res_session6_Statistics 20for 20teachers_class 20lecture_tammie_06

Session 6

Review

 Name 5 types of research.

 What are the 2 types of Research, by Philosophy?

 What are the 4 purposes of research?

 What are the Criteria for Research Project?

 What is a Population in terms of research?

 What is the purpose of a review of literature?

 Where can you go to do a review of literature?

 Why is sampling important?

Now Your Assignment

 The two articles to review…..

 Open ended…..not much direction--for a reason…

 Ask for a short summary, ½ to 1 page, of what you

can learn or tell me about the research.

 Last week we had discussed different kinds of

research, etc…..based just on that discussion

alone you might come up with some things to look

for associated with each article….

What can be learned from a

research article?

 Purpose of any research article is to share

results

– Conclusions & Recommendations

 Before you accept the conclusions and

recommendations of any research article

what do you need to know?

What Did You Learn From Your

Review of the Two Articles?

 What kind of study was this?

– Survey

– Experimental

– Historical

– Experimental

– Correlational

– Evaluation

– Naturalistic

 What was the purpose of the study?

– What were the research questions? Were they questions or hypotheses?

 What was the population?

– Was there a sample? Did the paper describe how the sample was taken?

– How big was the sample? Did it describe how they determine sample size

 Was it qualitative or quantitative? How can you tell?

 Did the author give a good intro to the problem?

 Describe the methods used?

 What statistics were used?

 How was the paper divided? What were the sections?

Article 1:

 What kind of study was this?

 What was the purpose of the study?

 What was the population?

 Was it qualitative or quantitative? How can you

tell?

 Did the author give a good intro to the problem?

 Describe the methods used?

 What statistics were used?

Article 2: Evaluation of a Livestock

Ethics Curriculum for High School Youth

 What kind of study was this? This used a quasi-experimental design

 What was the population? Agricultural Education students from Indiana high schools.

– Was there a sample? yes

– Did the paper describe how the sample was taken? yes

– How big was the sample? 305 students

– Did it describe how they determine sample size? no

 Was it qualitative or quantitative? Qualitative How can you tell? Answered questions like a survey

 What was the purpose of the study? Evaluate effectiveness of a livestock ethics curriculum developed for high

school students in Agricultural Education classes.

– Are participants aware of the principles involved in making ethical choices when faced with decisions in youth

livestock programs?

– Are participants able to determine whether certain practices at a youth livestock show are ethical or unethical?

– Will participants make ethical choices when faced with decisions in youth livestock programs as demonstrated by

real life case study analysis?

– Will demographics such as current grade in school, gender, years enrolled in 4-H, years enrolled in FFA, years

enrolled in beef, swine, sheep. Horse, dairy, and other livestock projects, or previous participation in a livestock

ethics curriculum; help explain the difference in pre and post-test scores amongst participants?

 Pre-test and post-test; given before and after the curriculum is taught.

 Describe the methods used?

 Stats used? Descriptive.

 What were the parts of the article?

Article 3: Teacher Attrition Among Women

in Secondary Agricultural Education

 What kind of study was this? Mixed-method case study

 What was the population? Female students who took at least one

pre service course at Oklahoma State University between 1999 to 2004.

n=36; N=78

 Was it qualitative or quantitative? Qualitative, it says so.

 Purpose of the Study? To investigate female under-

representation in AGED through the lens of Grissmer & Kirby’s

theory of teacher attrition to better understand this phenomenon.

Res Questions

 Profile the women demographically.

 Analyze attrition trends of the students in the pre service program.

 Qualitatively explore women’s experiences in the AGED context.

 Methods? Interviews; semi-structured interview protocol.

 Stats? Descriptive

Article 4: A Study of Supervisor and Employee

Perceptions of Work Attitudes in Information

Age Manufacturing Industries.

 What kind of study was this? Experimental design

 What was the population? Employees of manufacturing industries in central Illinois area.

 Sample group? Cluster sampling (without replacement where each industry was treated as a cluster)

 -1209 for six industries

 -yes; n=N/(N(d)^2+1) where n = sample size, N= total population, d= level of significance (0.05)

 Was it qualitative or quantitative? Qualitative, used questionnaires

 What was the purpose of the study? To investigate (a) whether the type of job (i.e. information job

versus non-information job) was related to employee work attitudes. (b) if there existed any difference between

work attitudes as perceived by employees and as perceived by their supervisors, and (c) if there existed any

relationship between employee work attitudes and demographic variables such as age, gender, level of education,

and length of service.

– Hypotheses:

 H01: At the p 0.05 level of confidence, there is no significant difference between the self-perceptions of work attitudes of industrial

employees with information jobs and their work attitudes as rated by their supervisors.

 H02: At the p 0.05 level of confidence, there is no significant difference between the self-perceptions of work attitudes of industrial

employees with non-information jobs and their work attitudes of industrial jobs and their work attitudes as rated by their supervisors.

 H03: At the p 0.05 level of confidence, there is no significant difference between the perceptions of work attitudes of industrial

employees with non-informational jobs and industrial employees with information jobs.

 H04: At the p 0.05 level of confidence, there is no significant relationship between the work attitudes of information employees and the

variables of gender, age, level of education, and length of service.

 H05: At the p 0.05 level of confidence, there is no significant relationship between the work attitudes of non-

information

 Describe the methods used?

 Stats?

For Tonight

Statistics for Teachers



Based on:

Hyperstat Online and Learning Statistics Through

Playing Cards by Thomas R. Knapp (1996)

Adapted by: Tammie Pannells and David Agnew

Statistics

“If you can assign a number to it,

you can measure it”

Dr. W. Edward Demming





 Statistics

– refers to calculated quantities regardless of whether or

not they are from a sample

– is defined as a numerical quantity

– Often used incorrectly to refer to a range of techniques

and procedures for analyzing data, interpreting data,

displaying data, and making decisions based on data.

Because that is the basic learning outcomes of a

statistics course.



Stating the Problem

Developing a hypothesis :

– Methods: estimation and hypothesis testing.

 Estimation, the sample is used to estimate a

parameter and a confidence interval about the

estimate is constructed.

– Parameter: numerical quantity measuring some

aspect

– Confidence Interval: range of values that estimates a

parameter for a high proportion of the time

 Hypothesis Testing: the most common use

– Hypothesis: an intelligent guess or assumption that guides

the design of the study

– Null hypothesis: there is no difference or there is no effect

– Alternative hypothesis: there is a difference or there is an

effect

– Hypotheses: more than hypothesis, which are related to the

population

Inferential statistics

 Inferential statistics

– Infers or implies something about population from a

sample.

 Population: A total group

 Sample: A few from the whole group

 Representative sample: a sample that is equally

propionate to the population

 Random Sample: a sample that is chosen strictly by

chance is not “hand-picked”

– Probability: the percentage of change that an event will

occur

Variables

 A variable: any measured characteristic or attribute that differs for

different subjects.

 Two types:

– Quantitative: sometimes called "categorical variables.“

 measured on one of three scales:

– Ordinal: first second or third choice (most of the children

preferred red popsicles, and grape was the second choice)

– Interval: direct time periods between two events ( time it

takes a child to respond to a question)

– Ratio scale: compares the number of times one event

happens in comparison to another event. (example: the

number of time a black card is pulled in comparison to the

number of times a red card is pulled)

– Qualitative:

 measured on a nominal scale.

Variables

 Two categories:

 Independent

– Variables in an experiment or study which are

not easily to be manipulated without changing

the participants.

 Age, gender, year, classroom teacher, any

personal background data, etc

 Dependent

– Variables which are changed in an experiment

 Hours of sleep, amount of food, time given to

complete an activity, curriculum, instructional

method, etc.

Descriptive statistics

 Descriptive statistics

– summarize a collection of data in a clear and understandable way.

 Example: Scores of 500 children on all parts of a standardized test.

 Methods: numerical and graphical.

– Numerical: more precise- uses numbers as accurate measure

 mean the arithmetic average which is calculated by adding

a the scores or totals and then dividing by the number of

scores.

 standard deviation. These statistics convey information

about the average degree of shyness and the degree to

which people differ in shyness.

– Graphical: better for identifying patterns

 stem and leaf display : a graphical method of displaying

data to show how several data are aligned on a graph

 box plot. Graphical method to show what data are

included. The box stretches from the 25th percentile to the

the 75th percentile

 historgrams.

 Since the numerical and graphical approaches compliment each

other, it is wise to use both.

Data Analysis

 Explaining and interpreting the data:

– Data are plural

 You are looking at more than one number or group of numbers;

subject-verb agreement is important when writing.

 Central Tendency: measures of the location of the middle or the center

of the whole data base for a variable or group of variables

– Frequency: the number of times a number appears

– Mean: the arithmetic average

– Mode: the number that appears most often

– Median: the number in the middle when numbers are arranged by

value

– Skew: A distribution is skewed if one of its tails is longer than the

other. Data may be skewed positively or negatively.

 Standard deviation: the amount of variance between each sigma

Parameters or Parametric Data

 Parameter: a numerical Greek letters are used to designate

quantity measuring some parameters

aspect of a population of

scores. Quantity Parameter Statistic

– Parameters are usually

estimated by statistics

computed in samples Mean μ M





 Quantity Parameter Standard deviation σ s

Greek letters are

commonly accepted for

writing formulas Proportion π p



 Statistical symbols are

most common in Correlation ρ r

reporting actual data

analysis in reports or

articles.

Tools for Measuring

 Measurement is the assignment of numbers to objects or

events in a systematic fashion.

– Four levels:

 nominal: assigning items to groups or categories

– Examples: Classroom, color, size

 Ordinal: ordered in the sense that higher numbers represent higher

values

– Examples 1= freshmen, 2= sophomore

 Interval: one unit on the scale represents the same magnitude on the

trait or characteristic being measured across the whole range of the

scale.

– Interval scales do not have a "true" zero point,

 it is not possible to make statements about how many times higher

one score is than another.

 Ratio: represents the same magnitude on the trait or characteristic

being measured across the whole range of the scale.

– DO have true zero points

Research Techniques

 Types of hypothesis testing:

– T-test: comparing the mean of two groups

– ANOVA: Analysis of Variance – used to compare the

means of several variables

– Correlation: compares the relationship of two groups

– Chi Square of independence: explains if is a relationship

between the attributes of two variables.

– Linear regression: the prediction of one variable based

on another variable, when the relationship between the

variables is assumed to assumed to be linear.

References

 David M. Lance HyperStat Online Statistics Textbook

http://davidmlane.com/hyperstat/

 Knap, T. R. (1996). Learning Statistics Through Playing

Cards. SAGE publications London

 Sanocki, T. (2001). Student Friendly Statistics. PrenticeHall

Upper Saddle River NJ

 Fox, J. A. & Levin, J. ( 2005). Elementary Statistics in the

Criminal Justice Reseach The Essentials Pearson Boston

Review from last week, Answers

Criteria for Research

Project

 Universality -- can be completed by

anyone

 Replication -- can be repeated under

same conditions with same results

 Control -- use parameters to control as

many variables as possible

 Measurement -- important to quantify as

much as possible

Types of Research

-- by Method

 Experimental

 Correlational

 Evaluation

 Historical

 Naturalistic

 Survey

Types of Research

-- by Philosophy

 Quantitative -- (Positivistic)

– Things are meaningful only if we can verify

them with our five senses.



 Qualitative -- (Post-positivistic)

– All research is value-laden. Can’t remove

self from research.

What is the Purpose of

Research?

 Describe -- Ex: settings

 Predict -- Ex: success based on ACT

 Improve--Ex: teaching methods

 Explain -- answers “why?”