INFO 287-16
Seminar in Information Science
Topic: Collecting and Analysing Data for Evidence
Spring 2022 Syllabus

Dr. Renée Jefferson

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

Syllabus Links
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Canvas Information: Courses will be available on January 26th at 6 am PT unless you are taking an intensive or a one-unit or two-unit class that starts on a different day. In that case, the class will open on the first day that the class meets.

You will be enrolled in the Canvas site automatically.

Course Description

This course offers a practical introduction to data collection and analysis methods used by library and information science professionals to make evidence-based decisions.  The 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.

Course Requirements

Course Calendar

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




1. Jan 26 – Jan 30

(Wed – Sun)  

Introduction to the Course

Introduction to Your Classmates

Due: Introduction-Part 1 (1/30)


2. Jan 31 – Feb 6

Overview of Course Assignments


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

Due: Data Collection & Analysis Pretest (2/6)

3. Feb 7 – Feb 13

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

Identify conference paper topic.

Due: Conference Topic (2/13)

Readings: Small & Mardis: Scenario I, Ch1, Ch2, & Ch3

Data Collection Methods: Weeks 4-8

4. Feb 14 – Feb 20


Due: Observation Initial Post (2/20)

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

*Additional readings posted in Canvas.


Response post due 3/20.

5. Feb 21 – Feb 27


Due: Interview Initial Post (2/27)

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

*Additional readings posted in Canvas.


Response post due 3/20.

6. Feb 28 – Mar 6


Due: Survey Initial Post (3/6)

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

*Additional readings posted in Canvas.


Response post due 3/20.

7. Mar 7 – Mar 13


Due: Documents Initial Post (3/13)

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.


Response post due 3/20.

8. Mar 14 – Mar 20


Due: Response Posts (3/20)

Data Analysis Methods: Weeks 9-14

9. Mar 21 – Mar 27

Qualitative Data Analysis

Due: Qualitative Response Post #1 (3/27)

10. Mar 28 – Apr 3

Qualitative Data Analysis


11. Apr 4 – Apr 10


Due: Qualitative Response Post #2 (4/10)

12. Apr 11 – Apr 17

Quantitative Data Analysis


13. Apr 18 – Apr 24


Due: Quantitative Response Post #1 (4/24)

14. Apr 25 – May 1

Quantitative Data Analysis


15. May 2 –  May 8


Due: Quantitative Response Post #2 (5/8)

16. May 9 – May 15

Conference Paper Work Week

Due: Data Collection & Analysis Posttest (5/15)

17. May 16 (Mon)

Last Day of Class

Due: Conference Paper (5/16)


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 that 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.

Other Relevant Information:

Discussion is an important element of this course and will be used to explore the concepts presented in the reading.  The forum is a venue for learning, asking questions, agreeing, disagreeing, and admit uncertainty.  As such, you are expected to follow the social rules based on SJSU's Do's and Don'ts of Online Posts and ALA's Statement of Appropriate Conduct.

To benefit most from the course, students must complete required readings and online resource materials, complete the assignments, and participate in-class activities.  Students are expected to participate in scheduled course modules, complete reading assignments, actively contribute to class discussions, and submit assignments on time. 

Course Workload Expectations

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 Prerequisites

INFO 200, other prerequisites may be added depending on content

Course Learning Outcomes

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.

Core Competencies (Program Learning Outcomes)

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.


Required Textbooks:

  • Connaway, L. & Powell, R. (2010). Basic research methods for librarians 5th edition. Libraries Unlimited. Available as free eBook through King Library arrow gif indicating link outside sjsu domain
  • 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: B07CGMR7Q6arrow gif indicating link outside sjsu domain

Grading Scale

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

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


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, university-wide policy information relevant to all courses, such as academic integrity, accommodations, etc. will be available on Office of Graduate and Undergraduate Programs' Syllabus Information web page at: Make sure to visit this page, review and be familiar with these university policies and resources.

In order to request an accommodation in a class please contact the Accessible Education Center and register via the MyAEC portal.

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