Food Protection Trends - January/February 2018 - 31

Current knowledge
Current Knowledge was a variable constructed from
summed items that measured general food safety knowledge (13). The survey asked respondents to mark "agree,
disagree, or unsure" for 15 statements about the safety
of various food handling practices. Items were reviewed
by food safety experts for accepted practice and were
designated as correct or incorrect statements, a priori
(13). Item responses were coded from agree/disagree to
correct/incorrect responses, and not-sure responses were
coded to incorrect responses. An item difficulty test was
used to reduce the list of 15 knowledge items to only those
deemed to be neither too easy nor too difficult (18). No
items were too easy; four items were too difficult. The
remaining 11 items were tested for internal consistency to
identify the set of items with the highest alpha value. Six
items were identified for the final summed variable named
Current Knowledge (α = 0.74).
Information sufficiency
In addition to actual food safety knowledge, the respondents Knowledge Perception was the number, on a scale
of 0 to 100, that the respondent felt best reflected the
amount of knowledge they have about the risks associated
with the consumption of unpasteurized milk products.
Respondents were asked to type this number into a box
on the survey. Information Sufficiency was also measured
on a 0 to 100 scale, but the item asked the respondent
to write the number that they believed represented the
amount of information they need for their purposes to
be able to make decisions regarding their consumption
of unpasteurized milk products. The measure Knowledge Threshold was calculated as the difference between
Knowledge Perception and Information Sufficiency.
Information gathering capacity
The summed variable Information Gathering Capacity
was derived from two items that measured the ease of
finding information about the safety of milk products and
the usefulness of that information to the respondent. The
two items had sufficient internal consistency to justify the
construction of the summed variable (α = 0.74).
Information channel beliefs
The respondent's opinion about the food safety information found through a variety of media and information
channels was measured by six items. Principal component
analysis was used to reduce the data into factors that best
sorted the concepts. Two factors were identified and
named Media Bias Beliefs (three items, α = 0.78) and
Validity Cues Beliefs (two items, α = 0.75). The remaining
item did not meet item-testing criteria and was eliminated
from further analysis.

Information source beliefs
Sources of information about the food safety of milk
products were queried in 14 Likert-scaled items related to
the usefulness of the media. Principal component analysis
was used to sort data into three factors based on eigenvalues
over 0.5 and Cronbach alpha above 0.6. Items within each
factor were summed and checked for internal consistency;
professional Information Sources (α = 0.90) included
television, radio, news, magazines, newsletters, pamphlets,
and health care professional conversations; Common
Information Sources (α = 0.89) included websites,
YouTube, family/friends' conversations, and Facebook;
and, Another Internet Source (α = 0.75) included Twitter,
blogs, and Pinterest.
Information seeking/avoiding behavior
Five items measured the respondent's behavior regarding
the seeking or avoiding information about the safety of milk
products. The items that remained after internal consistency
analysis were summed to form the variable Information
Seeking/Avoiding Behavior (α = 0.90). The remaining two
items were eliminated from further analysis. The variable
Information Seeking/Avoiding Behavior was one of three
behavioral outcome variables for the RISP model portion of
the Mental Models of Milk Product Consumption.
Information processing behavior
Heuristic Processing Behavior was measured in four
items, all of which had sufficient internal consistency to
be included in the summed variable (α = 0.82). Systematic
Processing Behavior was also measured in four items with
moderately low internal consistency (α = 0.64). All items
were retained and summed to form the variable representing
the importance of the variable to the overall mental models.
Heuristic and Systematic Processing Behavior were two
of three behavioral outcome variables for the RISP model
portion of the Mental Models of Milk Product Consumption.
Behavioral subjective norm
Two survey items measured the influence of family and
friends on the respondent's choice of milk products to consume. The Likert-scaled, normative item queried the respondent about family members' and friends' expectations of the
safety of their milk/dairy product choices. The Likert-scaled,
motivation item asked to what degree the respondent wanted
to please their referent others. The variable Behavioral Subjective Norm was computed as the product of the normative
and motivation item responses.
Perceived behavioral control
External controls over the consumption of milk products
were measured in two survey items. One item measured
the respondent's perception of the possibility of getting
the type of milk products they wanted to consume, and the

January/February Food Protection Trends

31



Table of Contents for the Digital Edition of Food Protection Trends - January/February 2018

Assessing the Usage of Food Thermometers at American University Football Tailgates
An Assessment of Potential Heavy Metal Contaminants in Bivalve Shellfish from Aquaculture Zones along the Coast of New South Wales, Australia
Mental Models of Pasteurized and Unpasteurized Milk Product Consumption in the United States
Sanitary Carcass Dressing and Food Safety Practices in South Central U.S. Small and Very Small Establishments Manufacturing Fresh and Not-Ready-to-Eat Pork Products
Beyond the Bio - Joe Frank
PDG Highlight The Dairy Safety & Quality Professional Development Group
General Interest Paper The Role of the European Food Safety Authority (EFSA) in the Fight against Antimicrobial Resistance (AMR)
Industry Products
Coming Events
Food Protection Trends - January/February 2018 - Cover1
Food Protection Trends - January/February 2018 - Cover2
Food Protection Trends - January/February 2018 - 1
Food Protection Trends - January/February 2018 - 2
Food Protection Trends - January/February 2018 - 3
Food Protection Trends - January/February 2018 - 4
Food Protection Trends - January/February 2018 - 5
Food Protection Trends - January/February 2018 - 6
Food Protection Trends - January/February 2018 - 7
Food Protection Trends - January/February 2018 - Assessing the Usage of Food Thermometers at American University Football Tailgates
Food Protection Trends - January/February 2018 - 9
Food Protection Trends - January/February 2018 - 10
Food Protection Trends - January/February 2018 - 11
Food Protection Trends - January/February 2018 - 12
Food Protection Trends - January/February 2018 - 13
Food Protection Trends - January/February 2018 - 14
Food Protection Trends - January/February 2018 - 15
Food Protection Trends - January/February 2018 - 16
Food Protection Trends - January/February 2018 - 17
Food Protection Trends - January/February 2018 - An Assessment of Potential Heavy Metal Contaminants in Bivalve Shellfish from Aquaculture Zones along the Coast of New South Wales, Australia
Food Protection Trends - January/February 2018 - 19
Food Protection Trends - January/February 2018 - 20
Food Protection Trends - January/February 2018 - 21
Food Protection Trends - January/February 2018 - 22
Food Protection Trends - January/February 2018 - 23
Food Protection Trends - January/February 2018 - 24
Food Protection Trends - January/February 2018 - 25
Food Protection Trends - January/February 2018 - 26
Food Protection Trends - January/February 2018 - Mental Models of Pasteurized and Unpasteurized Milk Product Consumption in the United States
Food Protection Trends - January/February 2018 - 28
Food Protection Trends - January/February 2018 - 29
Food Protection Trends - January/February 2018 - 30
Food Protection Trends - January/February 2018 - 31
Food Protection Trends - January/February 2018 - 32
Food Protection Trends - January/February 2018 - 33
Food Protection Trends - January/February 2018 - 34
Food Protection Trends - January/February 2018 - 35
Food Protection Trends - January/February 2018 - 36
Food Protection Trends - January/February 2018 - 37
Food Protection Trends - January/February 2018 - 38
Food Protection Trends - January/February 2018 - 39
Food Protection Trends - January/February 2018 - 40
Food Protection Trends - January/February 2018 - 41
Food Protection Trends - January/February 2018 - 42
Food Protection Trends - January/February 2018 - 43
Food Protection Trends - January/February 2018 - 44
Food Protection Trends - January/February 2018 - 45
Food Protection Trends - January/February 2018 - 46
Food Protection Trends - January/February 2018 - 47
Food Protection Trends - January/February 2018 - 48
Food Protection Trends - January/February 2018 - 49
Food Protection Trends - January/February 2018 - 50
Food Protection Trends - January/February 2018 - 51
Food Protection Trends - January/February 2018 - Sanitary Carcass Dressing and Food Safety Practices in South Central U.S. Small and Very Small Establishments Manufacturing Fresh and Not-Ready-to-Eat Pork Products
Food Protection Trends - January/February 2018 - 53
Food Protection Trends - January/February 2018 - 54
Food Protection Trends - January/February 2018 - 55
Food Protection Trends - January/February 2018 - 56
Food Protection Trends - January/February 2018 - 57
Food Protection Trends - January/February 2018 - 58
Food Protection Trends - January/February 2018 - 59
Food Protection Trends - January/February 2018 - 60
Food Protection Trends - January/February 2018 - 61
Food Protection Trends - January/February 2018 - 62
Food Protection Trends - January/February 2018 - 63
Food Protection Trends - January/February 2018 - 64
Food Protection Trends - January/February 2018 - 65
Food Protection Trends - January/February 2018 - 66
Food Protection Trends - January/February 2018 - 67
Food Protection Trends - January/February 2018 - Beyond the Bio - Joe Frank
Food Protection Trends - January/February 2018 - 69
Food Protection Trends - January/February 2018 - 70
Food Protection Trends - January/February 2018 - PDG Highlight The Dairy Safety & Quality Professional Development Group
Food Protection Trends - January/February 2018 - General Interest Paper The Role of the European Food Safety Authority (EFSA) in the Fight against Antimicrobial Resistance (AMR)
Food Protection Trends - January/February 2018 - 73
Food Protection Trends - January/February 2018 - 74
Food Protection Trends - January/February 2018 - 75
Food Protection Trends - January/February 2018 - 76
Food Protection Trends - January/February 2018 - 77
Food Protection Trends - January/February 2018 - 78
Food Protection Trends - January/February 2018 - 79
Food Protection Trends - January/February 2018 - 80
Food Protection Trends - January/February 2018 - 81
Food Protection Trends - January/February 2018 - Industry Products
Food Protection Trends - January/February 2018 - 83
Food Protection Trends - January/February 2018 - 84
Food Protection Trends - January/February 2018 - 85
Food Protection Trends - January/February 2018 - 86
Food Protection Trends - January/February 2018 - 87
Food Protection Trends - January/February 2018 - Coming Events
Food Protection Trends - January/February 2018 - Cover3
Food Protection Trends - January/February 2018 - Cover4
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