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College of Professional and Global Education · School of Information

Seminar in Information Science - Collecting and Analysing Data for Evidence Based Decisions
INFO 287

  • Spring 2023
  • Section 16
  • 3 Unit(s)
  • 01/25/2023 to 05/15/2023
  • Modified 05/22/2023

Canvas Information: Courses will be available January 25th, 6 am PT.

You will be enrolled in the Canvas site automatically.

Contact Information

Dr. Renée Jefferson
Other contact information: telephone: (843) 991-3346
Office Hours: Virtual, by appointment

Course Description and Requisites

This course offers a practical introduction to data collection and analysis methods used by library and information science professionals to make evidence-based decisions.  Focus is on primary and secondary data collection methods as well as qualitative and quantitative data analysis methods.  Students will compare and contrast different data collection and data analysis methods published in library and information science literature.  They will explore different types of evidence and how they inform decision-making.


INFO 200, other prerequisites may be added depending on content.

Classroom Protocols


Students are expected to participate fully in all class activities. It is expected that students will be open-minded and participate fully in discussions in class and debate in a mature and respectful manner. Use of derogatory, condescending, or offensive language including profanity is prohibited. Disagreement is healthy and perfectly acceptable. Expressing disagreement should always include an explanation of your reasoning and, whenever possible, evidence to support your position. In accordance with San José State University's Policies, the Student Code of Conduct, and applicable state and federal laws, discrimination based on gender, gender identity, gender expression, race, nationality, ethnicity, religion, sexual orientation, or disability is prohibited in any form.

Program Information

Course Workload

Success in this course is based on the expectation that students will spend, for each unit of credit, a minimum of forty-five hours over the length of the course (normally 3 hours per unit per week with 1 of the hours used for lecture) for instruction or preparation/studying or course related activities including but not limited to internships, labs, clinical practica. Other course structures will have equivalent workload expectations as described in the syllabus.

Instructional time may include but is not limited to:
Working on posted modules or lessons prepared by the instructor; discussion forum interactions with the instructor and/or other students; making presentations and getting feedback from the instructor; attending office hours or other synchronous sessions with the instructor.

Student time outside of class:
In any seven-day period, a student is expected to be academically engaged through submitting an academic assignment; taking an exam or an interactive tutorial, or computer-assisted instruction; building websites, blogs, databases, social media presentations; attending a study group; contributing to an academic online discussion; writing papers; reading articles; conducting research; engaging in small group work.

Course Goals

Core Competencies (Program Learning Outcomes) Supported

INFO 287 supports the following core competencies:

  1. L Demonstrate understanding of quantitative and qualitative research methods, the ability to design a research project, and the ability to evaluate and synthesize research literature.

Course Learning Outcomes (CLOs)

Upon successful completion of the course, students will be able to:

  1. Demonstrate knowledge of data collection methods and their use in making evidence-based decisions.
  2. Compare and contrast data collection methods used in library and information science research.
  3. Demonstrate knowledge of data analysis methods and their use in making evidence-based decisions.
  4. Compare and contrast data analysis methods used in library and information science research.

Course Materials


Required Textbooks:

  • Connaway, L. & Powell, R. (2010). Basic research methods for librarians 5th edition. Libraries Unlimited. Available as free eBook through King Library
  • Small, R. V., & Mardis, M. A. (2018). Research methods for librarians and educators: Practical applications in formal and informal learning environments. Libraries Unlimited. Available through Amazon: B07CGMR7Q6

Recommended Textbooks:

  • Lewis-Beck, M., Bryman, A., & Liao, T. (2004) The SAGE encyclopedia of social science research methods. SAGE. Available as free eBook through King Library

Course Requirements and Assignments


The following table includes the course assignment points and due dates. Due dates may change to accommodate class needs. Sufficient notice will be provided for any change in due dates.




Due Dates

Introduction (Part 1 & Part 2)



1/30, 2/2

Data Collection & Analysis Pretest & Posttest

#1, #3


2/6, 5/15

Conference Topic

#2, #4



Data Collection Methods Forums: Initial Posts (4@5pts) &

Response Posts (4@5pts)

#1, #2


2/20, 2/27, 3/6,

3/13, 3/20

Data Collection Analysis Forums: Response Posts (4@5pts)

#3, #4


3/27, 4/10,

4/24, 5/8

Conference Paper

#1, #2, #3, #4







Late Assignment Policy

Assignments are designed to accommodate students with a variety of work schedules and personal commitments. The schedule assumes all coursework will be collected by midnight Pacific Time (PT) on the assigned date. If you have an illness (medical documentation required) or family emergency, please contact the instructor. Late assignments (submitted after midnight on the assigned date) will result in a reduction of points. The points for an assignment will be reduced by 5% for each day that the assignment is submitted after the due date. Extra-credit assignments are not available. Incomplete grades will not be granted except in extraordinary circumstances.

Grading Information

The standard SJSU School of Information Grading Scale is utilized for all iSchool courses:

97 to 100A
94 to 96A minus
91 to 93B plus
88 to 90B
85 to 87B minus
82 to 84C plus
79 to 81C
76 to 78C minus
73 to 75D plus
70 to 72D
67 to 69D minus
Below 67F


In order to provide consistent guidelines for assessment for graduate level work in the School, these terms are applied to letter grades:

  • C represents Adequate work; a grade of "C" counts for credit for the course;
  • B represents Good work; a grade of "B" clearly meets the standards for graduate level work or undergraduate (for BS-ISDA);
    For core courses in the MLIS program (not MARA, Informatics, BS-ISDA) — INFO 200, INFO 202, INFO 204 — the iSchool requires that students earn a B in the course. If the grade is less than B (B- or lower) after the first attempt you will be placed on administrative probation. You must repeat the class if you wish to stay in the program. If - on the second attempt - you do not pass the class with a grade of B or better (not B- but B) you will be disqualified.
  • A represents Exceptional work; a grade of "A" will be assigned for outstanding work only.

Graduate Students are advised that it is their responsibility to maintain a 3.0 Grade Point Average (GPA). Undergraduates must maintain a 2.0 Grade Point Average (GPA).

University Policies

Per University Policy S16-9 (PDF), relevant university policy concerning all courses, such as student responsibilities, academic integrity, accommodations, dropping and adding, consent for recording of class, etc. and available student services (e.g. learning assistance, counseling, and other resources) are listed on the Syllabus Information web page. Make sure to visit this page to review and be aware of these university policies and resources.

Course Schedule

The schedule represents an overview of the topics that will be covered. Depending on class needs, the schedule is subject to change. The majority of the assignments are due on Sunday at 11:59 PM (PT) unless otherwise noted.




1. Jan 25 – Feb 5


Introduction to the Course

Introduction to Your Classmates

Due: Introduction-Part 1 (1/29)

Due: Introduction-Part 2 (2/3, Wed)

Due: Data Collection & Analysis Pretest (2/5)

2. Feb 6 – Feb 19

What is the “evidence” in evidence-based decision-making?

Collecting Data Using Observations

Due: Observation Initial Post (2/12)

Due: Observation Response Post (2/19)


Readings: Small & Mardis (Scenario I, Chs1-3, Scenario III, Ch8, and Ch15-Direct Observations); Connaway & Powell (Ch5, pp. 178-183; Ch7, pp. 216-218)


*Additional readings posted in Canvas.

3. Feb 20 – Mar 5

Collecting Data Using Interviews

Due: Interview Initial Post (2/26)

Due: Interview Response Post (3/5)


Readings: Small & Mardis, Scenario II and Ch4; Scenario VI and Ch15-Interviews; Connaway & Powell Ch 5, pp.170-182.


*Additional readings posted in Canvas.

4. Mar 6 – Mar 19

Collecting Data Using Surveys

Due: Survey Initial Post (3/12)

Due: Survey Response Post (3/19)


Readings: Small & Mardis: Scenario VIII and Ch19; Connaway & Powell (Ch4, pp.107-114)


*Additional readings posted in Canvas.

5. Mar 20 – Apr 2

Using Documents as Data

Due: Document Initial Post (3/26)

Due: Document Initial Post (4/2)


Readings: Small & Mardis: Scenario IV and Ch10; Scenario VI and Ch15-Document Review; Connaway & Powell, Ch 7, pp. 222-229.


*Additional readings posted in Canvas.

6. Apr 3 – Apr 16

Qualitative Data Analysis

Due: Qualitative Evidence Activity (4/16)

7. Apr 17 – Apr 30

Quantitative Data Analysis

Due: Quantitative Evidence Activity (4/30)

8. May 1 – May 14

Work on Evidence-Based Report

Due: Data Collection & Analysis Posttest (5/7)

9. May 15

Class ends

Due: Evidence-Based Report (5/15)