1. Data Collection
Population
Sampling

2. Where we are now… Methods Data collection Analysis Interpretation
Observation Background survey
Broad area of research
Literature review
Problem definition
Problem statement
Research questions
Theoretical framework
Variables clearly identified
Hypothesis generation
Research design
Methods
Data collection
Analysis
Interpretation
Deduction
Hypothesis substantiated?
RQ answered?

3. Lesson objectives Review – sampling Collecting Quantitative data,
Collecting Qualitative data

4. Population
A population consists of an entire set of objects, observations, or scores that have something in common.
Example: All males between the ages of 15 and 18.
Other examples ?
Your population?

5. Population and sample A sample is a subset of a population
Not practical to study every member of the population, so we get “a sample” from the population
Sampling
Procedure by which some members of a given population are selected as representatives of the entire population

6. Parameters and Statistics
The distribution of a population can be described by several parameters such as the mean and standard deviation. Estimates of these parameters taken from a sample are called statistics.
Parameters
Numerical quantity measuring some aspect of the population
Statistics
Numerical quantity measuring some aspect of the sample
Quantitative

7. Parameters and statistics
Greek letters are used to designate parameters.. Parameters are rarely known and are usually estimated by statistics computed in samples.
Quantity Parameter Statistic
Mean μ M
Standard deviation σ s
Proportion π p
Correlation ρ r
Quantitative

8. Definition of sampling terms
Sampling unit
Subject under observation on which information is collected
Sampling fraction
Ratio between the sample size and the population size
Sampling frame
Any list of all the sampling units in the population
Sampling scheme
Method of selecting sampling units from sampling frame
Quantitative

9. What we need to know Concepts Calculations Representativeness
Sampling methods
Choice of the right design
Calculations
Sampling error
Design effect
Sample size
Quantitative

10. Sampling and representativeness
Target Population  Sampling Population  Sample
Sampling
Population
Sample
Target Population
Quantitative

11. Inferential statistics
Used to draw inferences about a population from a sample
Inferential statistics require samples to be
Random
Representative
Quantitative

12. Representativeness Person Place (ex : urban vs. rural) Time
Demographic characteristics (age, sex…)
Exposure/susceptibility
Place (ex : urban vs. rural)
Time
Seasonality
Day of the week
Time of the day
Ensure representativeness before starting, confirm once completed !!!!!!
Quantitative

13. Sampling error No sample is the exact mirror image of the population
Expressed by standard error
of mean, proportion, differences, etc
Function of
amount of variability in measuring factor of interest
sample size
Quantitative

14. Methods used in probability samples
Simple random sampling
Systematic sampling
Stratified sampling
Cluster sampling
Multistage sampling
Quantitative

15. Multiple stage sampling
Principle
= consecutive samplings
example : sampling unit = household
1rst stage : drawing areas or sections
2nd stage : drawing buildings, houses
3rd stage : drawing households
Quantitative 8

16. Systematic error (Bias) !
Quality of an estimate
No precision
Random error !
Precision but
no validity
Systematic error (Bias) !
Precision & validity 7

17. Select Participants: Choose a Sample Size
Select a sample size as large as possible from the individuals available
Select a sufficient number of participants for the statistical tests you will use
Calculate the sample size using a sample size formula
Quantitative

18. Select Participants: Choose a Sample Size
A rough estimate:
15 participants in each grp in an expt
30 participants for a correlational study
350 individuals for a survey study but depend of several factors
Quantitative

19. Calculating sample size
Krejcie and Morgan (1970)
Quantitative

20. EXAMPLE
Gender difference in vocational interest of post matriculation at UiTM
Independent variable:
Dependent variable:
Research Design:
Population:
Sample size:
Instrument:

21. Calculating sample size
Use web calculator

23. Non Probability Sampling

24. Types of Purposeful Sampling
When Does Sampling Occur?
After Data Collection
has started
What is the intent?
To develop
many
perspectives
Extreme
Case
Sampling
To describe
particularly
troublesome
or enlightening
cases
Typical
To describe what
is “typical” to
those unfamiliar
with the case
What is the intent?
To take advantage
of whatever case
unfolds
Opportunistic
To locate
people or
sites to study
Snowball
To explore
confirming or
disconfirming
Confirming/
Disconfirming
Maximal
Variation
To generate a theory or concept
Theory or Concept
To describe some
sub-group in depth
Homogenous
Before Data Collection

25. One may sample.. Maximal variation Extreme case
Most hardworking/ Highest achiever
Lowest achiever
Extreme case ??

26. Examples of Non-Probability Samples
Convenience Sampling: participants are selected because they are willing and available to be studied
Snowball Sampling: the researcher asks participants to identify other participants to become members of the sample.

27. Non Probability sampling example
Study delinquent behaviour during recess.
I selected 1 school out of 4 - Interviewed all 4 principals and toured all 4 schools. I chose school to which I was given most access with fewest restrictions. Also school that reported the widest variations in delinquent behaviour during recess (very high to virtually no display).
Then sampled different locations with camera to find most varied activity and least self-conscious/guarded behavior. Where?
Turned out to be behind the surau.
Later used snowball approach in choosing children to interview.

28. Your sample?

29. Collecting quantitative data

30. Flow of Activities in Collecting Data
Identify the variable
Operationally define the variable
Locate data (measures, observations, documents with questions and scales)
Collect data on instruments yielding numeric scores

31. Identify Data Options: Specify Variables
Independent Variables
Dependent Variables
Intervening
Moderating

32. Identify Data Options: Operationalize Variables
Operational Definition: The specification of how the variable will be defined and measured
typically based on the literature
often found in reports under “definition of terms”
Sometimes the researcher must construct it

33. Flow of Activities in Collecting Data
Example
Identify the variable
Operationally define the variable
Locate data (measures, observations, documents with questions and scales)
Collect data on instruments yielding numeric scores
Self-efficacy for learning from others
Level of confidence that an individual can learn something by being taught by others
13 items on a self-efficacy attitudinal scale from Bergin (1989)
Scores of each item ranged from 0-10 with 10 being “completely confident.”

34. Flow of Activities in Collecting Data
Example
Identify the variable
Operationally define the variable
Locate data (measures, observations, documents with questions and scales)
Collect data on instruments yielding numeric scores
Learning motivation of adult learners
Interest and level of engagement of an individual
----- items on a questionnaire developed
Scores of each item ranged from 1-5 with 5 being “most interested”

35. Scales of measurement

36. Identify Data Options: Select Scales of Measurement
Nominal (Categorical): categories that describe traits or characteristics
participants can check 
 Female  Male
Ordinal: participants rank the order of a characteristic, trait or attribute

37. Identify Data Options: Select Scales of Measurement
Interval: provides “continuous” response possibilities to questions with assumed equal distance ; scale with no true zero
Discrete (SD SA)
Metric (oC)
Ratio: a scale with a true zero and equal distances among units

38. Identify the level of measurement
Practice
Identify the level of measurement

39. Ratio ? Nominal? Ordinal? Interval? Measurement level? Age Religion
Gender
Income bracket
Test scores
CGPA
Frequency of asking questions
Time spent on task
Level of acceptance
(0 – never, 5 – all the time)
Activity
( 0 –not active, 5 – very active)
Ratio ?
Nominal?
Ordinal?
Interval?

40. Measurement level? Age Religion Gender Income bracket Test scores CGPA
Frequency of asking questions
Time spent on task
Level of acceptance
(0 – never, 5 – all the time)
Activity
( 0 –not active, 5 – very active)
Ratio
Nominal
Ordinal
Interval/ Ordinal
Interval/Ordinal

41. Identify Data Options: Choose Types of Data Measures
An instrument is a tool for measuring, observing, or documenting quantitative data
Types of Instruments
Performance Measures (e.g. test performance)
Attitudinal Measures (measures feelings toward educational topics)
Behavioral Measures (observations of behavior)
Factual Measures (documents, records)

42. Record and Administer Data Collection: Locate or Develop an Instrument
Develop your own instrument
Locate an existing instrument
Modify an existing instrument

43. Record and Administer Data Collection: Locate or Develop an Instrument
Strategies to use
Look in published journal articles
Run an ERIC search and use the term “instruments” and the topic of the study
Go to ERIC web site for Evaluation and Assessment
Examine guides to commercially available tests

44. Developing a questionnaire

45. Obtain Reliable and Valid Data
Reliability: individual scores from an instrument should be nearly the same or stable on repeated administrations of the instrument
Bathroom scale

46. Reliability Types of reliability
Test-retest (scores are stable over time)
Internal consistency (consistent scores across the instrument)
Cronbach coefficient alpha if items are scored as continuous variables (SA—SD)
Inter-rater reliability (similarity in observation of a behavior by two or more individuals)

47. Validity
Validity: the ability to draw meaningful and justifiable inferences from the scores about a sample or a population

48. Types of validity
Content (representative of all possible questions that could be asked)
Criterion-referenced (scores are a predictor of an outcome or criterion they are expected to predict
Construct (determination of the significance, meaning, purpose and use of the scores)

49. Let’ s look at a School Based Assessment Research
The instrument

50. Collecting Qualitative Data

51. Key Ideas Gaining site permission Purposive sampling
Types of qualitative data
Protocols and Issues regarding administering and recording qualitative

52. Gaining Permission Gain permission from Institutional Review Board
Gain permission from “gatekeepers” at the research site
Gatekeepers: individuals at the site who provide site access, helps researcher locate people and identifies places to study
The gatekeeper may require written permission about the project

53. Information for the gatekeeper
Why their site was chosen
What time and resources are required
What will be accomplished at the site
What potential there is for your presence to be disruptive
What individuals at the site will gain from the study
How will you use and report the results

54. Types of data to Collect
Observations
Interviews
Documents
Audio-Visual Materials

55. Sources of Qualitative Data
From People:
Interviews
Surveys
Focus Groups
Participant Observation (field notes)
From Things:
Agency case records
Miscellaneous documents
Historical Artifacts
Media
Published materials

56. Projective Techniques Observation Interviews Documents Audio Visuals

57. Projective technique : Let’s gather data
Yahoo! Den Monang!

58. Types of Data to Collect: Observations
An observation is the process if gathering first-hand information by observing people and places at a research site.
Observational roles
Participant observer
Non-Participant observer
Observational roles can be changed

59. Types of Data to Collect: Observations
Conduct multiple observations
Record both descriptive and reflective field notes during the observation
Descriptive field notes describe the events, activities and people
Reflective field notes record personal reflections that relate to their insights, hunches or broad themes that emerge

60. Administering and Recording Data: Observational Protocols
The header: essential information about the observation
Left column to record descriptive notes
Right column to record reflective notes
A picture of the site may be sketched

61. Types of Data to Collect: Interviews
Types of Interviews
Individual
Focus group
Telephone

62. Structured, Unstructured, and Semi-Structured Interviews
Approach to
Data
Collection
Type of
Response
Options to
Questions
Types of
Interviews
Leading to
Quantitative
Close-
Ended
Structured/
semi-structured
Scores to
answers
Qualitative
Open-
Unstructured
Transcription
of words

63. Types of Data to Collect: Interviews
General open-ended questions are asked
allows the participant to create options for responding
participants can voice their experiences and perspectives
Information is recorded then transcribed for analysis

64. Administering and Recording Data: Interview Protocols
The header: essential information about the interview
Open-ended questions include
“ice-breaker”
ones that address major research questions
probes that clarify and elaborate
Closing comments thanking the participant

65. EXAMPLE: Semi structured Interview protocols
Name:
Date:
Time:
Venue:
Experience – please describe
Questions
Is the method a valid approach to addressing the learning needs of adult learners? Please describe your thoughts of the method
What do you think are the barriers to the utilization of the method in UiTM?
How would you rate the quality of learning using the method?
What overall suggestions do you have for improving the method?

66. Types of Data to Collect: Documents
Public and private records
Good source for text data
You must obtain permission before using documents
Scan documents when possible

67. Types of Data to Collect: Audio-Visual Materials
Determine the material that can provide evidence to address your research questions
Determine if the material is available and obtain permission to use it
Check the accuracy and authenticity of the material if you do not record it yourself
Collect the data and organize it

68. Administering and Recording Data: Field Issues
Time needed for data collection
Limit initial collection or one or two observations or interviews
Time is needed to establish a substantial data base

69. Administering and Recording Data: Field Issues
Obtaining permission to use materials
Ethical issues
Anonymity of participants
Convey true purpose of study without deception

70. Quantitative Data Analysis - A survey on parenting styles
NEXT
Quantitative Data Analysis
- A survey on parenting styles

71. Let’s gather data Confirm research Qs Fill in the Qnaire
Tabulate results
Qualitative data
What do..?