MANTENIMIENTO Y LIMPIEZA DE INSTALACIONES

Data analysis in this research followed two approaches. First, quantitative data was gathered, tabulated and compared to answer the primary research questions. Second, qualitative responses were analyzed to identify themes, which inform the quantitative results.

In general, primary comparisons were made between participant groups and their levels of ML, within the groups and their change in ML between the pre/post instruction evaluations, and within the groups in the overall difference between self-efficacy levels. In the sub-sections below, specific analysis plans are discussed

3.6.1 Quantitative analysis

Quantitative analysis formed the bulk of analysis in this research. The data was analyzed along three axes which are listed below and represented in tabular form in Table 10. Analyses were examined for statistical differences among the following groups.

1. Is there an overall significant difference between information and ML self- efficacy ratings between student groups?

2. Within each group, is there a significant change in reported levels of ML based on the interaction?

3. Within each group, is there a significant difference between their reported levels of IL versus ML?

These questions are grouped in Table 10 into independent and dependent variables. For each variable comparison, a statistical test and rationale for the test are included. While these comparisons are the foundation for analyzing

quantitative data, other statistical tests are included in Chapter 6 which examines the relationships between variables.

Table 10. Variable analysis Categorical

independent variable

Dependent variable

Statistical test Rationale

Academic major, years of education, use of information technology, level of interaction with the web Pre-test self- efficacy score (averaged overall score) Two-sample t- test To determine whether average baseline scores differ between these groups Academic major, years of education, use of information technology, level of interaction with the web Pre-test metadata interaction (descriptive and social metadata identification) Two-sample t- test To determine whether average baseline scores differ between these groups. Academic major, years of education, use of information technology, level of interaction with the web Compare average pre-instruction to post-instruction scores for self- efficacy and metadata task

Two sample t-test To determine whether average score differences differ between these groups. This will indicate whether there is a change in ML among these two groups based on a brief instruction

This study also looked at the correlation between IL baseline scores with ML baseline scores. Baseline self-efficacy scores were compared to the change in task scores to determine if self-efficacy was related to changes in task scores. In order to do both of these, this study used the Pearson correlation test.

3.6.2 Qualitative analysis

The qualitative analysis in this research was limited to participant

observations surrounding their metadata use. The background/informational survey and the metadata use survey contained questions designed to elicit information about how participants think of metadata, what types of metadata services they use, and what they feel are important elements of metadata use. The following thematic areas were explored using an open coding approach a) How do participants define metadata? Are there generally accepted definitions? b) When discussing metadata-use and usefulness, what tasks/purposes do they mention? c) When discussing metadata-use and usefulness, what outcomes do the participants mention?

3.7 Study boundaries

This research used a mixed-methods approach to examine the question,

“How do students use metadata and what impact does it have on their view of their IL?” This allowed the research to take a quantitative view of the difference

instruction on a specific technology to help participants generalize their

knowledge of metadata. This research also used qualitative methods to provide contextual responses regarding participant definitions, views and attitudes on metadata. It employed simple descriptive statistics on open-ended responses to provide a picture of how participants use metadata in common information environments and asked them to reflect on these uses.

This research also took a constructivist approach in gathering data in that it encouraged participants to ground their responses in their own experience and perceptions. By using self-efficacy based instruments to allow participants to rate their levels of information and ML, this research remains grounded in participant perspective as opposed to system functionality.

3.7.1 Alternatives considered

A number of alternative approaches were considered during the design of this research. For example, objective analysis of participant work by experts was considered to provide an objective evaluation of skill. Likewise, a separation of participants into multiple instruments and uses was considered, including an iterative survey design which would have asked librarians to comment on the views of the students. In the end, a single instrument approach was selected to allow the best chance to compare participant groups and to compare the change in metadata and IL through the interaction.

3.7.2 Assumptions

While this research employed a constructivist approach in investigating the role that ML plays in information interactions, it did have a number of base assumptions that drove the research. First, the study assumed that metadata is a valuable element in information environments. Second, the instructional element was designed to evaluate participant responses and view of ML when informed about the role of metadata in a specific application. As such, while it identified student competencies for specific tasks such as identification and creation of metadata, it did not seek to evaluate metadata elements or specific uses (e.g. which elements participants find useful). Finally, this research proceeded on the assumption that metadata is a generally understandable tool and concept for participants. While participants may not have had an in-depth understanding of the various roles and types of metadata or its terminology, it is assumed that there was a base level of understanding that allowed them to understand common examples. For example, the metadata exercises included the assumption that students would recognize the general structure and content of the screen-shots taken from Flickr and digg.com.