Personal epistemologies refers to psychological constructs, a set of beliefs, regarding the possibility and methods of knowing. Diverse empirical approaches have investigated this broad field, and are thoroughly reviewed by Hofer & Pintrich (1997, 2012).
Personal epistemologies are important because they are related to learning, political debate and decision making. D. Kuhn’s investigation of epistemological beliefs was driven by a concern for argumentative skills (D. Kuhn, 1991). Specific links have been drawn between epistemological beliefs, goal-structure orientation, and motivation (e.g. Chan & Elliott, 2004; Qian & Alvermann, 1995). Such educational processes are important because schooling is a major device for reproducing social stratification both via structured processes of assessment and selection and via subtler processes of imprinting identities, self-images and identities. Althusser (e.g. 2008) associates the prevail of the scientific world-image in the modern era with schooling, among the scopes of which is to reproduce beliefs about science. Science, in turn, serves as a principle for organizing social relationships in the industrial era.
Admitting the involvement of intentions, assumptions and theoretical constructs in scientific inquiry is not synonymous with radical relativism. Hacking (1999) claims that there is no such thing as a philosopher claiming that nothing is real, and that all beliefs have equal weight. Geertz claims that the accusation of radical relativism serves only to distract people from questioning particular ways of thinking (Geertz, 1984). Adding some perspective in scientific knowledge, if anything calls for more demanding scrutiny of assumptions, which should be less relativistic (Rodgers, 2008).
The reader interested in philosophy of science is referred to (Brown, 1993; Chalmers, 1999; Hacking, 1983).
In the proposed quantification, I defined the following dimensions, drawing from the philosophical accounts of science mentioned above.
Observation: Scientific knowledge is legitimate because it is based on observation and exact measurement.
Falsification: Scientific knowledge is legitimate because it is based on testing falsifiable hypotheses.
Reductionism: All phenomena can be explained by basic physical and chemical mechanisms.
Agnosticism: Scientific knowledge is no more legitimate than other forms of knowledge.
Derogation: Disciplines outside the natural sciences are not worth studying by smart people.
Realism: Science describes the physical universe itself.
Method: All disciplines should adopt the methodology of physics and chemistry.
Ideology: Science serves ideological agendas indirectly related to the quest for truth.
Construction: Science is a product of the human mind and its historical and social contexts.
In order to reproduce the analysis you will need to download the material in the git_hub repository. You will also need to have installed some prerequisite libraries, so make sure you run these lines in your computer.
install.packages("data.table")
install.packages("xtable")
install.packages("psych")
install.packages("Hmisc")
install.packages("Scale")The following lines will read in the data table, the item-key file and the file with all the item statements, and prepare them for the analysis.
library(Scale)
repdf <- read.table('epistemics28.dat',
header = T, sep = '\t')
# Remove demographics
repdf <- repdf[5:121]
# Remove fake respondents
excl <- which(apply(repdf, 1, function(x) sum(is.na(x))>= 115))
repdf2 <- repdf[-excl,]
# Convert NA's to 3's (See README)
repdf2[is.na(repdf2)] <- 3
# Read In Item Key
repkeyscr <- read.table('esri_key_scripted.dat',
header = T, sep = '\t')
# Read In Item Statements
esri_items <- readLines("esri_en.txt")
library(data.table)
repkeyscr <- data.table(repkeyscr)In order to conduct an Item Analysis to any of the subscales, say “Observation”, we will proceed as follows:
# Select the appropriate item labels from the item-key data table.
my_items <- as.character(repkeyscr[subscale=="observation", online])
# Use the info in the key to identify reverse items.
my_reverse <- which(repkeyscr[subscale=="observation", reverse])
# Create a Scale Data object, to be further processed)
esri_scale_adults <- Scale(repdf2[my_items],
reverse=my_reverse, items=esri_items)
# Preprocess the Data
esri_scale_pre <- PreProc(esri_scale_adults)
# Conduct Item Analysis
esri_scale_it <- ItemAnalysis(esri_scale_pre)
# Create - and print - the report
esri_table <- ReportTable(esri_scale_it)
library(xtable)
xt <- xtable(esri_table, align="lccccc")
print(xt, type="html", include.rownames=F)| Item | Corr. to scale | Factor Loading | Mean | SD |
|---|---|---|---|---|
| Q3_3 | 0.71 | 0.75 | 3.17 | 1.20 |
| Q3_14 | 0.65 | 0.72 | 3.11 | 1.08 |
| Q3_5 | 0.57 | 0.70 | 3.28 | 1.41 |
| Q3_12 | 0.56 | 0.69 | 2.83 | 1.10 |
| Q3_8 | 0.56 | 0.67 | 3.11 | 1.13 |
| Q3_11 | 0.52 | 0.65 | 2.22 | 0.94 |
| Q3_16 | 0.50 | 0.62 | 2.83 | 1.34 |
| Q3_1 | 0.55 | 0.59 | 2.50 | 1.34 |
| Q3_6 | 0.48 | 0.59 | 2.83 | 1.47 |
| Q3_17 | 0.30 | 0.51 | 2.72 | 1.23 |
| Q3_13 | 0.35 | 0.51 | 2.56 | 1.38 |
| Q3_7 | 0.30 | 0.38 | 3.28 | 1.32 |
| Q3_18 | 0.26 | 0.30 | 1.83 | 0.79 |
| Q3_2 | 0.25 | 0.22 | 3.06 | 1.16 |
| Q3_9 | 0.13 | 0.12 | 2.89 | 0.96 |
| Q3_4 | 0.01 | 0.03 | 3.17 | 1.42 |
| Q3_15 | 0.02 | 0.03 | 3.72 | 1.13 |
| Q3_10 | 0.16 | -0.25 | 2.67 | 1.03 |
Which are the best items for “Observation”? We need to pass the result of the ItemAnalysis() function to the ShowItems() function. Note that the second and fourth item are reverse items.
items_table <- data.frame(Items = ShowItems(esri_scale_it)[[2]])
xt <- xtable(items_table)
print(xt, type="html", include.rownames=F)| Items |
|---|
| Those scientific theories that are corroborated by data have to be true. |
| Scientific data mean nothing without their arbitrary interpretation. |
| All generalizations in Science are grounded on extensively observed principles. |
| Scientific progress occurs due to radical theoretical reformulations, and not due to the improvement of observation instruments. |
| Scientific progress is ensured by the improvement of the observation instruments that are now available. |
Acquiring more detailed reliability information can be achieved by selectively printing the reliability portion of the item analysis.
# the $ operator will extract the named element "rely"
print(esri_scale_it$rely)##
## Reliability Analysis of esri_scale_pre ScaleData object.
##
## A spearman correlation matrix of 18 items was calculated and submitted to Reliability analysis.
##
## The overall Cronbach's Alpha was 0.8 .
## Item(s) that exhibited low correlation with the rest of the scale were:
## 4,9,10 and 15 .
## Furthermore, deleting item(s) 4,9,10 and 15 may improve reliability.R’s vector operations allow for automatic analysis of all the subscales, by repeating the above process for each subscale. The following function captures what we did above, but with a placeholder instead of a specific subscale name. This function take any scale name as a character and return an item Analysis Object.
AnalyzeAnyScale <- function(any_scale){
# Select the appropriate item labels from the item-key data table.
my_items <- as.character(repkeyscr[subscale==any_scale, online])
# Use the info in the key to identify reverse items.
my_reverse <- which(repkeyscr[subscale==any_scale, reverse])
# Create a Scale Data object, to be further processed)
esri_scale_adults <- Scale(repdf2[my_items],
reverse=my_reverse, items=esri_items)
# Preprocess the Data
esri_scale_pre <- PreProc(esri_scale_adults)
# Conduct Item Analysis
ItemAnalysis(esri_scale_pre)
}Having a flexible function that can take any scale name as input allows us to exploit the lapply() vector operation in R. lapply() will apply a function to any member of a list. Thus:
# These are all the scale names
all_scales <- levels(repkeyscr$subscale)
# This will apply the function to each one of them
Big_Item_Analysis <- lapply(all_scales, AnalyzeAnyScale)
# This will name each element with its proper scale name
names(Big_Item_Analysis) <- all_scalesNow, we’ve got all the item analyses in the object Big_Item_Analysis. In order to examine any of them, we can invoke it by name.
Big_Item_Analysis$ideology##
## Reliability Analysis of esri_scale_pre ScaleData object.
##
## A spearman correlation matrix of 11 items was calculated and submitted to Reliability analysis.
##
## The overall Cronbach's Alpha was 0.84 .
## Item(s) that exhibited low correlation with the rest of the scale were:
## 2 .
## Furthermore, deleting item(s) 2 and 8 may improve reliability.A gls factor analysis was conducted. Items were regressed to
## a single factor. Their loadings are the following:
## Q3_33 Q3_39 Q3_36 Q3_35 Q3_38 Q3_37 Q3_41 Q3_32 Q3_40 Q3_42 Q3_34
## 0.155 0.382 0.461 0.535 0.552 0.657 0.697 0.709 0.784 0.803 0.833Similarly, we can extract particular elements from each subscale in the Big_Item_Analysis object. This is possible because each ItemAnalysis object returns data from the original analyses (in the psych package). For any purpose, we should write simple functions in order to extract the element. The process is much more clearer when one examines the structure of the ItemAnalysis object. For example, the Cronbach Alpha coefficient is hidden in it$rely$alpha$total$raw_alpha, where it is an ItemAnalysis object. Thus, a simple function to find it in any ItemAnalysis object is the following:
findAlpha <- function(it){
it$rely$alpha$total$raw_alpha
}Applying it to each element of the Big_Item_Analysis object with sapply(). sapply() is a close relative of lapply() that comes in handy when the result does not need to be structured as a list. Here, the result of each operation is a single number, so we are good with a vector.
Alphas <- sapply(Big_Item_Analysis, findAlpha)
# Binding the extracted Alphas with the sclae names
# into a data.frame
alphas_table <- data.frame(Scale=all_scales, Alphas)
# ... and printing it out nicely
xt <- xtable(alphas_table)
print(xt, type="html", include.rownames=F)| Scale | Alphas |
|---|---|
| agnosticism | 0.83 |
| construction | 0.90 |
| derrogation | 0.86 |
| falsification | 0.83 |
| ideology | 0.84 |
| method | 0.70 |
| observation | 0.80 |
| realism | 0.87 |
| reductionism | 0.78 |
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