by getting permission from all
required personnel to be able to gain access to the local county jail that I’m employed with. I
know it will not be an issue, but permission is always more respectful. I will observe different
correctional officers throughout the facility since no one has the same job. Our county jail has
control officers who runs the entire facility, booking officers who manage all intake prisoners,
pod officers who manages all housed prisoners in their dorms, court officers who handles
prisoners in court, and utility officers who makes sure all prisoners have all their necessity as
well as making sure they are fed three times daily and deliver their commissary weekly.
I will sit with different shifts at different times of their shifts day and night and just
observe how they react from the beginning, middle, and towards the end of their shift and see
how they manage their stress throughout their entire shift. Our county jail is currently under staff
so it will be the perfect time to observe for the data I’ll collect. I’m sure the sheriff and jail
administrators will also love to see the data I collect to see how they can possibly improve the
work environment. I chose this study because I wish to one day be in the position of running the
jail as an administrator and collecting this data will help me in the future with what can be done
differently to better help the staff.
References:
Pallant, Julie (2020).SPSS Survival ManualG(7th ed.). Open University Press. ISBN:
9780335249497.
