1. Field Research Designs
Purpose of field research designs
Types of field studies
Planning a field study
Sampling plan
Questionnaire design
Online Questionnaires
Data analysis
Special concerns in field research

2. Some Definitions Population Element Sample Subject Sampling
An entire group of people, events or things of interest
Element
Single member of population
Sample
Subgroup of the population
Subject
Single member of sample
Sampling
Selecting sufficient number of elements from population so that features of the sample (e.g., mean) can be generalized to the population

3. Advantages of Sampling
Less cost
Compared to cost of studying population
Less error
In collecting & analysing data
Less time
Because fewer elements considered
Less intrusive/destructive
E.g., when measurement changes phenomenon

4. Sampling Plan Random sampling
Each member of population has EQUAL chance of being selected into sample
Ease of identifying population
Company vs. community vs. student populations
Determining sample
Random number tables, computerized routine, drawing from urn

5. Sampling Plan Random sampling
Randomly select additional participants if initially selected ones refuse/cannot
Order population, sample every xth person – ordering is not related to variable of interest
E.g., Immigration checks
Use of convenience sample
Include variables that assess representativeness of obtained sample
If response rate is low
e.g., ethnic harassment study

6. Modified random sampling
Sampling Plan
Modified random sampling
Stratified random sampling
Divide population into subgroups & randomly select from subgroups
Sub-grouping expected to influence results (e.g., motivational levels in R&D vs. secretarial staff)
Used when total sample size is small and number of subgroups is large
E.g., visible minorities at Scar campus

7. Modified random sampling (cont’d)
Sampling Plan
Modified random sampling (cont’d)
Cluster sampling
Choose participants based on membership of a group
Groups are then chosen to participate in study
Stats computed can have large sampling errors
E.g., examine units in 4 vs. 30 boxes of a shipment
Over-sampling from a subgroup
E.g., gays in the org’n
Need to weight descriptive stats appropriately

8. Field Research Designs
Sampling plan
Questionnaire design
Online Questionnaires
Data analysis
Special concerns in field research

9. Use existing measures of concepts
Questionnaire Design
Use existing measures of concepts
Comparability
Reliability (standardization)
Validity

10. Questionnaire Design Writing Items Comprehensiveness Accuracy
E.g., commitment scale
Accuracy
Maintain respondents’ cooperation & dignity

11. Questionnaire Design Writing Items Structured vs. Open-ended items
Respondent involvement in research
Purpose of research
Exploratory vs. confirmatory
Type of question
E.g., When all possibilities are not known/too many
Resource availability
Time & money for coding & analysing
Saks 69-73; Sekaran

12. Questionnaire Design Writing Items
Use simple, direct, familiar language
Be clear & specific (avoid ambiguous items)
Use positively and negatively worded items
Avoid double-barreled items
Avoid Leading questions
Avoid loaded questions
Ensure applicability to all respondents
Avoid recall-dependent items
Saks 69-73; Sekaran

13. Questionnaire Design Writing Items Minimize Response styles
Yea/Nay sayers (acquiescence)
Positive vs. negatively worded items
Social Desirability
Forced choice format
Content-specific anchors (e.g., BARS)
Items scattered across survey
Saks 69-73; Sekaran

14. Response options in structured scales
Questionnaire Design
Response options in structured scales
Types of Rating Scales
Likert, Semantic Differential, Itemized Rating etc. (p Sekaran)
Bimodal responding
Using only a portion of the response options
Ensure anchors have same meaning to all respondents
Use numbers w/verbal descriptors

15. Response options in structured scales
Questionnaire Design
Response options in structured scales
Identify time frame of phenomenon of interest
Optimal number of scale points
5 points is best, fewer results in less variability
Instructions
Provide examples
Participants’ education level
Previous exposure to method of data collection
e.g., web/ surveys

16. Response options in structured scales
Questionnaire Design
Response options in structured scales
Sequencing
General to specific, easy to difficult
Avoid placing positively and negatively worded items tapping into the same dimension near each other
Beware of ordering effects
Issues with dispersal of items
Numbering
Attend to data analyses issues
Sekaran 242

17. Response options in structured scales
Questionnaire Design
Response options in structured scales
Layout (appearance)
Introduction
e.g., Study Information Sheet
Organization
By sections
Personal Data
Request sensitive personal data at the end
Open-ended questions in the end
Conclusion
Sekaran

18. Ways to Optimize Return Rate
Questionnaire Design
Pre-testing Survey
Readability, item content, ambiguities
Ways to Optimize Return Rate
Upper management or union support
Work time allocated for survey completion
Coercion, confidentiality concerns
Participants’ belief in value of research
Previous experience with HR research
30% rate is common

19. Optimizing Return Rate
Questionnaire Design
Optimizing Return Rate
Professional appearance
For mailed survey: use first class mail & include return postage
Send reminders
Provide Incentives for responding
Keep survey at optimal length
Identify characteristics of non-responders to establish representativeness of sample
Identify mechanism for clarifying questions

20. Online Questionnaires
Advantages
Speed
Delivery to participants
Completed surveys to researcher
Cost efficiency
Environmental costs (e.g., paper, ink)
Personnel costs (e.g., typing, data entry)

21. Online Questionnaires
Concerns
Respondents’ access to computers
Establish invariance b/w paper-pencil and computer versions (e.g.,achievement, attitude measures)
Ballot stuffing
Unique access control numbers
Start up costs
E.g. survey monkey $20/month
Technical difficulties during survey administration
Researcher’s control over design interface
E.g. survey monkey
Employee reactions to online surveys

22. Preliminary Data Cleaning
Data Analysis
Preliminary Data Cleaning
Use descriptive data to catch errors
E.g., means, ranges, standard deviations
Coding open-ended responses
Analysis & Interpretation
Descriptive data
Frequencies, means
Group comparisons
T-tests, ANOVAs
Establish relations between variables
Correlations, regressions

23. Special Issues in Field Research
Scale reduction
Alternatives to shortening existing scales
Reduce number of variables
Use alternative methods of measurement
E.g., peer ratings, archival data etc.

24. Special Issues in Field Research
Percept-percept problem
Response bias due to cross-sectional, mono-method measurement of all variables
Alternatives to self-report questionnaires
E.g., archival, objective data
Multiple data collection times
E.g., Longitudinal study
Dispositional influences
E.g., neuroticism

25. Special Issues in Field Research
Survey matching
Ensure confidentiality & anonymity
Controlling extraneous variables
Conceptual understanding
Sample characteristics
Measurement or control of variables

26. Special Issues in Field Research
Response Variability
Dichotomous scales (e.g., y/n responses)
Ethics
Info re: research objectives
Precautions re: anonymity
Limit demographic info requested
Web/ based surveys
Mechanisms for research feedback
Implications, planned action, follow up