Procesamiento y análisis de la información

The goal of this study is to develop a profile of Iowa community college athletics coaches, and then to examine the potential relationships among these profile characteristics and to explore differences among sub-samples of this population. This chapter will begin with a review of the study’s research questions and hypotheses, followed by the rationale behind using a mixed methods approach to the study. Next, an outline of the study’s research design for both the quantitative and qualitative approaches will be specified. Then, information about the population and sampling methods will be given, followed by data collection procedures and instrumentation for both the quantitative and qualitative portions of the study. The data analysis procedures for the quantitative and qualitative will be detailed. Ethical considerations and limitations/delimitations will conclude the chapter.

Research Questions and Hypotheses

This study was guided by four main research questions that called for the examination of community college coaches’ profile characteristics. Furthermore, hypotheses for research question 2 are stated in the null form. Because research question 1 refers to a descriptive analysis, and research questions 3 and 4 are qualitative and descriptive in nature, only research question 2 was included in the hypothesis testing. All hypotheses were tested at a minimum of 0.05 level of statistical significance.

Research question 1: What are Iowa CC coaches’ profile characteristics, such as their

backgrounds, roles, experiences, motivations to coach, challenges encountered, how they self- identify as teachers, their level of job satisfaction, and their perceived level of impact on their student-athletes’ educations?

Research question 2: Are there statistically significant mean differences in profile characteristics among various sub-samples of this IA CC coaching population?

a. Between female coaches and male coaches? b. Between assistant coaches and head coaches? c. Between full-time and part-time coaches?

d. Between coaches with extra jobs and coaches without extra jobs? e. Among coaches of different career levels?

f. Among coaches of different age levels?

g. In addition, are there differences in profile characteristics between the CC coaches and the non-CC coaches (NCAA D-III and NAIA) who completed the same survey?

Null hypothesis 1: There are no statistically significant mean differences in profile characteristics among the sub-samples of this IA CC coaching population.

Research question 3: Do coaches’ motivations to coach and types of challenges encountered predict whether the coaches’ report being satisfied in their jobs?

Null hypothesis 2: The coaches’ motivations to coach and their challenges do not predict whether the coaches are satisfied in their job(s).

Research question 4: How do CC coaches deal with the challenges associated with their multiple roles, maintaining their passion for and resilience in their coaching profession?

Research question 5: In what ways do CC coaches believe they influence their student-athletes’ educational experiences and outcomes?

Table 2 outlines the alignment of the research questions with the methods used to answer the questions. Because both quantitative and qualitative techniques are employed, table 2

presents a description of the sample, data collection methods and data analysis techniques relevant to answer each research question. In addition, it should be noted that elements of the Watt and Richardson's Fit Choice Model (FCM) and domains of Role Theory apply to each of the research questions.

Table 2

Alignment of Research Questions and Methods

Research question Theory Sample Data collection

Data analysis

1. What are Iowa CC coaches’ profile characteristics, such as backgrounds, roles, experiences, motivations, challenges, teacher identification, job satisfaction, and perceived impact on student- athletes’ education? FIT CM & Role theory Purposive sample of CC coaches in Iowa Qualtrics survey, Semi- structured interviews SPSS descriptive & cross-tabular analysis, Phenomenological iterative coding & theming

2. Are there statistically significant differences among sub-groups of this Iowa coaching population?

FIT CM & Role theory

CC coaches only: Female & male coaches, Assistant & head coaches, Full-time & part-time, With extra jobs & no extra jobs,

CC D-I & CC D-II, FB & men’s basketball & all other sports,

Career levels, Age levels, Squad sizes, Competition levels Qualtrics survey SPSS independent samples t-tests and One-way analyses of variance with posthoc analyses

3. Do the coaches reported motivations and challenges predict whether the coaches’ are satisfied in their jobs?

FIT CM & Role theory

Total sample of CC, NAIA, & NCAA D-III coaches who completed the survey Qualtrics survey SPSS logistic regression model predicting job satisfaction 4. How do CC coaches deal with

the potential challenges associated with their multiple roles and ultimately persist in their coaching profession?

FCM & Role theory

Seven CC coaches who indicated on survey an interest in speaking with investigator

Semi- structured interviews

Phenomenological iterative coding & theming

5. In what ways do these CC coaches believe they influence their student-athletes’ education?

FCM & Role theory

Seven CC coaches who indicated on survey an interest in speaking with investigator Qualtrics Survey, Semi- structured interviews SPSS descriptive analysis, Phenomenological iterative coding & theming

In addition to the major research questions, the investigator is interested in examining the efficacy of the survey instrument that was created for this study. To accomplish this, exploratory factor analysis (EFA) will be run. Since the CC coaching sample size (n = 60) is insufficient to effectively run this analysis, the total sample of small-school coaches in Iowa including coaches in the NAIA and NCAA D-III levels will be used. It is believed that the null hypotheses

concerning any potential mean differences for the EFA-created variables of interest among the three levels of coaches will be upheld.

Rationale for Mixed Methods Model

To answer the research questions, a convergent parallel mixed methods model of inquiry with triangulated quantitative and qualitative processes was utilized. Creswell (2014) defines convergent parallel mixed methods as an approach where a researcher collects both quantitative and qualitative data, analyzes them separately, and then compares the results to see if the

findings confirm each other or not. The key assumption of this approach is that both qualitative and quantitative data provide different types of information. However, together, they should yield similar findings (Creswell, 2014).

Greene, Kreider, and Mayer (2005) describe four major purposes for using a mixed methods approach to a study: (1) understanding more defensibly, with stronger validity or credibility and less known bias, as with the classic triangulation approach; (2) understanding more comprehensively, developing more complete and full pictures of our social world through the use of multiple perspectives; (3) understanding more insightfully, with new ideas,

perspectives, creative concepts and meanings, as when findings diverge and thus require reconciliation via further analysis, reframing or some other shift in perspective; and (4)

understanding with greater value consciousness and with greater diversity of values, stances and positions through the inclusion of different methods that themselves advance different values.

It could be argued that “mixed model” is a more appropriate term than “mixed method,” because “mixing” can be applied to methods beyond those used in the study (Tashakkori and Teddlie, 1998). According to Caracelli and Greene (1997), “mixing” can constitute parallel or sequential use of different methods (Bazeley, 2002). A mixed model approach is a process of discovering and then piecing together pieces of a puzzle to find answers to questions (Jick, 1979). Smith (1997) states,

From data in the form of numbers, one makes inferences in the same way as with data in the form of words, not by virtue of probabilistic algorithms. Statistics are not privileged. The inference is not mechanized. With this way of viewing knowledge, ‘mixed' methods may even be a misnomer, as both surveys and participant observation yield equivalent data. Inferences are based on the inquirer's coordinating multiple lines of evidence to gain an overall understanding of the phenomenon… Yet, because the inquirer is the instrument, all information flows through a single perspective. (p. 77).

To summarize, numbers should be used to help answer questions, but verbal comments should not be disregarded. Supporting this, Bazeley (2002) contends that,

Mixed methods are used to enrich understanding of an experience or issue through confirmation of conclusions, extension of knowledge, or by initiating new ways of thinking about the subject of the research… validity stems from the appropriateness, thoroughness, and effectiveness with which those methods are applied and the care given to thoughtful weighing of the evidence… (p. 420)

McKim (2017) emphasizes that one question a mixed methods researcher must ask oneself is, "Are mixed methods going to add more value than a single method?" Mixed methods research requires additional time because of the collection and analysis of two types of data (Creswell & Plano Clark, 2011). Studies that use a mixed methods approach can potentially gain deeper and broader levels of understanding about phenomenon versus studies that utilize only quantitative or qualitative approaches (McKim, 2017). DeCuir-Gunby and Schutz (2017) agree, and they summarize four advantages associated with conducting mixed methods research. Namely, mixed methods:

1. Allows for the examination of complex problems by providing to triangulate or corroborate findings with multiple sources of evidence.

2. Allows maximization of strengths of both quantifiable approaches (e.g., generalizability) and qualitative approaches (e.g., in-depth analysis of a small number of cases) while minimizing weaknesses of both quantitative approaches (e.g., lack of participant's voice) and qualitative approaches (e.g., lack of generalizability).

3. Allows for the use of a variety of quantitative and qualitative methods and multiple perspectives or paradigms.

4. Increases the possibility of finding results that the researcher can write about and publish, while a single approach may result in nonsignificant or unpublishable findings.

Research Design

The current study uses a mixed methods design to research certain profile characteristics describing CC coaches along with their perspectives that could be too complex to accurately and reliably analyze with only quantitative or qualitative techniques. This complete profile includes the coaches' motivations, challenges, roles, levels of teacher self-identity, and beliefs about how

their coaching job(s) impact their personal/family lives, and how they impact student-athletes' education. To set the stage, a pilot study was conducted to strengthen the investigative

instruments that were used. The development of the quantitative survey and qualitative interview protocol stemmed from this pilot study. This pilot study is detailed in the instrumentation section.

Quantitative Methodological Approach

First, along with descriptive statistics to establish key characteristics of the CC coaching sample, exploratory factor analysis (EFA) was employed to provide an initial understanding of the fit of the latent constructs and the correlations among variables for the complete sample of surveyed coaches (CC, NAIA, and NCAA D-III levels.). This provided a group of summated key variables of interest that would be used for further statistical analysis. Because larger sample sizes enhance the reliability of the resulting factors/constructs (Leech, Barrett, & Morgan, 2015), all the CC, NAIA, and NCAA D-III coaches' responses were included into the EFA. In EFA, one postulates that there is a smaller set of unobserved variables or constructs underlying the variables actually observed or measured, and this is commonly used to assess the validity of the survey instrument and its questions (Leech, et al., 2015).

After EFA, a series of statistical tests to examine mean differences of these variables of interest among sub-groups of the CC coaches were conducted. These tests include independent samples t tests and one-way analyses of variance (ANOVA). If statistically significant mean differences (p < .05), appropriate post hoc analyses were run to discover where those differences specifically took place.

Informed by results from the examination of mean differences, a logistic regression analysis examined if the potential predictive relationships of certain variables of interest would

align with the literature and theoretical concepts used to inform this study. According to Leech, Barrett, and Morgan (2015), logistic regression is used to predict an outcome or dependent variable from a set of predictor variables, and it does not rely on some of the assumptions on which multiple regression are based. Leech and colleagues (2015) also emphasize that logistic regression requires large samples to be accurate. In the present study, 60 CC coaches completed the survey. Because this is on the edge of acceptability, and because the regression model is strengthened with larger samples, all coaches (CC, NAIA, and NCAA D-III) were included in the model. As this is a study about CC coaches, the first block of the logistic regression model includes a dummy-coded variable of whether the coach is a CC coach or not a CC coach. Qualitative Methodological Approach

In addition to the quantitative techniques employed, a phenomenological approach to qualitative techniques via seven semi-structured interviews was used to gain deeper levels of understanding on the topics and phenomena of interest. It is important to note that this phenomenological approach was restricted to the qualitative portion of this mixed methods study. Creswell (2014) states that a phenomenological approach describes the lived experiences of a particular sample relating to an interesting phenomenon that becomes highlighted by the study participants. This description culminates in the “essence of the experiences for several individuals who have all experienced the phenomenon” (p. 14).

Guided by the theoretical framework (FIT Choice Model and Role Theory) and by the investigator’s prior experiences and knowledge with athletics coaching, along with abundant anecdotal information prevalent in popular media, the major phenomena explored centers around describing the coaches’ profile characteristics (experiences, backgrounds, motivations,

satisfaction) and how these characteristics potentially influence one another.. Based on this, an appropriate interview protocol (interview guide is in Appendix C) was constructed to explore the phenomena of interest.

According to Merriam and Tisdell (2016), semi-structured interview protocols are well- suited for exploration of participants’ perceptions and opinions regarding complex and

sometimes sensitive issues and enable probing for more information and clarification of responses. For the present study, the complex and sensitive issues related to the coaches discussing their views on the potential interplay among the coaches' profile characteristics. Furthermore, this type of protocol proves useful when interviewing the sample group who might come from professional, educational, and personal backgrounds that are either varied or similar (Merriam & Tisdall, 2016). In this study, it was anticipated that the interview participants will have similar backgrounds and experiences that have led them to their current positions and philosophies. However, the semi-structured interview protocol allowed for potential variability in the coaches' responses that might take the protocol in varying directions.

Population and Sampling

The following sections describe the current study’s context (or setting) along with the sampling process used to gain access to the study’s participants. In addition, information on the human subjects approval by the Iowa State University Institutional Review Board along with the informed consent is given.

Study Setting

To examine CC athletics coaches' profile characteristics, their levels of job satisfaction and their perceived impacts on their student-athletes' education, data was collected from an online survey that was emailed to all potential participants during fall 2016 and winter 2017. An

online search was conducted to build a list of potential participants (head coaches and assistant coaches) from CC, NAIA, and NCAA D-III athletics programs from the state of Iowa. Because the NAIA, NCAA, and NJCAA do not maintain updated lists of coaches in their divisions, this search process was labor-intensive manual one. Many of the NAIA and NCAA D-III institutions are private, liberal arts focused, and religiously based. In contrast, many of the CC institutions are primarily public. These particular settings within which the coaches work potentially play a contextual role in the study's findings.

The Iowa Community College Athletics Conference (ICCAC) consists of 15 institutions (13 from Iowa and two from Nebraska). The ICCAC sanctions 12 men’s sports and 11 women’s sports across all divisions (I, II, and III), but the majority of the athletic programs are in D-I and D-II community college athletics.

There are nine NCAA D-III Institutions in the Iowa Intercollegiate Athletic Conference, and they compete in 13 men’s sports and 11 women’s sports. Two Iowa institutions that

compete in the NCAA D-III Midwest Conference were also included. The Midwest Conference supports 10 men’s sports and 10 women’s sports.

In Iowa, there are 12 institutions that compete in the NAIA. While these schools compete with each other, they are actually members of four separate conferences (Great Plains Athletic Conference, Heart of America Conference, North Star Conference and the Chicagoland Conference). All four of these conferences include schools spanning multiple states. Two of these schools with coaches in this study compete independently of a conference.

Sampling Plan

Community college intercollegiate athletics coaches in Iowa comprise this study’s main population of interest. Targeted were 221 community college coaches in the Iowa Community

College Athletic Conference. Although CC coaches in Iowa comprise the main population of interest, 365 NCAA D-III coaches in Iowa, and 297 NAIA coaches in Iowa were included as populations of secondary interest that contribute to enhancing certain statistical analyses for this study. The coaches' levels of experience range from entry level/early career to those who are over typical retirement age. The literature points out that there are significantly more males coaching than females within intercollegiate athletics (Kamphoff, 2010), and that females tend to complete surveys at higher rates than males (Dillman et al., 2014). These factors could have had a negative impact on total survey response rate, which would have resulted in weakening

statistical analyses. As a result, a random sample of the Iowa small-school coaching population was not sought. Instead, all CC, NAIA, and NCAA D-III coaches with available emails were contacted and considered potential study participants.

A targeted purposive sample was used, as this study had a clearly defined focus. The main population of interest and the sample for this current study are CC coaches in Iowa. Secondary populations of interest are NAIA and NCAA D-III coaches in Iowa. According to Johnson and Christensen (2008), purposive sampling allows the investigator to select specific criteria for study participants. The participants in this study fit two basic criteria: (a) were at least 18 years of age at the time of the study and (b) have experience coaching any intercollegiate sport at the community college, NAIA, and NCAA D-III levels of competition in the state of Iowa.

For quantitative analysis, sample size needs to be large enough for statistical procedures to be utilized that will make it feasible for the investigator to draw inferences with some

Plano Clark, 2007). For qualitative research, two to 10 participants are ideal, as it allows for multiple perspectives of an explored phenomenon (Maxwell, 2012).

An exhaustive online search was executed to compile a complete list of coaches’ publicly available email addresses. Panels were created to denote the coaches’ names, email addresses, sport(s) coached, and position(s) held (assistant coach or head coach). The researcher believed that response rates would be higher with this targeted sample of participants, especially in consideration of the researcher’s positionality as a coach with deep roots in Iowa.