Food Protection Trends - January/February 2018 - 32

other measured the probability of getting the quantity and
type of milk products desired. The two items had internal
consistency (α = 0.764) and were summed to form the
variable Perceived Behavioral Control.
Attitude
The variable Attitude was calculated as the sum of the
products of a group of statements measuring a respondent's
beliefs and corresponding belief evaluation statements specifically associated with the consumption of milk products
(1). The survey item construction began with a preliminary
focus group study conducted to document an array of beliefs
and belief evaluations expressed by consumers of milk or
dairy products (15). Preliminary belief statements from the
focus groups (Likert-scaled (agree/disagree), coded from
1-5, n = 39) and corresponding belief evaluation statements (Somatic-differential (likely/unlikely or important/
unimportant), coded from -3 to +3, n = 39), were written
based on focus group transcripts. Key informants who were
either pasteurized milk/dairy product consumers (n = 8)
or unpasteurized milk/dairy product consumers (n = 18)
volunteered to respond to a short online survey designed to
produce preliminary data. Data from belief and evaluation
items were multiplied to form products that corresponded to
individual attitude statements. Principal component analysis
was used to sort 16 items into factors with acceptable internal
consistency. Fourteen items were sorted into three factors
and were named: Attitude-Concerns (items = 8, α = 0.873);
Attitude-Responsibility (items = 3, α = 0.777); and Attitude-Benefits (items = 3; α = 0.616). Two items that asked
about attitudes toward organic foods and milk regulation
failed to be included in any of the factors and were eliminated
from further analysis.
Mental model data analysis
Four mental models were qualitatively constructed
by triangulating quantitative outcomes of four different
methods of statistical analysis: Group difference testing of
nonparametric (Chi-Square) and parametric (ANOVA)
variables, discriminant analysis (DM) using continuously
scaled variables only, and hierarchical linear modeling
(HLM) using both nominal and continuous variables.
Three HLM analyses were completed for the RISP model
constructs to delineate significant variables predictive
of information seeking/avoiding, heuristic information
processing, and systematic information processing behaviors.
As with the RISP model constructs, HLM analysis was
used to identify significant variables that were predictive
of milk consumption behavior. Along with the variables
typically included within TPB models (e.g., Subjective
Norms, Perceived Behavioral Controls, and Attitude),
the HLM analysis also included the behavioral outcomes
variables from the RISP model but as predictive variables
on milk consumption behavior. Separate analyses were

32

Food Protection Trends January/February

completed that included the variables Information Seeking/
Avoiding, Heuristic Information Processing, or Systematic
Information Processing.
Data from the analyses were examined for outcomes that
distinguished differences among the milk consumption/
residence categories. Qualitatively distinguishing outcomes
from group difference testing were defined as the statistically
significant variables. For discriminant analysis, qualitatively
distinguishing variables were defined as standardized canonical discriminant coefficients greater than 0.3. The significant
variables from HLM were defined as distinguishing. Mental
models were qualitatively constructed to highlight distinguishing features as either "high" or "low" if they were above
or below the central point of the item scale and to delineate
the differences among the four residence/milk consumption
preference models.
RESULTS
Milk and residency groups
After the dataset was inspected for non-eligible responses,
the 454 survey responses represented 30.4% consumers of
pasteurized milk products (n = 138) and 69.6% consumers
of unpasteurized milk products (n = 316). The milk consumption groups were further sub-divided based on residency: pasteurized group, 82 urban (59.4%) and 56 rural
(40.6%); unpasteurized group, 199 urban (63.0%) and 117
rural (37.0%). There was no difference in the proportion of
respondents in either the milk consumption or residence
groups (P = 0.270).
For mental modeling, a three-level behavioral variable
was constructed for milk groups: exclusive selection of
pasteurized or unpasteurized milk products, or a combination
of the two types. Within the urban residency group, there
were 85 (30.9%) respondents in the pasteurized exclusively
group, 139 (50.5%) in the milk combination group, and 51
(18.5%) in the unpasteurized exclusively group. Within the
rural residency group, there were 52 (31.0%) respondents
in the pasteurized exclusively group, 90 (53.6%) in the milk
combination group, and 26 (15.5%) in the unpasteurized
exclusively group. No statistically significant differences were
found between residency groups (P = 0.687).
Individual characteristics (Table 1)
No differences were found for the demographic characteristics Gender (pasteurized P = 0.057, unpasteurized P =
0.411, variable P = 0.081) or Education (pasteurized P =
0.449, unpasteurized P = 0.170, variable P = 0.188). For the
variable Age, no differences were found among consumers
of pasteurized milk product milk (P = 0.250). For unpasteurized milk consumers, the proportion of respondents in
the 30 - 39 yr. age bracket was greater than other age groups
(P = 0.041), and the overall variable was also different (P =
0.014). Income was generally high among respondents, but
no differences were noted among pasteurized milk con-



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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